what is the conclusion of the research hypothesis

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Educational resources and simple solutions for your research journey

What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Step-by-Step Guide: How to Craft a Strong Research Hypothesis

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Table of Contents

A research hypothesis is a concise statement about the expected result of an experiment or project. In many ways, a research hypothesis represents the starting point for a scientific endeavor, as it establishes a tentative assumption that is eventually substantiated or falsified, ultimately improving our certainty about the subject investigated.   

To help you with this and ease the process, in this article, we discuss the purpose of research hypotheses and list the most essential qualities of a compelling hypothesis. Let’s find out!  

How to Craft a Research Hypothesis  

Crafting a research hypothesis begins with a comprehensive literature review to identify a knowledge gap in your field. Once you find a question or problem, come up with a possible answer or explanation, which becomes your hypothesis. Now think about the specific methods of experimentation that can prove or disprove the hypothesis, which ultimately lead to the results of the study.   

Enlisted below are some standard formats in which you can formulate a hypothesis¹ :  

• A hypothesis can use the if/then format when it seeks to explore the correlation between two variables in a study primarily.  

Example: If administered drug X, then patients will experience reduced fatigue from cancer treatment.  

• A hypothesis can adopt when X/then Y format when it primarily aims to expose a connection between two variables  

Example: When workers spend a significant portion of their waking hours in sedentary work , then they experience a greater frequency of digestive problems.  

• A hypothesis can also take the form of a direct statement.  

Example: Drug X and drug Y reduce the risk of cognitive decline through the same chemical pathways  

What are the Features of an Effective Hypothesis?  

Hypotheses in research need to satisfy specific criteria to be considered scientifically rigorous. Here are the most notable qualities of a strong hypothesis:  

• Testability: Ensure the hypothesis allows you to work towards observable and testable results.  
• Brevity and objectivity: Present your hypothesis as a brief statement and avoid wordiness.  
• Clarity and Relevance: The hypothesis should reflect a clear idea of what we know and what we expect to find out about a phenomenon and address the significant knowledge gap relevant to a field of study.   

Understanding Null and Alternative Hypotheses in Research  

There are two types of hypotheses used commonly in research that aid statistical analyses. These are known as the null hypothesis and the alternative hypothesis . A null hypothesis is a statement assumed to be factual in the initial phase of the study.   

For example, if a researcher is testing the efficacy of a new drug, then the null hypothesis will posit that the drug has no benefits compared to an inactive control or placebo . Suppose the data collected through a drug trial leads a researcher to reject the null hypothesis. In that case, it is considered to substantiate the alternative hypothesis in the above example, that the new drug provides benefits compared to the placebo.  

Let’s take a closer look at the null hypothesis and alternative hypothesis with two more examples:  

Null Hypothesis:  

The rate of decline in the number of species in habitat X in the last year is the same as in the last 100 years when controlled for all factors except the recent wildfires.  

In the next experiment, the researcher will experimentally reject this null hypothesis in order to confirm the following alternative hypothesis :  

The rate of decline in the number of species in habitat X in the last year is different from the rate of decline in the last 100 years when controlled for all factors other than the recent wildfires.  

In the pair of null and alternative hypotheses stated above, a statistical comparison of the rate of species decline over a century and the preceding year will help the research experimentally test the null hypothesis, helping to draw scientifically valid conclusions about two factors—wildfires and species decline.   

We also recommend that researchers pay attention to contextual echoes and connections when writing research hypotheses. Research hypotheses are often closely linked to the introduction ² , such as the context of the study, and can similarly influence the reader’s judgment of the relevance and validity of the research hypothesis.  

Seasoned experts, such as professionals at Elsevier Language Services, guide authors on how to best embed a hypothesis within an article so that it communicates relevance and credibility. Contact us if you want help in ensuring readers find your hypothesis robust and unbiased.  

References  

• Hypotheses – The University Writing Center. (n.d.). https://writingcenter.tamu.edu/writing-speaking-guides/hypotheses  
• Shaping the research question and hypothesis. (n.d.). Students. https://students.unimelb.edu.au/academic-skills/graduate-research-services/writing-thesis-sections-part-2/shaping-the-research-question-and-hypothesis  

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The Scientific Method by Science Made Simple

Understanding and using the scientific method.

The Scientific Method is a process used to design and perform experiments. It's important to minimize experimental errors and bias, and increase confidence in the accuracy of your results.

In the previous sections, we talked about how to pick a good topic and specific question to investigate. Now we will discuss how to carry out your investigation.

Steps of the Scientific Method

• Observation/Research
• Experimentation

Now that you have settled on the question you want to ask, it's time to use the Scientific Method to design an experiment to answer that question.

If your experiment isn't designed well, you may not get the correct answer. You may not even get any definitive answer at all!

The Scientific Method is a logical and rational order of steps by which scientists come to conclusions about the world around them. The Scientific Method helps to organize thoughts and procedures so that scientists can be confident in the answers they find.

OBSERVATION is first step, so that you know how you want to go about your research.

HYPOTHESIS is the answer you think you'll find.

PREDICTION is your specific belief about the scientific idea: If my hypothesis is true, then I predict we will discover this.

EXPERIMENT is the tool that you invent to answer the question, and

CONCLUSION is the answer that the experiment gives.

Don't worry, it isn't that complicated. Let's take a closer look at each one of these steps. Then you can understand the tools scientists use for their science experiments, and use them for your own.

OBSERVATION

This step could also be called "research." It is the first stage in understanding the problem.

After you decide on topic, and narrow it down to a specific question, you will need to research everything that you can find about it. You can collect information from your own experiences, books, the internet, or even smaller "unofficial" experiments.

Let's continue the example of a science fair idea about tomatoes in the garden. You like to garden, and notice that some tomatoes are bigger than others and wonder why.

Because of this personal experience and an interest in the problem, you decide to learn more about what makes plants grow.

For this stage of the Scientific Method, it's important to use as many sources as you can find. The more information you have on your science fair topic, the better the design of your experiment is going to be, and the better your science fair project is going to be overall.

Also try to get information from your teachers or librarians, or professionals who know something about your science fair project. They can help to guide you to a solid experimental setup.

The next stage of the Scientific Method is known as the "hypothesis." This word basically means "a possible solution to a problem, based on knowledge and research."

The hypothesis is a simple statement that defines what you think the outcome of your experiment will be.

All of the first stage of the Scientific Method -- the observation, or research stage -- is designed to help you express a problem in a single question ("Does the amount of sunlight in a garden affect tomato size?") and propose an answer to the question based on what you know. The experiment that you will design is done to test the hypothesis.

Using the example of the tomato experiment, here is an example of a hypothesis:

TOPIC: "Does the amount of sunlight a tomato plant receives affect the size of the tomatoes?"

HYPOTHESIS: "I believe that the more sunlight a tomato plant receives, the larger the tomatoes will grow.

This hypothesis is based on:

(1) Tomato plants need sunshine to make food through photosynthesis, and logically, more sun means more food, and;

(2) Through informal, exploratory observations of plants in a garden, those with more sunlight appear to grow bigger.

The hypothesis is your general statement of how you think the scientific phenomenon in question works.

Your prediction lets you get specific -- how will you demonstrate that your hypothesis is true? The experiment that you will design is done to test the prediction.

An important thing to remember during this stage of the scientific method is that once you develop a hypothesis and a prediction, you shouldn't change it, even if the results of your experiment show that you were wrong.

An incorrect prediction does NOT mean that you "failed." It just means that the experiment brought some new facts to light that maybe you hadn't thought about before.

Continuing our tomato plant example, a good prediction would be: Increasing the amount of sunlight tomato plants in my experiment receive will cause an increase in their size compared to identical plants that received the same care but less light.

This is the part of the scientific method that tests your hypothesis. An experiment is a tool that you design to find out if your ideas about your topic are right or wrong.

It is absolutely necessary to design a science fair experiment that will accurately test your hypothesis. The experiment is the most important part of the scientific method. It's the logical process that lets scientists learn about the world.

On the next page, we'll discuss the ways that you can go about designing a science fair experiment idea.

The final step in the scientific method is the conclusion. This is a summary of the experiment's results, and how those results match up to your hypothesis.

You have two options for your conclusions: based on your results, either:

(1) YOU CAN REJECT the hypothesis, or

(2) YOU CAN NOT REJECT the hypothesis.

This is an important point!

You can not PROVE the hypothesis with a single experiment, because there is a chance that you made an error somewhere along the way.

What you can say is that your results SUPPORT the original hypothesis.

If your original hypothesis didn't match up with the final results of your experiment, don't change the hypothesis.

Instead, try to explain what might have been wrong with your original hypothesis. What information were you missing when you made your prediction? What are the possible reasons the hypothesis and experimental results didn't match up?

Remember, a science fair experiment isn't a failure simply because does not agree with your hypothesis. No one will take points off if your prediction wasn't accurate. Many important scientific discoveries were made as a result of experiments gone wrong!

A science fair experiment is only a failure if its design is flawed. A flawed experiment is one that (1) doesn't keep its variables under control, and (2) doesn't sufficiently answer the question that you asked of it.

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How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

This is awesome.Wow.

It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

Explained well and in simple terms. Quick read! Thank you

It awesome. It has really positioned me in my research project

Brief and easily digested

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How to State the Conclusion about a Hypothesis Test

After you have completed the statistical analysis and decided to reject or fail to reject the Null hypothesis, you need to state your conclusion about the claim. To get the correct wording, you need to recall which hypothesis was the claim.

If the claim was the null, then your conclusion is about whether there was sufficient evidence to reject the claim. Remember, we can never prove the null to be true, but failing to reject it is the next best thing. So, it is not correct to say, “Accept the Null.”

If the claim is the alternative hypothesis, your conclusion can be whether there was sufficient evidence to support (prove) the alternative is true.

Use the following table to help you make a good conclusion.

The best way to state the conclusion is to include the significance level of the test and a bit about the claim itself.

For example, if the claim was the alternative that the mean score on a test was greater than 85, and your decision was to  Reject then Null , then you could conclude: “ At the 5% significance level, there is sufficient evidence to support the claim that the mean score on the test was greater than 85. ”

The reason you should include the significance level is that the decision, and thus the conclusion, could be different if the significance level was not 5%.

If you are curious why we say “Fail to Reject the Null” instead of “Accept the Null,” this short video might be of interest:  Here

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How to write a strong conclusion for your research paper

Last updated

17 February 2024

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Short on time? Get an AI generated summary of this article instead

Writing a research paper is a chance to share your knowledge and hypothesis. It's an opportunity to demonstrate your many hours of research and prove your ability to write convincingly.

Ideally, by the end of your research paper, you'll have brought your readers on a journey to reach the conclusions you've pre-determined. However, if you don't stick the landing with a good conclusion, you'll risk losing your reader’s trust.

Writing a strong conclusion for your research paper involves a few important steps, including restating the thesis and summing up everything properly.

Find out what to include and what to avoid, so you can effectively demonstrate your understanding of the topic and prove your expertise.

• Why is a good conclusion important?

A good conclusion can cement your paper in the reader’s mind. Making a strong impression in your introduction can draw your readers in, but it's the conclusion that will inspire them.

• What to include in a research paper conclusion

There are a few specifics you should include in your research paper conclusion. Offer your readers some sense of urgency or consequence by pointing out why they should care about the topic you have covered. Discuss any common problems associated with your topic and provide suggestions as to how these problems can be solved or addressed.

The conclusion should include a restatement of your initial thesis. Thesis statements are strengthened after you’ve presented supporting evidence (as you will have done in the paper), so make a point to reintroduce it at the end.

Finally, recap the main points of your research paper, highlighting the key takeaways you want readers to remember. If you've made multiple points throughout the paper, refer to the ones with the strongest supporting evidence.

• Steps for writing a research paper conclusion

Many writers find the conclusion the most challenging part of any research project . By following these three steps, you'll be prepared to write a conclusion that is effective and concise.

• Step 1: Restate the problem

Always begin by restating the research problem in the conclusion of a research paper. This serves to remind the reader of your hypothesis and refresh them on the main point of the paper. 

When restating the problem, take care to avoid using exactly the same words you employed earlier in the paper.

• Step 2: Sum up the paper

After you've restated the problem, sum up the paper by revealing your overall findings. The method for this differs slightly, depending on whether you're crafting an argumentative paper or an empirical paper.

Argumentative paper: Restate your thesis and arguments

Argumentative papers involve introducing a thesis statement early on. In crafting the conclusion for an argumentative paper, always restate the thesis, outlining the way you've developed it throughout the entire paper.

It might be appropriate to mention any counterarguments in the conclusion, so you can demonstrate how your thesis is correct or how the data best supports your main points.

Empirical paper: Summarize research findings

Empirical papers break down a series of research questions. In your conclusion, discuss the findings your research revealed, including any information that surprised you.

Be clear about the conclusions you reached, and explain whether or not you expected to arrive at these particular ones.

• Step 3: Discuss the implications of your research

Argumentative papers and empirical papers also differ in this part of a research paper conclusion. Here are some tips on crafting conclusions for argumentative and empirical papers.

Argumentative paper: Powerful closing statement

In an argumentative paper, you'll have spent a great deal of time expressing the opinions you formed after doing a significant amount of research. Make a strong closing statement in your argumentative paper's conclusion to share the significance of your work.

You can outline the next steps through a bold call to action, or restate how powerful your ideas turned out to be.

Empirical paper: Directions for future research

Empirical papers are broader in scope. They usually cover a variety of aspects and can include several points of view.

To write a good conclusion for an empirical paper, suggest the type of research that could be done in the future, including methods for further investigation or outlining ways other researchers might proceed.

If you feel your research had any limitations, even if they were outside your control, you could mention these in your conclusion.

After you finish outlining your conclusion, ask someone to read it and offer feedback. In any research project you're especially close to, it can be hard to identify problem areas. Having a close friend or someone whose opinion you value read the research paper and provide honest feedback can be invaluable. Take note of any suggested edits and consider incorporating them into your paper if they make sense.

• Things to avoid in a research paper conclusion

Keep these aspects to avoid in mind as you're writing your conclusion and refer to them after you've created an outline.

Dry summary

Writing a memorable, succinct conclusion is arguably more important than a strong introduction. Take care to avoid just rephrasing your main points, and don't fall into the trap of repeating dry facts or citations.

You can provide a new perspective for your readers to think about or contextualize your research. Either way, make the conclusion vibrant and interesting, rather than a rote recitation of your research paper’s highlights.

Clichéd or generic phrasing

Your research paper conclusion should feel fresh and inspiring. Avoid generic phrases like "to sum up" or "in conclusion." These phrases tend to be overused, especially in an academic context and might turn your readers off.

The conclusion also isn't the time to introduce colloquial phrases or informal language. Retain a professional, confident tone consistent throughout your paper’s conclusion so it feels exciting and bold.

New data or evidence

While you should present strong data throughout your paper, the conclusion isn't the place to introduce new evidence. This is because readers are engaged in actively learning as they read through the body of your paper.

By the time they reach the conclusion, they will have formed an opinion one way or the other (hopefully in your favor!). Introducing new evidence in the conclusion will only serve to surprise or frustrate your reader.

Ignoring contradictory evidence

If your research reveals contradictory evidence, don't ignore it in the conclusion. This will damage your credibility as an expert and might even serve to highlight the contradictions.

Be as transparent as possible and admit to any shortcomings in your research, but don't dwell on them for too long.

Ambiguous or unclear resolutions

The point of a research paper conclusion is to provide closure and bring all your ideas together. You should wrap up any arguments you introduced in the paper and tie up any loose ends, while demonstrating why your research and data are strong.

Use direct language in your conclusion and avoid ambiguity. Even if some of the data and sources you cite are inconclusive or contradictory, note this in your conclusion to come across as confident and trustworthy.

• Examples of research paper conclusions

Your research paper should provide a compelling close to the paper as a whole, highlighting your research and hard work. While the conclusion should represent your unique style, these examples offer a starting point:

Ultimately, the data we examined all point to the same conclusion: Encouraging a good work-life balance improves employee productivity and benefits the company overall. The research suggests that when employees feel their personal lives are valued and respected by their employers, they are more likely to be productive when at work. In addition, company turnover tends to be reduced when employees have a balance between their personal and professional lives. While additional research is required to establish ways companies can support employees in creating a stronger work-life balance, it's clear the need is there.

Social media is a primary method of communication among young people. As we've seen in the data presented, most young people in high school use a variety of social media applications at least every hour, including Instagram and Facebook. While social media is an avenue for connection with peers, research increasingly suggests that social media use correlates with body image issues. Young girls with lower self-esteem tend to use social media more often than those who don't log onto social media apps every day. As new applications continue to gain popularity, and as more high school students are given smartphones, more research will be required to measure the effects of prolonged social media use.

What are the different kinds of research paper conclusions?

There are no formal types of research paper conclusions. Ultimately, the conclusion depends on the outline of your paper and the type of research you’re presenting. While some experts note that research papers can end with a new perspective or commentary, most papers should conclude with a combination of both. The most important aspect of a good research paper conclusion is that it accurately represents the body of the paper.

Can I present new arguments in my research paper conclusion?

Research paper conclusions are not the place to introduce new data or arguments. The body of your paper is where you should share research and insights, where the reader is actively absorbing the content. By the time a reader reaches the conclusion of the research paper, they should have formed their opinion. Introducing new arguments in the conclusion can take a reader by surprise, and not in a positive way. It might also serve to frustrate readers.

How long should a research paper conclusion be?

There's no set length for a research paper conclusion. However, it's a good idea not to run on too long, since conclusions are supposed to be succinct. A good rule of thumb is to keep your conclusion around 5 to 10 percent of the paper's total length. If your paper is 10 pages, try to keep your conclusion under one page.

What should I include in a research paper conclusion?

A good research paper conclusion should always include a sense of urgency, so the reader can see how and why the topic should matter to them. You can also note some recommended actions to help fix the problem and some obstacles they might encounter. A conclusion should also remind the reader of the thesis statement, along with the main points you covered in the paper. At the end of the conclusion, add a powerful closing statement that helps cement the paper in the mind of the reader.

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Organizing Your Social Sciences Research Paper

• 9. The Conclusion
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
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• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
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• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
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• Further Readings
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• Bibliography

The conclusion is intended to help the reader understand why your research should matter to them after they have finished reading the paper. A conclusion is not merely a summary of the main topics covered or a re-statement of your research problem, but a synthesis of key points derived from the findings of your study and, if applicable based on your analysis, explain new areas for future research. For most college-level research papers, two or three well-developed paragraphs is sufficient for a conclusion, although in some cases, more paragraphs may be required in describing the key findings and highlighting their significance.

Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University.

Importance of a Good Conclusion

A well-written conclusion provides important opportunities to demonstrate to the reader your understanding of the research problem. These include:

• Presenting the last word on the issues you raised in your paper . Just as the introduction gives a first impression to your reader, the conclusion offers a chance to leave a lasting impression. Do this, for example, by highlighting key findings in your analysis that advance new understanding about the research problem, that are unusual or unexpected, or that have important implications applied to practice.
• Summarizing your thoughts and conveying the larger significance of your study . The conclusion is an opportunity to succinctly re-emphasize  your answer to the "So What?" question by placing the study within the context of how your research advances past studies about the topic.
• Identifying how a gap in the literature has been addressed . The conclusion can be where you describe how a previously identified gap in the literature [first identified in your literature review section] has been addressed by your research and why this contribution is significant.
• Demonstrating the importance of your ideas . Don't be shy. The conclusion offers an opportunity to elaborate on the impact and significance of your findings. This is particularly important if your study approached examining the research problem from an unusual or innovative perspective.
• Introducing possible new or expanded ways of thinking about the research problem . This does not refer to introducing new information [which should be avoided], but to offer new insight and creative approaches for framing or contextualizing the research problem based on the results of your study.

Bunton, David. “The Structure of PhD Conclusion Chapters.” Journal of English for Academic Purposes 4 (July 2005): 207–224; Conclusions. The Writing Center. University of North Carolina; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Conclusions. The Writing Lab and The OWL. Purdue University; Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Structure and Writing Style

I.  General Rules

The general function of your paper's conclusion is to restate the main argument . It reminds the reader of your main argument(s) strengths and reiterates the most important evidence supporting those argument(s). Do this by clearly summarizing the context, background, and the necessity of examining the research problem in relation to an issue, controversy, or a gap found in the literature. However, make sure that your conclusion is not simply a repetitive summary of the findings. This reduces the impact of the argument(s) you have developed in your paper.

When writing the conclusion to your paper, follow these general rules:

• Present your conclusions in clear, concise language. Re-state the purpose of your study, then describe how your findings differ or support those of other studies and why [i.e., describe what were the unique, new, or crucial contributions your study made to the overall research about your topic].
• Do not simply reiterate your findings or the discussion of your results. Provide a synthesis of arguments presented in the paper to show how these converge to address the research problem and the overall objectives of your study.
• Indicate opportunities for future research if you haven't already done so in the discussion section of your paper. Highlighting the need for further research provides the reader with evidence that you have an in-depth awareness of the research problem but that further analysis should take place beyond the scope of your investigation.

Consider the following points to help ensure your conclusion is presented well:

• If the argument or purpose of your paper is complex, you may need to summarize the argument for your reader.
• If, prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the end of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration that returns the topic to the context provided by the introduction or within a new context that emerges from the data [this is opposite of the introduction, which begins with general discussion of the context and ends with a detailed description of the research problem]. 

The conclusion also provides a place for you to persuasively and succinctly restate the research problem, given that the reader has now been presented with all the information about the topic . Depending on the discipline you are writing in, the concluding paragraph may contain your reflections on the evidence presented. However, the nature of being introspective about the research you have conducted will depend on the topic and whether your professor wants you to express your observations in this way. If asked to think introspectively about the topic, do not delve into idle speculation. Being introspective means looking within yourself as an author to try and understand an issue more deeply, not to guess at possible outcomes or make up scenarios not supported by the evidence.

II.  Developing a Compelling Conclusion

Although an effective conclusion needs to be clear and succinct, it does not need to be written passively or lack a compelling narrative. Strategies to help you move beyond merely summarizing the key points of your research paper may include any of the following:

• If your paper addresses a critical, contemporary problem, warn readers of the possible consequences of not attending to the problem proactively based on the evidence presented in your study.
• Recommend a specific course or courses of action that, if adopted, could address a specific problem in practice or in the development of new knowledge leading to positive change.
• Cite a relevant quotation or expert opinion already noted in your paper in order to lend authority and support to the conclusion(s) you have reached [a good source would be from a source cited in your literature review].
• Explain the consequences of your research in a way that elicits action or demonstrates urgency in seeking change.
• Restate a key statistic, fact, or visual image to emphasize the most important finding of your paper.
• If your discipline encourages personal reflection, illustrate your concluding point by drawing from your own life experiences.
• Return to an anecdote, an example, or a quotation that you presented in your introduction, but add further insight derived from the findings of your study; use your interpretation of results from your study to recast it in new or important ways.
• Provide a "take-home" message in the form of a succinct, declarative statement that you want the reader to remember about your study.

III. Problems to Avoid

Failure to be concise Your conclusion section should be concise and to the point. Conclusions that are too lengthy often have unnecessary information in them. The conclusion is not the place for details about your methodology or results. Although you should give a summary of what was learned from your research, this summary should be relatively brief, since the emphasis in the conclusion is on the implications, evaluations, insights, and other forms of analysis that you make. Strategies for writing concisely can be found here .

Failure to comment on larger, more significant issues In the introduction, your task was to move from the general [topic studied within the field of study] to the specific [the research problem]. However, in the conclusion, your task is to move the discussion from specific [your research problem] back to a general discussion framed around the implications and significance of your findings [i.e., how your research contributes new understanding or fills an important gap in the literature]. In short, the conclusion is where you should place your research within a larger context [visualize the structure of your paper as an hourglass--start with a broad introduction and review of the literature, move to the specific method of analysis and the discussion, conclude with a broad summary of the study's implications and significance].

Failure to reveal problems and negative results Negative aspects of the research process should never be ignored. These are problems, deficiencies, or challenges encountered during your study. They should be summarized as a way of qualifying your overall conclusions. If you encountered negative or unintended results [i.e., findings that are validated outside the research context in which they were generated], you must report them in the results section and discuss their implications in the discussion section of your paper. In the conclusion, use negative or surprising results as an opportunity to explain their possible significance and/or how they may form the basis for future research.

Failure to provide a clear summary of what was learned In order to discuss how your research fits within your field of study [and possibly the world at large], you need to summarize briefly and succinctly how it contributes to new knowledge or a new understanding about the research problem. This element of your conclusion may be only a few sentences long, but it often represents the key takeaway for your reader.

Failure to match the objectives of your research Often research objectives in the social and behavioral sciences change while the research is being carried out due to unforeseen factors or unanticipated variables. This is not a problem unless you forget to go back and refine the original objectives in your introduction. As these changes emerge they must be documented so that they accurately reflect what you were trying to accomplish in your research [not what you thought you might accomplish when you began].

Resist the urge to apologize If you've immersed yourself in studying the research problem, you presumably should know a good deal about it [perhaps even more than your professor!]. Nevertheless, by the time you have finished writing, you may be having some doubts about what you have produced. Repress those doubts! Don't undermine your authority as a researcher by saying something like, "This is just one approach to examining this problem; there may be other, much better approaches that...." The overall tone of your conclusion should convey confidence to the reader concerning the validity and realiability of your research.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Concluding Paragraphs. College Writing Center at Meramec. St. Louis Community College; Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University; Freedman, Leora  and Jerry Plotnick. Introductions and Conclusions. The Lab Report. University College Writing Centre. University of Toronto; Leibensperger, Summer. Draft Your Conclusion. Academic Center, the University of Houston-Victoria, 2003; Make Your Last Words Count. The Writer’s Handbook. Writing Center. University of Wisconsin Madison; Miquel, Fuster-Marquez and Carmen Gregori-Signes. “Chapter Six: ‘Last but Not Least:’ Writing the Conclusion of Your Paper.” In Writing an Applied Linguistics Thesis or Dissertation: A Guide to Presenting Empirical Research . John Bitchener, editor. (Basingstoke,UK: Palgrave Macmillan, 2010), pp. 93-105; Tips for Writing a Good Conclusion. Writing@CSU. Colorado State University; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Writing Conclusions. Writing Tutorial Services, Center for Innovative Teaching and Learning. Indiana University; Writing: Considering Structure and Organization. Institute for Writing Rhetoric. Dartmouth College.

Writing Tip

Don't Belabor the Obvious!

Avoid phrases like "in conclusion...," "in summary...," or "in closing...." These phrases can be useful, even welcome, in oral presentations. But readers can see by the tell-tale section heading and number of pages remaining that they are reaching the end of your paper. You'll irritate your readers if you belabor the obvious.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Another Writing Tip

New Insight, Not New Information!

Don't surprise the reader with new information in your conclusion that was never referenced anywhere else in the paper. This is why the conclusion rarely has citations to sources that haven't been referenced elsewhere in your paper. If you have new information to present, add it to the discussion or other appropriate section of the paper. Note that, although no new information is introduced, the conclusion, along with the discussion section, is where you offer your most "original" contributions in the paper; the conclusion is where you describe the value of your research, demonstrate that you understand the material that you have presented, and position your findings within the larger context of scholarship on the topic, including describing how your research contributes new insights to that scholarship.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Conclusions. The Writing Center. University of North Carolina.

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How to Write a Conclusion for Research Papers (with Examples)

The conclusion of a research paper is a crucial section that plays a significant role in the overall impact and effectiveness of your research paper. However, this is also the section that typically receives less attention compared to the introduction and the body of the paper. The conclusion serves to provide a concise summary of the key findings, their significance, their implications, and a sense of closure to the study. Discussing how can the findings be applied in real-world scenarios or inform policy, practice, or decision-making is especially valuable to practitioners and policymakers. The research paper conclusion also provides researchers with clear insights and valuable information for their own work, which they can then build on and contribute to the advancement of knowledge in the field.

The research paper conclusion should explain the significance of your findings within the broader context of your field. It restates how your results contribute to the existing body of knowledge and whether they confirm or challenge existing theories or hypotheses. Also, by identifying unanswered questions or areas requiring further investigation, your awareness of the broader research landscape can be demonstrated.

Remember to tailor the research paper conclusion to the specific needs and interests of your intended audience, which may include researchers, practitioners, policymakers, or a combination of these.

Table of Contents

What is a conclusion in a research paper, summarizing conclusion, editorial conclusion, externalizing conclusion, importance of a good research paper conclusion, how to write a conclusion for your research paper, research paper conclusion examples.

• How to write a research paper conclusion with Paperpal? 

Frequently Asked Questions

A conclusion in a research paper is the final section where you summarize and wrap up your research, presenting the key findings and insights derived from your study. The research paper conclusion is not the place to introduce new information or data that was not discussed in the main body of the paper. When working on how to conclude a research paper, remember to stick to summarizing and interpreting existing content. The research paper conclusion serves the following purposes: 1

• Warn readers of the possible consequences of not attending to the problem.
• Recommend specific course(s) of action.
• Restate key ideas to drive home the ultimate point of your research paper.
• Provide a “take-home” message that you want the readers to remember about your study.

Types of conclusions for research papers

In research papers, the conclusion provides closure to the reader. The type of research paper conclusion you choose depends on the nature of your study, your goals, and your target audience. I provide you with three common types of conclusions:

A summarizing conclusion is the most common type of conclusion in research papers. It involves summarizing the main points, reiterating the research question, and restating the significance of the findings. This common type of research paper conclusion is used across different disciplines.

An editorial conclusion is less common but can be used in research papers that are focused on proposing or advocating for a particular viewpoint or policy. It involves presenting a strong editorial or opinion based on the research findings and offering recommendations or calls to action.

An externalizing conclusion is a type of conclusion that extends the research beyond the scope of the paper by suggesting potential future research directions or discussing the broader implications of the findings. This type of conclusion is often used in more theoretical or exploratory research papers.

Align your conclusion’s tone with the rest of your research paper. Start Writing with Paperpal Now!  

The conclusion in a research paper serves several important purposes:

• Offers Implications and Recommendations : Your research paper conclusion is an excellent place to discuss the broader implications of your research and suggest potential areas for further study. It’s also an opportunity to offer practical recommendations based on your findings.
• Provides Closure : A good research paper conclusion provides a sense of closure to your paper. It should leave the reader with a feeling that they have reached the end of a well-structured and thought-provoking research project.
• Leaves a Lasting Impression : Writing a well-crafted research paper conclusion leaves a lasting impression on your readers. It’s your final opportunity to leave them with a new idea, a call to action, or a memorable quote.

Writing a strong conclusion for your research paper is essential to leave a lasting impression on your readers. Here’s a step-by-step process to help you create and know what to put in the conclusion of a research paper: 2

• Research Statement : Begin your research paper conclusion by restating your research statement. This reminds the reader of the main point you’ve been trying to prove throughout your paper. Keep it concise and clear.
• Key Points : Summarize the main arguments and key points you’ve made in your paper. Avoid introducing new information in the research paper conclusion. Instead, provide a concise overview of what you’ve discussed in the body of your paper.
• Address the Research Questions : If your research paper is based on specific research questions or hypotheses, briefly address whether you’ve answered them or achieved your research goals. Discuss the significance of your findings in this context.
• Significance : Highlight the importance of your research and its relevance in the broader context. Explain why your findings matter and how they contribute to the existing knowledge in your field.
• Implications : Explore the practical or theoretical implications of your research. How might your findings impact future research, policy, or real-world applications? Consider the “so what?” question.
• Future Research : Offer suggestions for future research in your area. What questions or aspects remain unanswered or warrant further investigation? This shows that your work opens the door for future exploration.
• Closing Thought : Conclude your research paper conclusion with a thought-provoking or memorable statement. This can leave a lasting impression on your readers and wrap up your paper effectively. Avoid introducing new information or arguments here.
• Proofread and Revise : Carefully proofread your conclusion for grammar, spelling, and clarity. Ensure that your ideas flow smoothly and that your conclusion is coherent and well-structured.

Write your research paper conclusion 2x faster with Paperpal. Try it now!

Remember that a well-crafted research paper conclusion is a reflection of the strength of your research and your ability to communicate its significance effectively. It should leave a lasting impression on your readers and tie together all the threads of your paper. Now you know how to start the conclusion of a research paper and what elements to include to make it impactful, let’s look at a research paper conclusion sample.

How to write a research paper conclusion with Paperpal?

A research paper conclusion is not just a summary of your study, but a synthesis of the key findings that ties the research together and places it in a broader context. A research paper conclusion should be concise, typically around one paragraph in length. However, some complex topics may require a longer conclusion to ensure the reader is left with a clear understanding of the study’s significance. Paperpal, an AI writing assistant trusted by over 800,000 academics globally, can help you write a well-structured conclusion for your research paper. 

• Sign Up or Log In: Create a new Paperpal account or login with your details.  
• Navigate to Features : Once logged in, head over to the features’ side navigation pane. Click on Templates and you’ll find a suite of generative AI features to help you write better, faster.  
• Generate an outline: Under Templates, select ‘Outlines’. Choose ‘Research article’ as your document type.  
• Select your section: Since you’re focusing on the conclusion, select this section when prompted.  
• Choose your field of study: Identifying your field of study allows Paperpal to provide more targeted suggestions, ensuring the relevance of your conclusion to your specific area of research. 
• Provide a brief description of your study: Enter details about your research topic and findings. This information helps Paperpal generate a tailored outline that aligns with your paper’s content. 
• Generate the conclusion outline: After entering all necessary details, click on ‘generate’. Paperpal will then create a structured outline for your conclusion, to help you start writing and build upon the outline.  
• Write your conclusion: Use the generated outline to build your conclusion. The outline serves as a guide, ensuring you cover all critical aspects of a strong conclusion, from summarizing key findings to highlighting the research’s implications. 
• Refine and enhance: Paperpal’s ‘Make Academic’ feature can be particularly useful in the final stages. Select any paragraph of your conclusion and use this feature to elevate the academic tone, ensuring your writing is aligned to the academic journal standards. 

By following these steps, Paperpal not only simplifies the process of writing a research paper conclusion but also ensures it is impactful, concise, and aligned with academic standards. Sign up with Paperpal today and write your research paper conclusion 2x faster .  

The research paper conclusion is a crucial part of your paper as it provides the final opportunity to leave a strong impression on your readers. In the research paper conclusion, summarize the main points of your research paper by restating your research statement, highlighting the most important findings, addressing the research questions or objectives, explaining the broader context of the study, discussing the significance of your findings, providing recommendations if applicable, and emphasizing the takeaway message. The main purpose of the conclusion is to remind the reader of the main point or argument of your paper and to provide a clear and concise summary of the key findings and their implications. All these elements should feature on your list of what to put in the conclusion of a research paper to create a strong final statement for your work.

A strong conclusion is a critical component of a research paper, as it provides an opportunity to wrap up your arguments, reiterate your main points, and leave a lasting impression on your readers. Here are the key elements of a strong research paper conclusion: 1. Conciseness : A research paper conclusion should be concise and to the point. It should not introduce new information or ideas that were not discussed in the body of the paper. 2. Summarization : The research paper conclusion should be comprehensive enough to give the reader a clear understanding of the research’s main contributions. 3 . Relevance : Ensure that the information included in the research paper conclusion is directly relevant to the research paper’s main topic and objectives; avoid unnecessary details. 4 . Connection to the Introduction : A well-structured research paper conclusion often revisits the key points made in the introduction and shows how the research has addressed the initial questions or objectives. 5. Emphasis : Highlight the significance and implications of your research. Why is your study important? What are the broader implications or applications of your findings? 6 . Call to Action : Include a call to action or a recommendation for future research or action based on your findings.

The length of a research paper conclusion can vary depending on several factors, including the overall length of the paper, the complexity of the research, and the specific journal requirements. While there is no strict rule for the length of a conclusion, but it’s generally advisable to keep it relatively short. A typical research paper conclusion might be around 5-10% of the paper’s total length. For example, if your paper is 10 pages long, the conclusion might be roughly half a page to one page in length.

In general, you do not need to include citations in the research paper conclusion. Citations are typically reserved for the body of the paper to support your arguments and provide evidence for your claims. However, there may be some exceptions to this rule: 1. If you are drawing a direct quote or paraphrasing a specific source in your research paper conclusion, you should include a citation to give proper credit to the original author. 2. If your conclusion refers to or discusses specific research, data, or sources that are crucial to the overall argument, citations can be included to reinforce your conclusion’s validity.

The conclusion of a research paper serves several important purposes: 1. Summarize the Key Points 2. Reinforce the Main Argument 3. Provide Closure 4. Offer Insights or Implications 5. Engage the Reader. 6. Reflect on Limitations

Remember that the primary purpose of the research paper conclusion is to leave a lasting impression on the reader, reinforcing the key points and providing closure to your research. It’s often the last part of the paper that the reader will see, so it should be strong and well-crafted.

• Makar, G., Foltz, C., Lendner, M., & Vaccaro, A. R. (2018). How to write effective discussion and conclusion sections. Clinical spine surgery, 31(8), 345-346.
• Bunton, D. (2005). The structure of PhD conclusion chapters.  Journal of English for academic purposes ,  4 (3), 207-224.

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In a short paper—even a research paper—you don’t need to provide an exhaustive summary as part of your conclusion. But you do need to make some kind of transition between your final body paragraph and your concluding paragraph. This may come in the form of a few sentences of summary. Or it may come in the form of a sentence that brings your readers back to your thesis or main idea and reminds your readers where you began and how far you have traveled.

So, for example, in a paper about the relationship between ADHD and rejection sensitivity, Vanessa Roser begins by introducing readers to the fact that researchers have studied the relationship between the two conditions and then provides her explanation of that relationship. Here’s her thesis: “While socialization may indeed be an important factor in RS, I argue that individuals with ADHD may also possess a neurological predisposition to RS that is exacerbated by the differing executive and emotional regulation characteristic of ADHD.”

In her final paragraph, Roser reminds us of where she started by echoing her thesis: “This literature demonstrates that, as with many other conditions, ADHD and RS share a delicately intertwined pattern of neurological similarities that is rooted in the innate biology of an individual’s mind, a connection that cannot be explained in full by the behavioral mediation hypothesis.”  

Highlight the “so what”  

At the beginning of your paper, you explain to your readers what’s at stake—why they should care about the argument you’re making. In your conclusion, you can bring readers back to those stakes by reminding them why your argument is important in the first place. You can also draft a few sentences that put those stakes into a new or broader context.

In the conclusion to her paper about ADHD and RS, Roser echoes the stakes she established in her introduction—that research into connections between ADHD and RS has led to contradictory results, raising questions about the “behavioral mediation hypothesis.”

She writes, “as with many other conditions, ADHD and RS share a delicately intertwined pattern of neurological similarities that is rooted in the innate biology of an individual’s mind, a connection that cannot be explained in full by the behavioral mediation hypothesis.”  

Leave your readers with the “now what”  

After the “what” and the “so what,” you should leave your reader with some final thoughts. If you have written a strong introduction, your readers will know why you have been arguing what you have been arguing—and why they should care. And if you’ve made a good case for your thesis, then your readers should be in a position to see things in a new way, understand new questions, or be ready for something that they weren’t ready for before they read your paper.

In her conclusion, Roser offers two “now what” statements. First, she explains that it is important to recognize that the flawed behavioral mediation hypothesis “seems to place a degree of fault on the individual. It implies that individuals with ADHD must have elicited such frequent or intense rejection by virtue of their inadequate social skills, erasing the possibility that they may simply possess a natural sensitivity to emotion.” She then highlights the broader implications for treatment of people with ADHD, noting that recognizing the actual connection between rejection sensitivity and ADHD “has profound implications for understanding how individuals with ADHD might best be treated in educational settings, by counselors, family, peers, or even society as a whole.”

To find your own “now what” for your essay’s conclusion, try asking yourself these questions:

• What can my readers now understand, see in a new light, or grapple with that they would not have understood in the same way before reading my paper? Are we a step closer to understanding a larger phenomenon or to understanding why what was at stake is so important?  
• What questions can I now raise that would not have made sense at the beginning of my paper? Questions for further research? Other ways that this topic could be approached?  
• Are there other applications for my research? Could my questions be asked about different data in a different context? Could I use my methods to answer a different question?  
• What action should be taken in light of this argument? What action do I predict will be taken or could lead to a solution?  
• What larger context might my argument be a part of?  

What to avoid in your conclusion  

• a complete restatement of all that you have said in your paper.  
• a substantial counterargument that you do not have space to refute; you should introduce counterarguments before your conclusion.  
• an apology for what you have not said. If you need to explain the scope of your paper, you should do this sooner—but don’t apologize for what you have not discussed in your paper.  
• fake transitions like “in conclusion” that are followed by sentences that aren’t actually conclusions. (“In conclusion, I have now demonstrated that my thesis is correct.”)
• picture_as_pdf Conclusions

What Does a Conclusion Generally Imply?

If you do not know how to write a conclusion for research paper , you should remember the main thing – it should summarize the key points of the paper. It should also help readers understand the basic information, be memorable, and leave an impression because it is the last thing people read. The conclusion happens to be the best chance for you to both tie all the information together and mention the main points one more time. In general, it is one of the most important parts of a research paper.

In most cases, the conclusions of research papers are one paragraph long. Even though they do not usually introduce new data or information, they tend to offer a new perspective on the topic or reframe the issues.

Why Is It Important to Come Up With a Good Conclusion?

It is important to write an impressive conclusion research paper because it must serve a few critical purposes:

• It offers recommendations and implications . A conclusion happens to be an excellent place to talk about the broader implications of your research and proffer probable areas for further study. This part of your paper is also a great chance to provide practical recommendations based on the data you find.
• It provides closure . An outstanding conclusion delivers a sense of closure to your paper. The thing is that it should leave the audience with a feeling that they have reached the end of a thought-provoking and well-structured research project.
• It should leave a lasting impression . If you manage to come up with a well-crafted research paper conclusion, it will leave a lasting impression on your audience. You should remember that it is your final opportunity to leave readers with a call to action, a new idea, or a memorable quote.

What Types of Research Paper Conclusions Are There?

As mentioned before, a research paper conclusion delivers closure to the reader. When choosing a type, you should consider the goals of your study, its nature, and your target audience. Further, you can see the three common types of conclusions:

• Summarizing conclusion. This is the most common type , which is utilized across various disciplines. It tends to include reiterating the research question, the key points, and mentioning the significance of the findings one more time.
• Editorial conclusion. This type is less common, but you can use it in research papers that are focused on advocating or proposing a certain policy or viewpoint. It includes showing a strong opinion based on the data and offering calls to action or recommendations.
• Externalizing conclusion. It is a kind of conclusion that extends the research beyond the paper`s scope by offering probable future research directions. It may also discuss the wider implications of the findings. They usually use this type of conclusion in more exploratory or theoretical research papers.

Tips on Writing a Conclusion for a Research Paper

If you do not know how to write a conclusion for a research paper , you should consider the following steps:

You should restate your research topic

The first step when writing a conclusion must be to restate your research topic. In most cases, one sentence is enough for this purpose, and you should also explain the importance of your topic. You should bear in mind that this part must be concise and clear and state only the essential information.

Writing Metier experts also recommend avoiding such phrases as “ in summary ”, “ in conclusion ”, and “ in closing ”. This kind of phrases might be helpful in oral presentations , but it can turn out to be unnecessary and too obvious when ending an essay.

You should restate the thesis

This is the next step that can be performed by revising the initial thesis that you introduced in the paper`s introduction. At the same time, you should bear in mind that the thesis statement in your conclusion must be put in different words than those you used in your introduction. It is also possible to write this element effectively in one sentence.

You should summarize the key points of your paper

You can sum up the key points of your paper. The thing is that, it is helpful to read through the text a second time to see only the most important arguments and facts.  At the same time, there is no need to mention any more details than the key facts or arguments that you introduced in your paper.

The goal of summarizing these facts is to remind the audience of how important the research paper topic is.

You should connect the results or significance of the key points

You can introduce the significance of the key points after you discuss them. For example, after stating the key points you made in your argument, you may mention how the impacts of the topic can cause a certain outcome.

You might also introduce the results of studies that may assist you in adding emphasis to how you introduce the significance of your information. At the same time, there is no need to surprise the audience with new data in your conclusion that was not mentioned in your paper.

The conclusion is the part where you describe the value of your research and introduce your understanding of the data you have presented.

You should conclude your thoughts

When finishing your conclusion, you may come up with a call to action or pose an idea that will make your audience think further about your statements. Students can also use this sentence to address any issues that were left unanswered in the paper`s body paragraphs.

Effective Strategies for Writing a Research Paper Conclusion

Your conclusions in a research paper are the opposite of the introduction in terms of both placement and structure. The introduction should generally follow the inverted triangle format with the main statement element on top and the main point of research at the bottom.

The conclusion should follow the inverted introduction format by starting with the highlights of your findings and finishing with a general statement that should encourage the audience to think. It should also challenge the readers to take action based on what they have learned from your paper.

A few studies that performed an analysis of how conclusions are structured found that the bigger part of authors either synthesize the research work or restate and consolidate a research issue. When consolidating the issue, they tend to introduce the solutions or results of research.

The following tips can help you come up with an impressive conclusion:

Synthesize instead of summarizing

Yu already know that a research paper conclusion is not a summary of the content. A summary can be a part of this section, but the conclusion should go beyond simply restating your analyses and ideas.

Instead of mentioning again what you already said in the introduction, abstract, and body, you should show your audience how the main elements in your paper fit together.

Echoing the introduction

This method of writing brings your audience to a full circle by referring to or utilizing the same elements you used in your introduction. A research paper conclusion example of this would be retelling a scenario you presented in your introduction, but this time, you should manage to create a new understanding of the topic based on the study`s results.

Redirect your reader

Your conclusion should act as a bridge for your readers back to the real world after welcoming them into your research through your introduction and showing them your analyses, methodologies , and results. When you redirect your audience, you challenge them to take the data they receive from your paper and use it in real life.

It is also possible to approach this strategy by offering solutions to an existing issue or a course of action for further studies.

Challenge your own conclusion

This one is also known as the “so what” game, and it requires you to challenge your own ideas by asking this question while creating your conclusion. After you finish this part of the research paper, you might ask someone who will go through the content and challenge what you wrote.

For example, you can ask your friend to read it with you and have them ask this question after every viewpoint. Thanks to this strategy, you can find weaknesses in your conclusion and refine it in the process.

Address limitations

This method should also help you find the weak points in your paper, which should involve the aspects where there is a lack of argument. It will also allow you to see whether there are instances where your conclusion may be wrong.

This approach is especially helpful in writing conclusions of scientific research papers.

Show ideas to create a new meaning or picture

You should interpret all the relevant data in appropriate depth. It means that you should explain how the mechanisms or methodologies were utilized to help your audience get to the results of your study.  You should also consider that your study might not bring the results you expected.

In this scenario, you need to explain to your readers why this situation may have happened. If the outcome is what you expected, you should describe your theory supported by your evidence.

Pose questions

Research studies tend to be motivated by questions. Posing questions in a research paper conclusion , either to your audience or in general, might help them get a new point of view on the topic, which they might never have had before diving into your conclusion. It might also help you bring your key points together to come up with a new idea from your study.

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Elements That You Should Never Include in Your Conclusion

If you are willing to figure out how to write conclusions for research papers , you also need to know what things should never be included. So, here is a list of these elements:

• Dry summary . You should always bear in mind that summarizing might be an important part of a conclusion, but it is not the only part. The thing is that your conclusion must be more than just a summary – it should form your reader`s opinion about your topic. That is why you should not just repeat the facts. You need to contextualize them for the audience, offer a step for solving the issue, or suggest a new perspective.
• You should avoid too generic words . It is recommended to avoid cliched or generic phrasing in your conclusion. The reason is that some phrases or words are used too often to the point of becoming trite. If you are willing to come up with well-written and fresh research paper conclusions, you should never use the following phrases:
• In closing;
• In conclusion;
• In summary;
• To wrap up.
• No need to use new evidence or data . Research paper conclusions are the wrong place to present new data or evidence, especially if they are impressive enough to reshape the entire argument. Supporting evidence and significant facts belong to the paper`s body. When the audience is reading this section, they are still learning more about the topic. The reader tends to form their opinion by the conclusion. Therefore, the conclusion is more about retrospection. This implies that presenting unexpected information there can be frustrating to the audience.
• Never ignore negative results . You might want to hide negative results or just ignore them completely, but this action will harm your research paper in the end. The best solution is to own up to shortcomings in your paper and admit them. Thanks to your transparency, you will easily validate your other findings and prevent critics from introducing these shortcomings to damage the outcome.
• You should avoid ambiguous resolutions . Conclusions of research papers are expected to clean up any loose ends and wrap up arguments. If your conclusion happens to be ambiguous, it may seem that your research was inadequate, incomplete, or fundamentally flawed. Therefore, you should write your final words with direct language and take a firm position. Even if the information was inconclusive, you should state clearly that it was inconclusive. Thanks to this, you are going to sound both competent and confident.

Research Paper Conclusion Examples

Your conclusion must be a compelling close to the paper as a whole, which implies that it should highlight your hard work and research. Obviously, your conclusion must represent your unique style, but the following research paper conclusion example texts might be a starting point:

Ultimately, the data we researched all lead to the same conclusion: When encouraging a great work-life balance, we improve employee productivity, which can benefit the company in general. The research states that when workers feel that their personal lives are respected and valued by their employers, they tend to be more productive when at work. Moreover, company turnover is usually reduced when workers have a balance between their professional and personal lives. Even though additional research is necessary to determine ways companies can support employees in establishing a better work-life balance, it is obvious the need is there.

Social media happens to be a primary way of communication among young individuals. As we have seen in the introduced data, the bigger part of young people in high school utilizes a diversity of social media apps at least every hour, including Facebook and Instagram. While social media is a way to connect with peers, research supposes that its use correlates with body image problems. Young girls who have lower self-esteem usually utilize social media more often than those who do not use these apps daily. As the popularity of new applications is increasing and as more students are provided with smartphones, more research will be necessary to estimate the outcome of prolonged social media use.

Allow Writing Metier Experts to Write a RP Conclusion for You

You now have an example of research paper conclusion , and it might help you to come up with an outstanding piece. The importance of an impressive conclusion is obvious, but it is not an easy thing to write. That is why many students ask for help from someone who knows how to create a good conclusion.

Writing Metier has a team of professional research paper writers who are able to cope with any sort of assignments, including research paper conclusions. We can craft an ideal conclusion for your findings, and it will be impressive.

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There is no need to worry about anything or think about when to get time to write a conclusion. Instead, you can focus on your studies or work and allow expert writers from Writing Metier to do everything for you.

Frequently Asked Questions

What should i introduce in my research paper conclusion.

If you are eager to learn how to write a conclusion of a research paper , you should consider adding a few key elements, such as:

• A restatement of the research issue.
• A brief discussion of your research`s implications.
• A summary of your main arguments or findings.

Do I Need to Introduce New Arguments in my Research Paper Conclusion?

You should not add new arguments in your conclusion. It is a great idea to follow the formal structure of a paper, but new data can confuse your audience.

What Other Sections Should Be Included in My Research Paper?

The important parts of a research paper are usually the following:

• Introduction. This should include an introductory sentence, a thesis sentence with three main points, and a minimum of three supporting sentences.
• Body paragraphs that support each of the points.
• Conclusion. This is where you need to restate the thesis, add a minimum of two sentences that summarize your findings, and end with a suggestion for future research or a declarative statement.

What Is the Goal of a Research Paper Conclusion?

The goal of any conclusion is to sum up the key points of the paper, leave a lasting impression on the audience, and help them contextualize the data.

How Many Pages Should a Research Paper Conclusion Include?

There is no certain length for a conclusion. At the same time, it is a great idea not to make it too long because conclusions are expected to be succinct. Therefore, you should keep this part around 5 to 10 percent of the research paper`s total length. For example, for a paper that is 10 pages long, you should provide a conclusion under one page.

What Different Types of Research Paper Conclusions Are There?

Even though there are no formal types of research paper conclusions, they tend to fall under the categories of summarizing, editorial, and externalizing conclusions. You should bear in mind that these types are not mutually exclusive – the same paper might be both externalizing and summarizing. That is why you should follow the guidelines and requirements of your assignment.

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Laura Orta is an avid author on Writing Metier's blog. Before embarking on her writing career, she practiced media law in one of the local media. Aside from writing, she works as a private tutor to help students with their academic needs. Laura and her husband share their home near the ocean in northern Portugal with two extraordinary boys and a lifetime collection of books.

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Hypothesis | Definition

Hypothesis refers to a testable statement or prediction about the relationship between two or more variables in scientific research.

Understanding Hypothesis

In social science research, a hypothesis plays a crucial role in guiding the research process. It is essentially an educated guess or a prediction that researchers formulate based on existing theories, observations, or knowledge. A hypothesis helps define the direction of the study and provides a framework for data collection and analysis.

The Importance of a Hypothesis

A hypothesis is central to the research process for several reasons:

• Focuses the Study : By making a specific prediction, the hypothesis narrows the focus of the research. Instead of exploring a broad question, researchers can zero in on testing the specific prediction made by the hypothesis.
• Guides Research Design : Once a hypothesis is formulated, researchers can design their study in a way that either supports or refutes the hypothesis. This includes choosing appropriate research methods, collecting relevant data, and conducting analyses.
• Provides Direction : A clear hypothesis helps ensure that the research is purposeful and organized. It gives researchers a goal to work toward and a means to measure their findings against their predictions.
• Enables Testing of Theories : Many hypotheses are derived from existing theories. By testing a hypothesis, researchers can assess whether the theory holds up in different contexts or under different conditions.

Components of a Hypothesis

A well-formulated hypothesis usually contains several key components:

• Variables : These are the elements that the researcher is studying. Typically, a hypothesis involves an independent variable (the cause or predictor) and a dependent variable (the effect or outcome). For example, a researcher might hypothesize that “increased study time (independent variable) leads to higher test scores (dependent variable).”
• Relationship : The hypothesis also specifies the expected relationship between the variables. In the example above, the hypothesis predicts a positive relationship between study time and test scores.
• Testability : A hypothesis must be testable through empirical observation or experimentation. If a hypothesis cannot be tested, it remains a speculation or an idea rather than a scientific hypothesis.
• Falsifiability : For a hypothesis to be scientific, it must be falsifiable, meaning that it can be proven wrong. If a hypothesis cannot be disproven, it is not considered scientifically valid.

Types of Hypotheses

There are several types of hypotheses used in social science research, each serving a unique purpose. The most common types are:

1. Null Hypothesis (H0)

The null hypothesis asserts that there is no relationship between the variables being studied. It acts as a default assumption that the researcher tries to disprove or reject. For example, the null hypothesis might state, “There is no relationship between study time and test scores.”

Researchers typically use statistical tests to determine whether they can reject the null hypothesis. If the evidence suggests a significant relationship between the variables, the null hypothesis is rejected.

2. Alternative Hypothesis (H1)

The alternative hypothesis suggests that there is a relationship between the variables. It is the opposite of the null hypothesis. For example, the alternative hypothesis might state, “Increased study time is associated with higher test scores.”

The goal of the research is usually to provide enough evidence to support the alternative hypothesis.

3. Directional Hypothesis

A directional hypothesis makes a specific prediction about the direction of the relationship between variables. In other words, it predicts whether the relationship is positive or negative. For example, “Students who spend more time studying will score higher on tests.”

Directional hypotheses are often used when previous research or theory suggests a specific outcome.

4. Non-Directional Hypothesis

A non-directional hypothesis predicts that there will be a relationship between the variables but does not specify the direction of the relationship. For instance, “There is a relationship between study time and test scores.” Non-directional hypotheses are useful when the researcher is unsure whether the variables are positively or negatively correlated.

5. Complex Hypothesis

A complex hypothesis involves more than two variables and predicts the relationships among them. For example, “Increased study time and use of study aids will result in higher test scores.” Complex hypotheses are common in social science research, where multiple factors often interact to influence outcomes.

How to Formulate a Hypothesis

Formulating a strong hypothesis requires careful thought and consideration of existing knowledge and research. Here are some steps to guide you through the process:

1. Identify the Research Question

The first step in formulating a hypothesis is to identify a research question. This is the broader question you are trying to answer through your study. For example, “What factors influence student test scores?”

2. Conduct a Literature Review

A thorough review of the existing literature helps you understand what is already known about the topic. This step allows you to build on previous research and avoid duplicating studies. It also helps you identify gaps in the literature that your research could fill.

3. Identify the Variables

Next, determine which variables you want to study. In our example, the variables are “study time” and “test scores.” Make sure your variables are measurable and observable.

4. Make an Educated Guess

Based on the literature review and your understanding of the topic, make a prediction about how the variables are related. This prediction forms the basis of your hypothesis. For instance, you might predict that “students who study more will perform better on tests.”

5. Ensure Testability

Finally, ensure that your hypothesis is testable. This means you need to be able to collect data and analyze it to either support or reject your hypothesis.

Testing a Hypothesis

Once a hypothesis is formulated, the next step is to test it. This typically involves collecting data and analyzing it to determine whether the hypothesis is supported. Researchers use various methods to test hypotheses, including experiments, surveys, and observational studies.

1. Data Collection

The method of data collection will depend on the nature of the hypothesis and the research design. For example, if the hypothesis predicts that increased study time leads to better test scores, the researcher could collect data through surveys, test scores, and time logs.

2. Statistical Testing

Statistical tests are used to determine whether the data support the hypothesis. For instance, a common method is to conduct a correlation analysis to examine the relationship between study time and test scores.

3. Interpretation of Results

Once the data have been analyzed, researchers interpret the results to determine whether they support or refute the hypothesis. If the data show a significant relationship between the variables, the hypothesis is supported. If no relationship is found, the hypothesis is rejected.

Hypothesis in the Context of Social Science

In social science, hypotheses are essential for developing new theories, testing existing theories, and exploring relationships between social phenomena. Because social science often deals with complex and multifaceted human behaviors, hypotheses in this field must account for a wide range of variables and external factors.

For instance, a social scientist studying education may hypothesize that smaller class sizes improve student performance. However, they must also consider other variables, such as teacher quality, socioeconomic status, and access to resources. In this way, social science hypotheses often involve multiple variables and interactions.

Hypothesis and Research Ethics

It is important to consider ethics when formulating and testing hypotheses. Ethical considerations ensure that research does not harm participants and that the research process is transparent and unbiased. Researchers should avoid forming hypotheses that could lead to biased or misleading conclusions. Additionally, they must ensure that their testing methods respect participants’ rights and privacy.

A hypothesis is a vital element in the research process. It provides a focused and testable prediction about the relationship between variables, guiding researchers through data collection and analysis. By formulating a clear and testable hypothesis, social scientists can explore complex social phenomena, test theories, and contribute to the advancement of knowledge in their field.

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The Environmental Kuznets Curve (EKC) Hypothesis on GHG emissions: analyses for transportation industry of South Africa

• Open access
• Published: 27 September 2024
• Volume 5 , article number  302 , ( 2024 )

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• Oluwole Joseph Oladunni   ORCID: orcid.org/0000-0002-2718-7974 1 , 2 ,
• Oludolapo A. Olanrewaju 1 &
• Carman K. M. Lee 2  

A series of energy-econometrics techniques were employed for a 5-year time span between 2016 and 2020. The tests of Environmental Kuznets Curve (EKC) hypothesis were conducted essentially to examine the significance of economic growth (GDP), energy consumption (EC), with energy intensity (EI), and on-road passenger vehicles (PV) as related to economic development on the mitigation of carbon emissions (CO 2 - eq ) in the transportation industry of South Africa. The findings from the prevailing research imply that, with respect to South Africa’s transportation industry, CO 2 - eq emissions increased in the course of early phases of economic growth while it tends to decline at certain levels of economic threshold. Though the nation maintains the edge of turning points in both the industrial and circular economy. The results further indicate a nexus between GDP and EC, which consequently affect the CO 2 - eq emissions. The findings proffer the needs to monitor the EC from the long-run impacts alongside the short run impacts of the forecast. The per capita GDP from the short-run impacts of t-stat—(4.928) to the long run effects of t-stat—(5.033) rises, indicating its improper influence in the industry. To limit the use of fossil-based fuels, as demonstrated in the negative signal of EI for long-run impacts of a p-value (0.2835), then to the short run effects which possess a significant p-value. It also highlights the directional correlation surfacing between EC, EI and South Africa’s on-road PV. In the computation context, the series was determined to be stationary at its first differences, as evident by the R 2 combined with the R 2 (Adjusted) values of 0.9837 and 0.9827, respectively, for both long-run and short-run assessments. The indication of the research among others further reveals that public transportation systems of road and rail options, which have the potentials to incorporate alternative energy sources, can be the required efforts to mitigate climate change and global warming effects in the transportation industry.

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1 Introduction

The World Health Organization links air pollution to airborne particles that are harmful to living beings when they exceed a certain concentration threshold [ 1 ]. Greenhouse gases (GHG) are the gaseous compounds present in the atmosphere. They absorb infrared radiation and retain heat in the atmosphere, this is responsible for the greenhouse effect, eventually leading to global warming. On the necessity of economic development, owing to the expansion of economic growth, there has been a significant rise in transportation activities, industrial production, energy use and other human activities. These increased activities typically rely on the utilization of polluting energies and natural resources to the extent that economic development is frequently considered as a possible contributor to environmental degradation. In 2018, the International Energy Agency ranked the transportation industry second by virtue of its extensive reliance on fossil fuels (FFs) globally in terms of energy-related GHG and carbon dioxide (CO 2 ) emissions [ 2 ]. In the near future, road transportation, including passenger and freight vehicles, is expected to use more energy, with increase in emissions of over 50% [ 3 , 4 , 5 ].

Global concerns include the need for immediate action to mitigate GHG emissions given the increasing impact of the transportation industry on the environment [ 6 ]. The emission of GHG particularly carbon compounds, has far-reaching effects that extend beyond the surface consequence of global warming alone. In addition to the increase in respiratory and cardiovascular diseases, the number of all types of such associated diseases are also on the rise for the concerns of public health. These diseases ultimately result in a reduced lifespan for humans [ 7 ]. The transportation sector is among the principal sectors that trigger a country’s economic growth, in full measure, it impacts daily activities. However, it is strained as one of the main sources of energy consumption, resulting in environmental degradation. The discourse among researchers and experts in twenty-first century has focused on the collateral damage to our world due to the unbearable increase in carbon emissions that led to global warming from the outcome of economic developments that resulted to environmental degradation [ 8 ].

Over the course of decades, the transportation industry has relied heavily on nonrenewable energy sources, mostly fossil fuels, this has led to severe environmental effects, significant and increasing contribution to global GHG emissions [ 9 ]. The transportation industry continues to play a significant role in all the economic sectors that are main contributors to carbon emissions. It was found that the reason for the increasing energy consumption in the transport sector is the escalating increase in passenger vehicles and the increase in income earned by vehicle users. The sector as indicated in the literature, has its primary direct causes of carbon emissions from multiple dimensions of privately acquired passenger vehicles, accounting for over 700 million on-road passenger vehicles globally [ 4 , 10 ]. It is now a well-known fact, that achieving emissions’ mitigation in transportation industry is more sophisticated than realizing reductions from stationary sources [ 11 ].

This ever-increasing debate in the turn of the twenty-first century has to focus on the economic development and environmental degradation associated to carbon emissions, and consequently the global warming [ 12 , 13 ]. This necessitates global exclamation for several quarters. The global CO 2 atmospheric emissions based on the analysis conducted from NOAA’s Global Monitoring Laboratory is 414.72 parts per million [ 14 ]. Although China and the United States remain the leading emitters, Africa as accounted by records of formal inspections is found to generate fewer emissions than the rest of the world. However, worldwide carbon emissions in global temperature have now exceeded 1.26 0 C, evident by Hansen et al. [ 15 ]. Moreover, it is not only in the interests of South Africa and Africa, or any nation and, or continent; our world at large are all to take responsibility and be accountable on the bearable reduction of GHG emissions for the required lively air quality with serenity. In OECD nations the effects of transport infrastructure, economic growth, energy consumption, energy sources and carbon emissions were investigated on both short run and long run to determine the level of negative impacts of air quality and the measures to be taken to have an eco-friendly sustainable environment [ 13 , 16 , 17 ]. Countries worldwide, particularly those of developed economies, have acknowledged the importance of proper energy use with by-product emissions for optimal and strategic reductions. It is imperative to address the concerns regarding carbon emissions because emanating emissions negatively impact all forms of mortality via their influence on environmental air quality [ 18 ]. Africa carries the upset notoriety of having the highest mortality rate globally, many due to improper air quality as evidenced by available data (World Health Organization, 2018).

The primary source of energy used in the transportation sector is non-renewable energy of fossil fuel types, such as oil and gas, which discharge large amounts of GHG emissions [ 19 ]. This negatively impacts the environment and is responsible for a growing proportion of global emissions. The United Nations Conference on Trade and Development, which was established to further advance the role of the organization, stated that the transportation industry globally consumed approximately 67% of petroleum products in 2012. Based on the analysis, it has been forecasted that by the year 2035, if no drastic measures are taken, the energy consumption of fossil fuels will increase to 82%, and due to the persistent increase in passenger vehicles, its demand is deemed to rise to 78% by 2040 [ 20 ]. The outcomes surrounding these circumstances are the emissions of pollutants, particularly those of greenhouse gases present in the atmosphere, and of capable CO 2 equivalence. As it is estimated that there will be a 25% increase in CO 2 emissions only due to the combustion of fossil fuels in the transportation industry, it remains imperative to conduct research studies focusing on its mitigation. Furthermore, it is expected that CO 2 emissions will increase to 1.7% annually in industrialized emerging economies by 2030 [ 21 ], this is a better fit for concrete and genuine research engagements.

The transportation industry is one of the largest energy consumers, with increasing access of connectivity for point-to-point transfer of peoples, goods and services over the years. This has contributed to the increasing economic development. Although there is a growing demand for transportation services, it will result in increased energy consumption as the case applies, and thus the burning of fossil fuels. Consequently, this degrades air quality and the environment. The mitigation of CO 2 emissions, air pollution control due to road transport activities, energy management, and required freight management have been the prioritized objectives of sustainable ecosystems that are eco-friendly. In Africa, the highest energy consumption is still in the order of fossil fuels > coal > natural gas. Fossil fuels are frequently used in the transportation sector, whereas renewable energy usage is very minute in comparison. Although Africa is rich in clean energy sources that could better enhance air quality, however due to concerns in technological advancements and innovations, drive and will, it still relies heavily on non-renewable sources that degrade the environment [ 6 , 22 ]. By virtue of the excesses in the utilization of fossils that are consequently factored in sectorial degradation, calls have been made to curb its menace by shifting to clean renewable energy sources, thereby enhancing environmental sustainability. Previous research has demonstrated a nexus between transport energy consumption, economic growth, and carbon emissions in the transportation sector. Transportation is crucial in South Africa and has a significant impact on how daily tasks are carried out. Over the past 20 years, South Africa's population has increased to 60 million, with an economic growth rate of 2.39% from 1994 to 2022. Human population and economic growth are the main influencing factors that enhance the transportation industry, and subsequently, passenger vehicles. Saidi et al. [ 23 ] found that an increase in freight transport and per capita income played a significant role in deteriorating the quality of the environment. Nevertheless, the transportation industry is one of the main sectors in which energy consumption is at high ratio.

The transportation industry of South Africa has undergone significant growth over the years, however, this expansion has resulted in a number of environmental degradations, particularly those caused by CO 2 - eq emissions from excessive energy consumption. Estimates based on data provided by Statistics South Africa and South Africa Department of Transportation show that passenger turnover increased from 50.2 billion person-kilometres in 2010 to 152.6 billion person-kilometres in 2020, while it increased from 231.48 billion ton-km to 597 billion ton-km, for freight transportation [ 4 ]. According to the research conducted by Oladunni and Olanrewaju [ 6 ] of South Africa’s transportation industry, the energy (oil) consumption—EC of fossil fuels for the year 2020, which was estimated to be 74,498,076,377 L of kilometers covered, possesses a qualitative nexus to economic growth—in GDP of 101,659 Rand per capita. This, in turn led to the potency for energy intensity—EI of 523.359 tce/ Rand 10,000. Consequently, it produced degrading environmental impacts of around 426.3 million tons in equivalent of CO 2 emissions.

This research is pertinent as it proposes actions to enhance air and environmental quality in reducing GHGs, particularly CO 2 emissions in the transportation industry. The ultimate objective of this study is to analyze the contributions of selected environmental driving forces to carbon emissions in the transportation industry of South Africa and how they impact economic development. Furthermore, it adds to the body of literature, for which few already available on the nexus among energy consumption, its intensity, economic growth, and the required decline in carbon emissions. Consequently, the examined model's study of South Africa presents vital engineering management techniques in addressing the prevailing concerns of GHGs, and more in particular that of the CO 2 emissions for the transportation industry by adopting energy econometrics approaches.

The subsequent sections of the research are as follows: section two presents the literature of relevant studies to the present objective. The section three gives a comprehensive description of the parametric materials and variables, using the procedures that guided the study. The empirical results of the investigation are reported in the fourth section. The discussion of the findings is addressed in the fifth section, and concludes by outlining the practical implications, policy recommendations, study limitations and research gaps for further studies.

2 Literature review

With the application of diverse econometric techniques, a sizable body of literature examines the viability of the EKC hypothesis in respect to GHG emissions of different countries and regions. This resulted in variations in the estimated results [ 24 , 25 ]. Based on this hypothesis, the links between environmental pollution and economic growth per capita are in many cases (on a few exception) indicated to be inverted U-shape. This implies that working population earnings increase in tandem with economic growth. Therefore, environmental concerns will not require immediate intervention in the early stages, when environmental quality improves while the per capita income reaches the threshold known as the turning point. This hypothesis is as well demonstrated by Kang et al. 2016 [ 24 ]. Findings in the year 2016 from the studies of Kais and Sami [ 26 ] and Bilgili et al. [ 27 ] on EKC for GHG emissions show that the results depend on the type of analysis used (panel or time series) as well as the time period and geographical location that were studied. The pattern of the EKC hypothesis additionally supported by Danesh et al. [ 28 ] found that the majority of principal pollutants, including carbon monoxide (CO), nitrogen oxides (NOx), and sulfur oxides (SOx), sensed an inverted U-trajectory and supported the EKC hypothetical concept. According to Galeotti et al. [ 29 ], this link indicates multiple and mixed notions. While certain investigators observed a typical inverted U-shaped pattern, others expressed the notion that the turning point could not have been perfectly ideal [ 30 , 31 ]. Other researchers supported the findings for the existence of N-shaped correlations, as can be seen in the works of [ 32 ] and [ 33 ]. Nevertheless, the overwhelming nature of this research niche shows that economic growth does not directly translate into a long-term decrease in GHG emissions, as emissions are linked to economic expansion through energy consumption [ 34 , 35 ]. There is general agreement that rising energy consumption, which depends on the amount of energy the transportation sector uses, is the main cause of rising CO 2 emissions. However, the empirical results have shown that the evidence lacks stability because of variations in methodological approaches and for local, provincial, national, and global considerations, more specifically, the studied time period. Using statistical data from the United States, the empirical tests outcomes of [ 36 ] indicate that there is an unavoidable nexus between transportation energy consumption, income and fuels prices, one of which is a long-run relationship. The panel cointegration analysis conducted on OECD member states indicates that there is no connection between the price of gasoline, the amount of gasoline used (energy consumption), income, and car ownership in the short term. However, the results of the parametric variables demonstrate that they are connected. In a case study of the Malaysian economy, [ 37 ] analyzed some dynamic relationships between income, transportation energy consumption, and CO 2 emissions. The results demonstrate that income and transportation energy consumption are linked through the Granger causality. In their study of 107 economies, Liddle and Lung [ 38 ] evaluated the connection between per capita GDP and transport energy consumption and arrived at empirical findings that indicate that there is a long-term, positive unidirectional nexus between the two driving variables.

The Johansen cointegration results indicate that GDP impacts transportation energy consumption in the work of Achour and Belloumi [ 39 ]. However, the converse scenario is not applied in their analyses of the correlation between energy consumption and economic growth with respect to the economy of Tunisia. A generalized method of moments (GMM) was employed in the works [ 23 ] for the purpose of having a feedback confirmation on causality between transportation energy consumption and GDP of 75 nations of the world. Hence, determination was concluded. With the same methodology [ 40 ] empirically assessed the growth impact of public infrastructure under a panel of 18 OECD countries, revealing that infrastructure growth has a positive influence on labour productivity and total factor productivity. In recent years, a number of empirical studies have been conducted to better comprehend the variables of impacts on environmental quality, particularly energy consumption. Notwithstanding, there have been some attempts to shift from examining the environmental impacts of overall energy consumption to assessing the environmental effects of various energy sources, mostly non-renewable sources.

To further examine the correlation pattern for a country with a large human population, Maparu and Mazumder [ 41 ] assess the long-run causal relationship between transportation and economic growth in India. Vector Auto-regression and Vector Error Correction models were used to carry out short- and long-run causality checks, and the outcomes showed no long-run relationship. ARDL testing approach to cointegration and vector error correction model representation have been adopted to evaluate both the long-run and short-run links between economic growth, energy consumption, and carbon emissions to determine their consequential differences in impacts [ 42 , 43 ]. Rehermann et al. [ 44 ] examined the non-linear relationship between GDP per capita and transport energy consumption for countries in Latin America and the Caribbean. The findings support the N-shaped curve, while the elasticity values of transportation energy consumption with respect to GDP per capita do not demonstrate a tendency to decline over time. Sharif et al. [ 45 ] of ARDL using quantitative-on-quantitative (QQ) empirical research on the transportation-growth nexus, demonstrates that the United States’ transportation services benefit from economic growth. In addition, also with some considerations of ARDL as applied to Iran to include renewable and nonrenewable energies [ 46 ]. The fact that they serve as the driving forces for industrial development and economic growth, conversely, they lead to increase in the demand for mobility, increasing energy consumption, and intensity, investments in transportation infrastructure such as roads, highways, and bridges, and rising income levels. All of these play critical roles in the unbearable CO 2 emissions. For the purpose of achieving sustainable economic growth, [ 47 ] with [ 48 ] examined the EKC hypothesis in relation to substitution effect, growing contribution of transportation energy consumption to the resulting energy intensity and consequently the resulting GHG emissions. Energy intensity, which is a measure of a country’s energy efficiency, can be calculated either as total-factor energy efficiency or single-factor energy efficiency, as proposed by Pan et al. [ 49 ].

Being aware of how energy functions are essential, as increased energy consumption not only draws economies on track for industrialization, but also has the potential to worsen sustainability concerns [ 49 ]. Moreover, researchers continue to find it pertinent to investigate the response pattern of per capita GDP on the economy as it impacts transportation industry. The forecasts increase in GDP per capita in square or cubic functional forms can be measurable with considerable efforts. Taking into account the precepts of per capita GDP, the empirical test of [ 50 ] established long-standing assertions that adhering to environmental degradation in the short run would lead to positive environmental effects in the long run. The per capita GDP can be in its short-span increase or squared, and possibly with allowance, and then more. In EU countries, Sterpu et al. [ 51 ] investigate the validity of the EKC hypothesis by extending the per capita GDP to its quadratic [ 27 , 48 , 52 ] and cubic functional forms [ 53 , 54 , 55 ], examining the correlation between GHG emissions and per capita with the impact of energy consumption on GHGs. This is especially significant in urban areas where demand for automobile is highest [ 56 , 57 ]. In the modeled works of Gjorgievski et al. [ 58 ] as in the case of India argued that promoting nuclear energy production is the remedy to the nation’s GHG/CO 2 problems. The findings revealed that in the long term, increased nuclear energy use mitigates India’s carbon emissions. These have shown to be far-reaching evidence that one of the most important sectors for reducing carbon emissions is the transportation industry.

With respect to transportation, Alimujiang and Jiang [ 59 ] argue that energy is an essential component to maintaining economic growth, adding that an excessive reliance on fossil fuels could have two main adverse effects: (1) climate change and (2) air pollution, both of which pose threats to the planetary existence. Thus, the task of controlling air quality and climate change is critical. Nevertheless, new research endeavour are confirming the link between air pollution and global warming Zandalinas et al. [ 60 ]. Although relevant research on GHGs and CO 2 emissions from the transportation industry has made real strides, more actions are still required. Lu et al. [ 61 ] forecast future development trends for energy (oil) consumption and CO 2 emissions in the road transport industry and made recommendations to reduce intolerable oil usage. To forecast future development trends of energy consumption and GHG emissions for China and India’s road transport industry, Mittal et al. [ 62 ] created a good-fit model and assessed potential emission reduction programs. It is found that the study of China CO 2 emissions attracted lots of scrutiny. Wang et al. [ 63 ] assessed the EKC hypothesis through panel techniques by adopting provincial data of China and found the presence of a U-shaped theorem between economic growth and CO 2 emissions. On some occasions, energy intensity is assessed with convincing results to be a driving force for increasing and (primarily depending on its adaptability) mitigating CO 2 emissions and to ease the transition to low-carbon economy [ 64 ]. There is now large-scale evidence that economic development has a positive impact on the environment, while the same economic growth under loose regulatory conditions leads to increased energy consumption. As proposed by researchers, there are causal correlations between the driving forces of GHG emissions Hasan et al. [ 15 ]. The correlation between economic growth, energy consumption and intensity, passenger vehicles, and CO 2 reduction having studied by Roinioti and Koroneos, [ 65 ] demonstrates that they have both positive and negative impacts on human lives and air quality as further indicated by Khan et al. [ 66 ]. Based on these considerations, to efficiently reduce carbon emissions and enhance clean energy use, it is imperative to determine the correlation among energy consumption, its intensity, and economic growth on carbon emissions Zhao et al. [ 67 ]. Ensuing the well-known EKC framework Alataş [ 68 ], salient literary discussions have been held over the past two decades regarding the nexus between energy consumption, its intensity, and economic growth that led to deteriorating effects on air quality, which account for the increase in GHG emissions. Researchers, especially energy economics experts in the niche, have proffered that, buttressing the significance test of this hypothesis [ 69 , 70 , 71 ]. A growing body of research has looked at economic growth and energy consumption, but not simultaneously with their energy intensity to the yielding impacts of carbon emissions on the environment [ 71 , 72 ]. These studies examined developed, developing, and regional economies.

South Africa is investigated among the five developing countries examined by Sarkodie and Strezov [ 73 ] to determine the relationship between energy consumption and CO 2 emissions. Khan et al. [ 66 ] used the GMM technique to investigate the effects of energy consumption in transportation and logistics operations on environmental quality in 43 countries. Energy use and its intensities were demonstrated to determine the intensities of energy and how economic expansion affects environmental activities and the ensuing degradation in Malaysia and the OPEC countries, respectively [ 74 , 75 ]. Paramati et al. [ 76 ] applied FMOLS, CCEMG, and DOLS for their analysis to explore how energy can positively impact trade openness and economic growth in OECD countries. Further research activities reliably revealed that the ecological footprint (EF) for carbon emissions in the United States can be mitigated with controlled measures for natural resources, human capital, energy consumption, and economic growth impacts on EF of the United States. This is related to the determination of energy efficiency and its maximization for sectorial use, with respect to the transportation industry. Consequently, the outcomes of the ARDL further confirm that human capital can reduce EF, as energy consumption affects environmental deterioration [ 39 ].

In conclusion, no universally consistent nexus exist among variables, as already supported by the evidence of the EKC presented on the graph of the inverted U-shaped function, which is inconclusive. The findings have been subject to regional and national specifics, namely development path, population size and quality, economic structure, natural endowments, trade policy, and capacity of functioning institutions, as further envisaged in the empirical studies of Onafowora and Owoye [ 77 ] and Dijkgraaf and Vollebergh [ 78 ]. It can be seen that only few studies consider the presence of energy intensity in their investigative analyses, and far less considered the mixed relationship of transport energy consumption with energy intensity, that has been demonstrated in this research. As the South African transportation sector’s rising GHG or CO 2 - eq emissions becomes the focus of the present study, there exists a correlation between energy consumption, energy intensity, and economic growth. Based on previous research using similar approaches [ 4 , 6 , 20 ] the present research studies make efforts to literature by further widening the analysis of the correlations among the economic variables of impacts (–EC–EI–PV–GDP—CO 2 - eq emissions) taken from the transportation industry of South Africa as a reference case with the employment of datasets from 2016 to 2020.

3 Data and method

The 5-year dataset utilized as the parametric variables in this investigation was obtained from [ 6 ] for the nine provinces of South Africa between 2016 and 2020. The variables include the following:

Carbon emissions as per capita greenhouse gas (GHG) emissions, taken in tones of CO 2 equivalent.

Per capita gross domestic product (GDP), taken in South Africa Rand.

Per capita gross energy (oil) consumption (EC) taken in tons of oil equivalent.

Per capita energy intensity (EI), taken in Tce per 10,000 Rand.

Number of on-road passenger vehicles (PV) contributing to carbon emissions.

Taking into account the fact that the data for each province is different by characteristics in terms of population, energy use, and economic growth, it is observed that using variable per capita values will lead to significant results. To conduct these precepts, as in the case of South Africa, the nominal indices are operated over the population numbers. The employed datasets are presented in panel:

The panel dataset provides the values for the driving forces under investigation, namely EC, EI, GDP, and CO 2 - eq emissions, for the nine provinces of South Africa.

Time series data provide parametric values for each of the variables from the time period of 2016 to 2020 for each of the nine provinces of South Africa.

The data series were set up using a panel design. Data for the 2021–2023 timeframe are yet to be drawn to fit the investigation for public purposes.

In the current research analysis of EKC, three different types of empirical specifications are generally considered: (i) linear specifications, (ii) quadratic (inverted-U) specifications, and (iii) cubic (N-shaped) or sideways-mirrored (S-shaped) specifications [ 77 ]. The graph in Fig.  1 as shown illustrates the Environmental Kuznet Curve concepts and perspectives as demonstrated by the authors. This posits a correlation between the indicators of environmental degradation and economic development. It also suggests that during the early stages of industrialization and the absence of knowledge and circular economy, GHG emissions increase as environmental quality decreases. However, beyond a certain level of economic development, which varies based on different indicators, the trend reverses, with high economic growth and the inclusion of circular economy resulting in environmental improvement.

Enviromental energy-econometrics analysis of EKC hypothesis [Author’s design]

There are broad functional forms that possess additional pertinent factors, namely, external variables of time, provincial characteristics, and technical factors. The general form of the equation is as follows:

In accordance with the EKC specifications provided above, this study examines CO 2 - eq emissions (Q) as the dependent variable, per capita yearly GDP (Y) as the independent variable, time period (t) as a factor, and the explanatory variables (X). Furthermore, ɛ represents the random error component, and a i denotes the coefficients of the model, which can also be referred to as the marginal propensity for emissions. Upon conducting the EKC analyses for the three specifications, several technical details can be discerned:

IF ( →) a 1  >  0— linearity of correlation around GDP with CO 2 - eq emissions. [a 1 must be significant]

IF ( ↔)  a 1  <  0— monotonic decrease linkage around GDP and CO 2 eq  emissions. [a 1  must be significant]

IF a 1  >  0 , a 2  <  0 & a 3  =  0— quadratic linkage around GDP and CO 2 - eq  emissions. [Equilibria to be reached]

This is to evaluate the existence of an EKC-type nexus between CO 2 - eq emissions, economic growth, and the impact of energy consumption on CO 2 - eq emissions in the transportation industry, employing two energy-econometrics’ models as the basis for further analyses. To measure the environmental impacts, we use CO 2 - eq emissions as a dependent variable, while GDP, and EC, EI and PV are taken as the independent, controlling independent variables, respectively as the case applies.

3.2.1 Model 1

Taken as the first model, we employ quadratic to perform test on the EKC hypothesis as follow:

where Q corresponds to CO 2 - eq emissions, Y is the GDP per capita, X 1 … X n are the covariate explanatory variables. The ɛ it represents the error term, i denotes the provinces of South Africa while t is the time period. Other studies have employed similar approach, however, with different explanatory driving factors [ 78 ].

3.2.2 Model 2

Using the cubic equation, we applied the second model to conduct tests on the N-Shape hypothesis for the Kuznets curve as demonstrated:

The order of representations are as specified in Eq. ( 1 ). There are other researchers who employed a cubic model similar to the one utilized in this study due to their close proximity [ 48 ]. The parametric variables are taken in their logarithmic transform. The sign for coefficients of Y , Y 2 , Y 3  applied to economic growth and the specific correlations among them regulate the shape of the approximating surface.

We employed the ARDL bounds testing approach to reconfirm the presence of EKC and cointegration of variables as proposed by [ 79 ]. Eq. ( 5 ) fully remodeled in Eq. ( 6 ) from [ 48 ] background as:

where Δ denotes variable’s first difference operator, P stands for lag lengths. To use ARDL we first demonstrate cointegration among the variables. To proceed, the null hypothesis test of no cointegration is conducted against the alternative hypothesis in this other of format:

F-statistic is inculcated with respect to the series being integrated either at I(0) or I(1). As the case applies, if the F-statistic value is greater than the upper bound value, there exists cointegration among the variables. If the F-statistic value is below the crucial lower bound value, the acceptance of null hypothesis that there is no cointegration is observed, as no precision will be made following that F-statistic lies between upper and the lower bound values [ 52 ]. For the study’s validation, the critical and F-statistic values are selected by applying cointegration technique as put forward by [ 52 ]. The estimates of the short run coefficients are obtained by ( P ) whilst the long run dynamics are estimated with the coefficients \(\vartheta_{1} ,\vartheta_{2} ,\vartheta_{3} ,\vartheta_{4} ,\vartheta_{5} ,\vartheta_{6}\) as expressed in Eq. ( 6 ). The ARDL bound testing approach is an effective method for simultaneously determining better estimates of both short-run and long-run dynamics. It achieves this through a modest linear transformation, which provides a superior approach for obtaining more accurate estimates. To assess the robustness entirety of the empirical models, diagnostic tests on heteroskedasticity, normality and autocorrelation tests are conducted, thereby running the validity and consistency of the long run dynamics. This is carried using canonical cointegration regression, dynamic ordinary least square (DOLS), and modified least square (FMOLS).

Modeling the data to be analyzed [ 6 ], and the time span of 5 years in real terms along with their provincial locations are illustrated in Fig.  2 a, and b respectively as shown:

a South Africa’s driving forces impacts on GHG/CO 2 - eq emissions in transportation industry, 2016–2020. b. South Africa’s driving forces impacts on GHG / CO 2 - eq emissions in transportation industry, 2016–2020

4 Empirical results and analysis

The study investigates how economic growth in GDP per capita and its extensions, CO 2 - eq emissions, energy consumption, its yielding energy intensity and the on-road passenger vehicles cointegrate to bring forth the observable environmental impacts in the transportation industry of South Africa. We applied ARDL bound testing method in achieving this and to also prevent spurious regression. It is essential to examine the order of integration prior to ARDL bound testing method. Augmented Dickey-Fuller (ADF) and Philips Pearson (PP) tests are employed in attaining the consequential values in order to proceed. The findings of both ADF and PP indicate that none of the series is stationary at Level, as illustrated in Table  1 . Hence, the hypothesis of no stationary is rejected, as it implies that all the variables are integrated at first difference. The results further show that none of the variables is integrated at I(2). By the revealing response, the ARDL bounding technique is found appropriate.

When it has been demonstrated that none of the variables are integrated in the order I(2), the cointegration between them is further evaluated. The decision is prerequisite prior assessments of parametric variables for cointegration. To begin with, unrestricted VAR models are utilized and to subsequently identify the optimal lag length of 2 using SIC criterion. The optimal lag length of 2 adjustments is imperative at the selection of the optimal length. Thereafter, proceeding to find adjustments from the parametric variables. In confirming the cointegration, Wald test is applied to determine the value of F-statistic. The findings of Table  2 reveal the rejection of null hypothesis on the condition that no cointegration on the modeled equations.

Johansen cointegration test is employed to verify the validity of the F-statistic as generated by Wald test performance. In conducting Johansen cointegration, Trace statistics with Eigen-values were obtained. The relevance of Trace statistics and that of Eigen-values demonstrates the cointegration correlation among the investigated parametric variables. These are as presented in Table  3 in which the analyses made it evident that at the very least, cointegration correlations exist. Hence, Johansen cointegration results validate Wald statistics. Both long run and short run estimates for Eq. ( 6 ) were conducted to determine the level of significance of the exogenous variable of CO 2 - eq emissions and the underlying independent variables. In Table  4 , it can be observed that all the coefficients possess the responsive signs. More so, all the series are made significant at 0.05% level. In other words, the indication of GDP positive-path coefficient buttress that CO 2 - eq emissions in the transportation industry surfaces with increasing economic growth in both forecasts for long run and the short run.

In contrast as revealed in Table  4 , there are strong indications of long run and short run correlations among economic growth in square and cubic forecast with CO 2 - eq emissions found possessing negative sign coefficients. The implication as derived, implies that CO 2 - eq emissions in South Africa’s transportation industry increases at the early industrial phase of economic growth and fall after reaching certain level of economic expansion. The investigation validates the U-shaped EKC hypothesis in South Africa relating to transportation sector. The findings are related to [ 42 ] who conducted such line of analyses to confirm the existence of EKC in Italy, and in Turkey by [ 80 ], more so, in OECD countries [ 13 , 16 , 17 ].

With the peculiar case of South Africa transportation industry in the staggering amount of energy (oil) consumed, the on-road passenger vehicles, energy in oil consumption with its intensity are integrated in the model. This offers new directions to mitigate carbon emissions on the level at which South Africa’s economic development has reached from the general interpretation for the U-shaped EKC hypothesis, a level being depicted in Fig.  3 . It is observed that from the level at which energy are used in the transportation sector of South Africa, Energy consumption as inspected with its intensity contributes to the emissions of GHGs/CO 2 - eq  in South Africa. On the other hand, although in the long run passenger vehicles do not reveal a negative impact, however, in the scale of short run it possesses a sensitive negative impact. The demonstrated energy-econometrics analysis implies that increasing passenger vehicles (IC Engines) concurrently lead to increase in energy (oil) consumption vis-à-vis energy intensity.

Plot-trends correlations between economic growth and CO 2 - eq emissions in SA transport

The findings of the study relate to that of Zhao et al. [ 67 ] with a similar outcomes for China. Based on the research conducted, it was discovered that in numerous instances, public transportation alternatives are more eco-friendly for South Africa's transportation systems than the high volume of passenger vehicle traffic, which was found to be one of the primary contributors to CO 2 - eq emissions in the transportation industry.

This is proved viable as South Africa heavily relies on conventional (fossil) energy sources such as oil and coal, particularly oil (in fossil) for its transportation activities. Already, well over 90% of the energy use in the transportation industry is fossil-based fuels.

To check the capacity of the analyses, the resulting model of Eq. ( 6 ) is assessed by employing three different techniques, namely, fully modified least squares (FMOLS), dynamic least squares (DOLS), and canonical cointegration regression (CCR), purposed to examine the validity and reliability of the obtained outcomes through the ARDL bound test approaches [ 80 ]. The findings of Table  5 indicate that whilst economic growth possess a positive and significant impact on CO 2 - eq emissions, the square and cubic of economic growth ( GDP 2 and GDP 3 ) have negative significant impacts. This is the implied case applied to the transportation industry of South Africa. In addition, from the analytical interpretations being sensitive of the GDP flow-line, it can be rewarding to improve eco-friendly environment. Ultimately, the results of the ARDL bound test approach applied under three distinct techniques support the findings of the research which are further presented in Table  5 .

The CUSUM and CUSUMsq are performed with high sensitivity to verify the lack of structural invariance, endogeneity tests, and the reliability and stability of the models for both long and short run estimations. The results are graphically presented in Fig.  4 a and Fig.  4 b. The assessed stability diagnostics for both tests largely reside between the critical (red) lines; this implies that the model can be put forward for policy recommendations with respect to the availability of the data employed. They are found fit. It is noteworthy that, based on the most current literature, this research is considered to be forthcoming in South Africa and the continent of Africa. In Fig.  4 b, it can be observed that the data as they were not readily available from a single source of a database.

a Trend-plot for cummulative sum of recursive residual at critical bound of 5% significance. b  Trend-plot for cummulative square of recursive residual at critical bound of 5% significance

The readings may not be efficient and robust enough, however optimum determination has been exercised. Figure  4 b can only further implies that at the readings of 5% level of significance CUSUMsq for high sensitivity can be determined (as it also travels between red-lines indicator) with respect the prevailing empirical analysis.

As presented in Fig.  5 a, both the short-run and long-run impacts are identified. The parametric variables taken into consideration are contingent on the interpretations of EKC hypothesis in the derived models of energy econometrics technique for the South Africa's transportation sector. This serves as the underlying approach of the research studies. The interpretation of the readings depicts that for both long and short runs, indications exist for the correlation between economic growth in GDP per capita ( A region) and GHG emissions ( E ) of the transportation industry. As Energy Intensity (in the C” region) has to bridge the gap, there also exists a causal relationship between Energy Consumption—EC in the B’ region and Passenger Vehicles—PV in the D region. Along the axis of A-D-E in the composition of GDP, PV and GHGs there exist an indication of causal interconnection between the variables as evidently provided in empirical analyses of the 5-year employed dataset of transportation industry of South Africa.

a Acyclic model indicator of parametric variables-flow on EKC hypothetical analysis. b Directional linkages of resource-controls among selected driving forces over GHG/CO- eq emissions

Figure  5 b, as indicated, conveys the relational linkages of variables’ controls that exist among the selected driving forces impacting on GHG emissions in the transportation sector of South Africa. In line with the analyses performed on the hypothetical EKC, it can be deduced that the outcomes of GHG emissions are well dependent on variable’s computational inputs both quantitatively and qualitatively. As investigated for all the nine provinces of South Africa with time period of five years spanning from 2016 to 2020. The alterations and the adjustments of one or two or more of the endogenous variables can significantly lead to the required environmental outcomes.

5 Conclusion and policy implication

5.1 policy implications for reducing ghg emissions in transportation.

The study delves into the concept of energy econometrics complexity and applies the Environmental Kuznets Curve hypothesis, commonly utilized to analyze the nexus between economic development and environmental quality. South Africa is still a developing nation, despite being more developed of Africa's member states. In line with its Paris Agreement obligations, South Africa has been determined to further reduce its transportation sector GHG emissions from the 10-year of 60MtCO 2 - eq . This value serves as the share of the tranportation industry from the South Africa’s overall contribution of 1.2% of the world’s GHG emissions, totaling 8.08 billion metric tons in CO 2 equivalent, globally. Identifying the pattern of the Environmental Kuznets Curve (EKC) hypothesis as it pertains to economic development and environmental degradation for the requisite air quality is essential for assessing the impacts of the driving forces in the industry that contribute to carbon emissions.

Provinces in South Africa should be cognizant of their respective stages of economic development, energy use, and GHG emissions, particularly that of transportation sector. They should make targeted advances in economic development while effectively mitigating GHG emissions. At present, all the provinces are in the rising stage and have not yielded to the turning point of the interpreted EKC. The rising economic development due to carbon intensive energy is the primary reason for the accounted carbon emissions in the industry. Although, significant environmental degradation has been recorded, the nation still requires a lot of transportation systems to move people, goods, and services, nevertheless, those with sustainable air quality. According to the analysis of the development trend of transportation in various cities and provinces of South Africa at this stage, there are still problems of energy sources for both renewable and nonrenewable, and that of transportation means and modes. This, obviously lack transportation’s economic development objevtives. By updating the economic structure and controlling the development of transportation systems reasonably, a high developed economy with low carbon emissions can be achieved.

With a measure of controlling unbearable population and decreasing mortality rates while improving technological innovations, the effect of passenger vehicles on traffic emissions can be restrained. More to this effect, passengers can be guided on individual and personal benefits of choosing clean and green travel options. The transportation pricing index has the potential to be a significant factor in reducing carbon emissions. Altering consumption approach can be a viable strategy for achieving balance between the economy and the environment that will lead to sustainable development.Furthermore, the government should promote and incentivize the use of environmentally-friendly modes of transportation and the use of clean products among local residents. Modifying the cost of transportation services is an essential measure that can influence people's travel choices and subsequently impact the energy demand and carbon emissions in the transportation industry. Efforts should be made to enhance the affordability and convenience of public transportation in order to change people's preconceived notions about travel. Highway transportation is among the passenger travel that should be considered, particularly intercity and city buses, which mostly relies on fossil fuels for passenger transit. By so doing, the use of energy for automobile will predominantly shift towards natural gas and electricity. The government has the ability to diminish individuals' reliance on gasoline-powered vehicles by implementing tax policies, fuel surtaxes, and vehicle purchase taxes that are tailored to particular vehicle types. Furthermore, providing policy assistance for environmentally friendly automobile manufacturers to foster the growth of automobile industry that is clean. Offering of incentives and benefits to consumers who purchase such automobiles, can encourage individuals who use private cars to transition to a more environmentally friendly mode of transportation with reduced carbon emissions. To encourage long-distance of on-road travel that is environmentally friendly, it is important to build gas stations and charging infrastructures along the highway. This will gradually shift people's transportation practices and promote the development of low-carbon traffic in South Africa transportation sector and elsewhere.

It is important to note that there are limitations and gaps in the research. This study investigates the nexus between economic development and environmental degradation, specifically focusing on the income-emissions aspect of the EKC hypothesis in the transportation industry of South Africa. Due to limitations in data and geographic scope, our analysis is restricted to the nine provinces of South Africa over a five-year period from 2016 to 2020. Additional research investigations have the potential to broaden the temporal scope and increase the number of countries examined. Moreover, it has the capability to examine many sectors or industries both independently, and as integrated concerns, resulting in changes to the methods, and scale of the tests and diagnostics, which will ultimately lead to more outcomes.

5.2 Conclusion

Using energy econometrics techniques, this study investigates the effects of economic growth (GDP per capita), with it being squared and cubic, followed by energy consumption (EC), energy intensity (EI), and on-road passenger vehicles (PV) on the mitigation of GHG emissions in CO 2 equivalence for the transportation industry of South Africa. A five-year dataset spanning from 2016 to 2020, as it appears in Fig.  2 a, and b are adopted. The year can further be extended, only to portray an extension for subsequent forecasts. The study examined South Africa's nine provinces, considering their varying rates of economic development and dependence on fossil fuels for energy in the sector across all the provinces.

From the study period of 2016 to 2020 as 2021 only being the model’s extension forecast, South Africa’s per capita GDP ranges from R71, 920.00 to 101,659.00. The country’s energy (in oil) consumption (EC) from 2016 to 2020 is estimated ranging from 6,925,070,093 to 7,799,172,128 L and in conversion it tallies between 39.850 to 41.039 metric tons of oil consumption in energy content. The energy intensity (EI) for the study periods is within the range of 513 Tce per R10, 000.00 to 537 Tce per R10, 000.00 from 2016 to 2020 as estimated. South Africa’s on-road passenger vehicles for the research period of 2016 to 2020 are taken in units of vehicle population within the range of 11,964,234 and 12,701,630 of vehicle units. Considering the energy-econometrics debates around the EKC hypothesis sectioned into four main categories, namely; cointegration of the parametric variables, endogeneity concerns, simultaneity and omission bias for variables, the prevailing econometrics instruments are employed in the peculiar case of South Africa’s transportation industry.

In the context of South Africa, economic growth in GDP (inculcating GDP 2 and GDP 3 ), energy consumption with its intensity, and on-road passenger vehicles are modeled on CO 2 - eq emissions. In the investigation, the EKC test is employed to South Africa’s industry using the aforementioned variables as explanatory while taking CO 2 - eq emissions as the dependent variables. With the outcome of the prevailing research, CO 2 - eq emissions in South Africa’s transportation industry grew through the early phases of its economic expansion at specific level of economic threshold. To clarify the research main contribution, the study further demonstrates the directional nexus among South Africa’s on-road PV, EC, EI and per capita GDP with its excesses which is negatively probable, especially the economic expansion by a cubic scenario. The ARDL bound test approach was employed to analyze the cointegration correlation among the parametric variables. For the high performance of the model, three high-powered techniques were employed to examine the accuracy and reliability of the results from the ARDL bound test approach: FMOLS, DOLS and CCR, respectively.

Quantitatively, the series were determined to be stationary at their first differences, as indicated by R 2 and R 2 ( Adjusted ) values of 0.9837 and 0.9827 , respectively, for both long-run and short-run estimations. From the deductions of the findings, it is imperative to monitor the reactions of EC on the long effects of the t-stat —( 0.393 ) and p-value —( 0.6947 ) alongside the short run forecast impact of the t-stat —( 0.383 ) and p-value —( 0.7019 ). From the short run effects shown in the t-stat—( 4.928 ) with p-value ( 0.0000 ) to the long run effects demonstrated with p-value ( 0.0000 ), the per capita GDP increases, indicating its improper influence in the sector. Limiting the burning of fossil fuels is essential as shown by the negative signal of EI for the short run impacts of t-stat ( -1.100 ) with p-value ( 0.0000 ) and the long- run impacts shown of t-stat ( -1.076 ) with p-value ( 0.2835 ).

From these analyses, the following conclusions have been drawn:

There are implications of Environmental Kuznets curve (EKC) hypothesis in the significance of economic growth, energy consumption with its intensity, and on-road passenger vehicles in the transportation industry of South Africa.

Economic growth has a significant positive impact over GHG/CO 2 - eq emissions provided that it is checked without spanning out of control.

In both the long and short run paths, energy intensity can have significant positive impacts in South Africa.

Under proper investigation, the neutrality hypothesis is confirmed, as a correlation exist between CO 2 - eq emissions and economic growth which at large contribute to economic development.

There is also evidence of proportional nexus between the energy consumption and passenger vehicles with CO 2 - eq emissions in the transportation industry of South Africa.

In line with the outcomes of the research studies, it can be put forward for decision making, that there are convincing revelations between per capita economic growth and energy (oil) consumption that led to CO 2 - eq emissions. Automobiles that are IC-Engines running on fossil fuels should be minimized in order to contribute to the efforts of mitigating the impacts of climate change. By doing so, the mass transit can be cushioned. In addition, South Africa’s GHGs intensity can be mitigated by further enhancing renewables in the energy mix. To further support an eco-friendly environment, decision and policy makers should support alternative energy transport vehicles to limit the consumption of fossils.

Based on the accounts of this study, the following implied  knowledge among others are derived:

First, South Africa can further restructure the transportation industry to develop in a more sustainable ways, as its impacts on the environment are significantly dominant. Similarly, developing countries as a case with South Africa can focus on how their transportation systems and economic development affect environmental degradation to fully achieve intergovernmental sustainability goals, such as the ones outlined by the United Nations. For instance, that of the sustainable development goals. Consequently, this can further align South Africa's policies framing with those that are highly developed.

Furthermore, in the era of information age, the structure of the economy can be enhanced to move from carbon intensive energy to knowledge and circular economies. Notwithstanding their complexities, they are reliable path to post-industrial economy. Passenger vehicles contribute significantly to South Africa's total vehicle fleet GHG emissions. However, with rigorous fuel economy standards and increasing use of hybrid and electric vehicles, this share can be expected to decline over time as indicated by the EKC. To achieve sustainable development, it is imperative that governmental bodies prioritize policies targeting commercial vehicles, with particular emphasis on passenger on-road vehicles, in domains such as fuel economy regulations and electric vehicle (EV) deployment. Incentive-based regulations for hybrid and EV passenger vehicles can facilitate the production of cleaner energy and promote sustainable development.

Data availability

The data used to support this research is included within the manuscript. However, upon request, additional sources that involve analyses can be provided.

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OJO: Conceptualization, methodology, software application, validation, formal analysis, investigation, data resources, data curation, writing—original draft preparation, writing—review and editing, OJO, OAO and CKM Lee: visualization, project administration. OAO and CKM Lee: supervision. OAO and CKM Lee: internal funding acquisition.

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Oladunni, O.J., Olanrewaju, O.A. & Lee, C.K.M. The Environmental Kuznets Curve (EKC) Hypothesis on GHG emissions: analyses for transportation industry of South Africa. Discov Sustain 5 , 302 (2024). https://doi.org/10.1007/s43621-024-00518-6

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• Null and Alternative Hypotheses | Definitions & Examples

Null & Alternative Hypotheses | Definitions, Templates & Examples

Published on May 6, 2022 by Shaun Turney . Revised on June 22, 2023.

The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test :

• Null hypothesis ( H 0 ): There’s no effect in the population .
• Alternative hypothesis ( H a or H 1 ) : There’s an effect in the population.

Table of contents

Answering your research question with hypotheses, what is a null hypothesis, what is an alternative hypothesis, similarities and differences between null and alternative hypotheses, how to write null and alternative hypotheses, other interesting articles, frequently asked questions.

The null and alternative hypotheses offer competing answers to your research question . When the research question asks “Does the independent variable affect the dependent variable?”:

• The null hypothesis ( H 0 ) answers “No, there’s no effect in the population.”
• The alternative hypothesis ( H a ) answers “Yes, there is an effect in the population.”

The null and alternative are always claims about the population. That’s because the goal of hypothesis testing is to make inferences about a population based on a sample . Often, we infer whether there’s an effect in the population by looking at differences between groups or relationships between variables in the sample. It’s critical for your research to write strong hypotheses .

You can use a statistical test to decide whether the evidence favors the null or alternative hypothesis. Each type of statistical test comes with a specific way of phrasing the null and alternative hypothesis. However, the hypotheses can also be phrased in a general way that applies to any test.

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The null hypothesis is the claim that there’s no effect in the population.

If the sample provides enough evidence against the claim that there’s no effect in the population ( p ≤ α), then we can reject the null hypothesis . Otherwise, we fail to reject the null hypothesis.

Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept . Be careful not to say you “prove” or “accept” the null hypothesis.

Null hypotheses often include phrases such as “no effect,” “no difference,” or “no relationship.” When written in mathematical terms, they always include an equality (usually =, but sometimes ≥ or ≤).

You can never know with complete certainty whether there is an effect in the population. Some percentage of the time, your inference about the population will be incorrect. When you incorrectly reject the null hypothesis, it’s called a type I error . When you incorrectly fail to reject it, it’s a type II error.

Examples of null hypotheses

The table below gives examples of research questions and null hypotheses. There’s always more than one way to answer a research question, but these null hypotheses can help you get started.

*Note that some researchers prefer to always write the null hypothesis in terms of “no effect” and “=”. It would be fine to say that daily meditation has no effect on the incidence of depression and p 1 = p 2 .

The alternative hypothesis ( H a ) is the other answer to your research question . It claims that there’s an effect in the population.

Often, your alternative hypothesis is the same as your research hypothesis. In other words, it’s the claim that you expect or hope will be true.

The alternative hypothesis is the complement to the null hypothesis. Null and alternative hypotheses are exhaustive, meaning that together they cover every possible outcome. They are also mutually exclusive, meaning that only one can be true at a time.

Alternative hypotheses often include phrases such as “an effect,” “a difference,” or “a relationship.” When alternative hypotheses are written in mathematical terms, they always include an inequality (usually ≠, but sometimes < or >). As with null hypotheses, there are many acceptable ways to phrase an alternative hypothesis.

Examples of alternative hypotheses

The table below gives examples of research questions and alternative hypotheses to help you get started with formulating your own.

Null and alternative hypotheses are similar in some ways:

• They’re both answers to the research question.
• They both make claims about the population.
• They’re both evaluated by statistical tests.

However, there are important differences between the two types of hypotheses, summarized in the following table.

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To help you write your hypotheses, you can use the template sentences below. If you know which statistical test you’re going to use, you can use the test-specific template sentences. Otherwise, you can use the general template sentences.

General template sentences

The only thing you need to know to use these general template sentences are your dependent and independent variables. To write your research question, null hypothesis, and alternative hypothesis, fill in the following sentences with your variables:

Does independent variable affect dependent variable ?

• Null hypothesis ( H 0 ): Independent variable does not affect dependent variable.
• Alternative hypothesis ( H a ): Independent variable affects dependent variable.

Test-specific template sentences

Once you know the statistical test you’ll be using, you can write your hypotheses in a more precise and mathematical way specific to the test you chose. The table below provides template sentences for common statistical tests.

Note: The template sentences above assume that you’re performing one-tailed tests . One-tailed tests are appropriate for most studies.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (“ x affects y because …”).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses . In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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Turney, S. (2023, June 22). Null & Alternative Hypotheses | Definitions, Templates & Examples. Scribbr. Retrieved September 27, 2024, from https://www.scribbr.com/statistics/null-and-alternative-hypotheses/

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