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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."

How to Write a Hypothesis [31 Tips + Examples]

Writing hypotheses can seem tricky, but it’s essential for a solid scientific inquiry.

Here is a quick summary of how to write a hypothesis:

Write a hypothesis by clearly defining your research question, identifying independent and dependent variables, formulating a measurable prediction, and ensuring it can be tested through experimentation. Include an “if…then” statement for clarity.

I’ve crafted dozens in my research, from basic biology experiments to business marketing strategies.

Let me walk you through how to write a solid hypothesis, step by step.

Writing a Hypothesis: The Basics

Table of Contents

A hypothesis is a statement predicting the relationship between variables based on observations and existing knowledge. To craft a good hypothesis:

• Identify variables – Determine the independent and dependent variables involved.
• Predict relationships – Predict the interaction between these variables.
• Test the statement – Ensure the hypothesis is testable and falsifiable.

A solid hypothesis guides your research and sets the foundation for your experiment.

31 Tips for Writing a Hypothesis

There are at least 31 tips to write a good hypothesis.

Keep reading to learn every tip plus three examples to make sure that you can instantly apply it to your writing.

Tip 1: Start with a Clear Research Question

A clear research question ensures your hypothesis is targeted.

• Identify the broad topic you’re curious about, then refine it to a specific question.
• Use guiding questions like “What impact does variable X have on variable Y?”
• How does fertilizer affect plant growth?
• Does social media influence mental health in teens?
• Can personalized ads increase customer engagement?

Tip 2: Do Background Research

Research helps you understand current knowledge and any existing gaps.

• Review scholarly articles, reputable websites, and textbooks.
• Focus on understanding the relationships between variables in existing research.
• Academic journals like ScienceDirect or JSTOR.
• Google Scholar.
• Reputable news articles.

Tip 3: Identify Independent and Dependent Variables

The independent variable is what you change or control. The dependent variable is what you measure.

• Clearly define these variables to make your hypothesis precise.
• Think of different factors that could be influencing your dependent variable.
• Type of fertilizer (independent) and plant growth (dependent).
• Amount of screen time (independent) and anxiety levels (dependent).
• Marketing strategies (independent) and customer engagement (dependent).

Tip 4: Make Your Hypothesis Testable

A hypothesis must be measurable and falsifiable.

• Ensure your hypothesis can be supported or refuted through data collection.
• Include numerical variables or qualitative changes to ensure measurability.
• “Increasing screen time will increase anxiety levels in teenagers.”
• “Using fertilizer X will yield higher crop productivity.”
• “A/B testing marketing strategies will show higher engagement with personalized ads.”

Tip 5: Be Specific and Concise

Keep your hypothesis straightforward and to the point.

• Avoid vague terms that could mislead or cause confusion.
• Clearly outline what you’re measuring and how the variables interact.
• “Replacing chemical fertilizers with organic ones will result in slower plant growth.”
• “A social media break will decrease anxiety in high school students.”
• “Ads targeting user preferences will boost click-through rates by 10%.”

Tip 6: Choose Simple Language

Use simple, understandable language to ensure clarity.

• Avoid jargon and overly complex terms that could confuse readers.
• Make the hypothesis comprehensible to non-experts in the field.
• “Organic fertilizer will reduce plant growth.”
• “High schoolers will feel less anxious after a social media detox.”
• “Targeted ads will increase customer engagement.”

Tip 7: Formulate a Null Hypothesis

A null hypothesis assumes no relationship between variables.

• Create a counterpoint to your main hypothesis, asserting that there is no effect.
• This allows you to compare results directly and identify statistical significance.
• “Fertilizer type will not affect plant growth.”
• “Social media use will not influence anxiety.”
• “Targeted ads will not affect customer engagement.”

Tip 8: State Alternative Hypotheses

Provide alternative hypotheses to explore other plausible relationships.

• They offer a contingency plan if your primary hypothesis is not supported.
• These should still align with your research question and measurable variables.
• “Fertilizer X will only affect plant growth if used in specific soil types.”
• “Social media might impact anxiety only in certain age groups.”
• “Customer engagement might only improve with highly personalized ads.”

Tip 9: Use “If…Then” Statements

“If…then” statements simplify the cause-and-effect structure.

• The “if” clause identifies the independent variable, while “then” identifies the dependent.
• It makes your hypothesis easier to understand and directly testable.
• “If plants receive organic fertilizer, then their growth rate will slow.”
• “If teens stop using social media, then their anxiety will decrease.”
• “If ads are personalized, then click-through rates will increase.”

Tip 10: Avoid Assumptions

Don’t assume the audience understands your variables or relationships.

• Clearly define terms and relationships to avoid misinterpretation.
• Provide background context where necessary for clarity.
• Define “anxiety” as a feeling of worry or unease.
• Specify “plant growth” as the height and health of plants.
• Describe “personalized ads” as ads matching user preferences.

Tip 11: Review Existing Literature

Previous research offers insights into forming a hypothesis.

• Conduct a thorough literature review to identify trends and gaps.
• Use these studies to refine and build upon your hypothesis.
• Studies showing a link between screen time and anxiety.
• Research on organic versus chemical fertilizers.
• Customer behavior analysis in different marketing channels.

Tip 12: Consider Multiple Variables

Hypotheses with multiple variables can offer deeper insights.

• Explore combinations of independent and dependent variables to see their relationships.
• Plan experiments accordingly to distinguish separate effects.
• Studying fertilizer type and soil composition effects on plant growth.
• Testing social media use frequency and content type on anxiety.
• Analyzing marketing strategies combined with product preferences.

Tip 13: Review Ethical Considerations

Ethics are essential for trustworthy research.

• Avoid hypotheses that could cause harm to participants or the environment.
• Seek approval from relevant ethical boards or committees.
• Avoiding experiments causing undue stress to teenagers.
• Preventing chemical contamination when testing fertilizers.
• Respecting privacy with personalized ads.

Tip 14: Test with Pilot Studies

Small-scale pilot studies test feasibility and refine hypotheses.

• Use them to identify potential issues and adjust before full-scale research.
• Ensure pilot tests align with ethical standards.
• Testing different fertilizer types on small plant samples.
• Trying brief social media breaks with a small group of teens.
• Conducting A/B tests on ad personalization with a subset of customers.

Tip 15: Build Hypotheses on Existing Theories

Existing theories provide strong foundations.

• Use established frameworks to develop or refine your hypothesis.
• Testing theoretical predictions can yield meaningful data.
• Applying agricultural theories on soil and crop management.
• Using psychology theories on screen addiction and mental health.
• Referencing marketing theories like consumer behavior analysis.

Tip 16: Address Real-World Problems

Solve real-world problems through practical hypotheses.

• Make sure your research question has relevant, impactful applications.
• Focus on everyday challenges where actionable insights can help.
• Testing new eco-friendly farming methods.
• Reducing anxiety by improving digital wellbeing.
• Improving marketing ROI with personalized strategies.

Tip 17: Aim for Clear, Measurable Outcomes

The results should be easy to measure and interpret.

• Quantify your dependent variable or use defined qualitative measures.
• Avoid overly broad or ambiguous outcomes.
• Measuring plant growth as a percentage change in height.
• Quantifying anxiety levels through standard surveys.
• Tracking click-through rates as a percentage of total views.

Tip 18: Stay Open to Unexpected Results

Not all hypotheses yield expected results.

• Be open to learning new insights, even if they contradict your prediction.
• Unexpected findings often reveal unique, significant knowledge.
• Unexpected fertilizer types boosting growth differently than anticipated.
• Screen time affecting anxiety differently across various age groups.
• Targeted ads backfiring with specific customer segments.

Tip 19: Keep Hypotheses Relevant

Ensure your hypothesis aligns with the purpose of your research.

• Avoid straying from the original question or focusing on tangential issues.
• Stick to the research scope to ensure accurate and meaningful data.
• Focus on a specific type of fertilizer for plant growth.
• Restrict studies to relevant age groups for anxiety research.
• Keep marketing hypotheses within the same target customer segment.

Tip 20: Collaborate with Peers

Collaboration strengthens hypothesis development.

• Work with colleagues or mentors for valuable feedback.
• Peer review helps identify flaws or assumptions in your hypothesis.
• Reviewing hypothesis clarity with a lab partner.
• Sharing research plans with a mentor to refine focus.
• Engaging in academic peer-review groups.

Tip 21: Re-evaluate Hypotheses Periodically

Revising hypotheses ensures relevance.

• Update based on new literature, data, or technological advances.
• A dynamic approach keeps your research current.
• Refining fertilizer studies with recent organic farming research.
• Adjusting social media hypotheses for new platforms like TikTok.
• Modifying marketing hypotheses based on changing customer preferences.

Tip 22: Develop Compelling Visuals

Illustrating hypotheses can help communicate relationships effectively.

• Use diagrams or flowcharts to show how variables interact visually.
• Infographics make it easier for others to grasp your research concept.
• A flowchart showing fertilizer effects on different plant growth stages.
• Diagrams illustrating social media use and its psychological impact.
• Infographics depicting how various marketing strategies boost engagement.

Tip 23: Refine Your Data Collection Plan

A solid data collection plan is vital for a testable hypothesis.

• Determine the best ways to measure your dependent variable.
• Ensure your data collection tools are reliable and accurate.
• Using a ruler and image analysis software to measure plant height.
• Designing standardized surveys to assess anxiety levels consistently.
• Setting up click-through tracking with analytics software.

Tip 24: Focus on Logical Progression

Ensure your hypothesis logically follows your research question.

• The relationship between variables should naturally flow from your observations.
• Avoid logical leaps that might confuse your reasoning.
• Predicting plant growth after observing effects of different fertilizers.
• Linking anxiety to social media use based on screen time studies.
• Connecting ad personalization with customer behavior data.

Tip 25: Test Against Diverse Samples

Testing across diverse samples ensures broader applicability.

• Avoid drawing conclusions from overly narrow sample groups.
• Try to include different demographics or subgroups in your testing.
• Testing fertilizer effects on multiple plant species.
• Including different age groups in anxiety research.
• Experimenting with personalized ads across varied customer segments.

Tip 26: Use Control Groups

Control groups provide a baseline for comparison.

• Compare your test group with a control group under unchanged conditions.
• This allows you to isolate the effect of your independent variable.
• Comparing plant growth with organic versus no fertilizer.
• Testing anxiety levels with and without social media breaks.
• Comparing personalized ads with general marketing content.

Tip 27: Consider Practical Constraints

Work within realistic constraints for your resources and timeline.

• Assess the feasibility of testing your hypothesis.
• Modify the hypothesis if the required testing is unmanageable.
• Reducing fertilizer types to a manageable number for testing.
• Shortening social media detox periods to realistic durations.
• Targeting only specific marketing strategies to optimize testing.

Tip 28: Recognize Bias Risks

Biases can skew hypothesis formation.

• Acknowledge your assumptions and how they may affect your research.
• Minimize biases by clearly defining and measuring variables.
• Avoiding assumptions that organic fertilizer is inherently better.
• Ensuring survey questions don’t lead to specific anxiety outcomes.
• Testing marketing strategies objectively without favoring any method.

Tip 29: Prepare for Peer Review

Peer review ensures your hypothesis holds up to scrutiny.

• Provide a clear rationale for why your hypothesis is sound.
• Address potential criticisms to strengthen your research.
• Showing your plant growth study builds on existing fertilizer research.
• Demonstrating social media anxiety links through data and literature.
• Supporting your marketing hypotheses with solid behavioral data.

Tip 30: Create a Research Proposal

A proposal outlines your hypothesis, methodology, and significance.

• It ensures your hypothesis is clear and your methods are well-thought-out.
• Proposals also help secure funding or institutional approval.
• A proposal for fertilizer studies linking plant growth and soil health.
• Research plans connecting social media habits to anxiety measures.
• Marketing proposals tying customer behavior to personalized advertising.

Tip 31: Document Your Findings

Recording findings helps validate or challenge your hypothesis.

• Document the methodology, data, and conclusions clearly.
• This allows others to verify, replicate, or expand on your work.
• Recording fertilizer effects on plant height in different soil types.
• Survey results linking social media use with anxiety levels.
• Click-through data proving personalized ads’ impact on engagement.

Check out this really good video about how to write a hypothesis:

Hypothesis Examples for Different Situations

Let’s look at some examples of how to write a hypothesis in different circumstances.

• Marketing Analysis : “If personalized ads are shown to our target demographic, then click-through rates will increase by at least 10%.”
• Process Improvement : “If automated workflows replace manual data entry, then task completion times will decrease by 20%.”
• Product Development : “If adding a chatbot feature to our app increases customer support efficiency, then user satisfaction will improve by 15%.”
• Biology Experiment : “If students grow plants with different fertilizers, then the organic fertilizer will result in slower growth compared to the chemical fertilizer.”
• Psychology Research : “If high school students take a break from social media, then their levels of anxiety will decrease.”
• Environmental Study : “If a controlled forest area is exposed to a certain pollutant, then the local plant species will show signs of damage within two weeks.”

Professional Contacts

• Medical Research : “If a novel treatment method is applied to patients with chronic illness, then their recovery rate will increase significantly compared to standard treatment.”
• Technology Research : “If machine learning algorithms analyze big data sets, then the accuracy of predictive models will surpass traditional data analysis.”
• Engineering Project : “If new composite materials replace standard components in bridge construction, then the resulting structure will be more durable.”

Super Personal

• Gardening Experiment : “If different types of compost are used in home gardens, then plants receiving homemade compost will yield the most produce.”
• Fitness Routine : “If consistent strength training is combined with a high-protein diet, then muscle mass will increase more than with diet alone.”
• Cooking Techniques : “If searing is added before baking, then the resulting roast will retain more moisture.”

Final Thoughts: How to Write a Hypothesis

Crafting hypotheses is both a science and an art. It’s about channeling curiosity into testable questions that propel meaningful discovery.

Each well-thought-out hypothesis is a stepping stone that could lead to the breakthrough you’ve been seeking.

Stay curious and let your research journey unfold.

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism, run a free check.

Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

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.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

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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How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

A research hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. Hypotheses are sometimes called “educated guesses”, but they are in fact (or let’s say they should be) based on previous observations, existing theories, scientific evidence, and logic. A research hypothesis is also not a prediction—rather, predictions are ( should be) based on clearly formulated hypotheses. For example, “We tested the hypothesis that KLF2 knockout mice would show deficiencies in heart development” is an assumption or prediction, not a hypothesis. 

The research hypothesis at the basis of this prediction is “the product of the KLF2 gene is involved in the development of the cardiovascular system in mice”—and this hypothesis is probably (hopefully) based on a clear observation, such as that mice with low levels of Kruppel-like factor 2 (which KLF2 codes for) seem to have heart problems. From this hypothesis, you can derive the idea that a mouse in which this particular gene does not function cannot develop a normal cardiovascular system, and then make the prediction that we started with. 

What is the difference between a hypothesis and a prediction?

You might think that these are very subtle differences, and you will certainly come across many publications that do not contain an actual hypothesis or do not make these distinctions correctly. But considering that the formulation and testing of hypotheses is an integral part of the scientific method, it is good to be aware of the concepts underlying this approach. The two hallmarks of a scientific hypothesis are falsifiability (an evaluation standard that was introduced by the philosopher of science Karl Popper in 1934) and testability —if you cannot use experiments or data to decide whether an idea is true or false, then it is not a hypothesis (or at least a very bad one).

So, in a nutshell, you (1) look at existing evidence/theories, (2) come up with a hypothesis, (3) make a prediction that allows you to (4) design an experiment or data analysis to test it, and (5) come to a conclusion. Of course, not all studies have hypotheses (there is also exploratory or hypothesis-generating research), and you do not necessarily have to state your hypothesis as such in your paper. 

But for the sake of understanding the principles of the scientific method, let’s first take a closer look at the different types of hypotheses that research articles refer to and then give you a step-by-step guide for how to formulate a strong hypothesis for your own paper.

Types of Research Hypotheses

Hypotheses can be simple , which means they describe the relationship between one single independent variable (the one you observe variations in or plan to manipulate) and one single dependent variable (the one you expect to be affected by the variations/manipulation). If there are more variables on either side, you are dealing with a complex hypothesis. You can also distinguish hypotheses according to the kind of relationship between the variables you are interested in (e.g., causal or associative ). But apart from these variations, we are usually interested in what is called the “alternative hypothesis” and, in contrast to that, the “null hypothesis”. If you think these two should be listed the other way round, then you are right, logically speaking—the alternative should surely come second. However, since this is the hypothesis we (as researchers) are usually interested in, let’s start from there.

Alternative Hypothesis

If you predict a relationship between two variables in your study, then the research hypothesis that you formulate to describe that relationship is your alternative hypothesis (usually H1 in statistical terms). The goal of your hypothesis testing is thus to demonstrate that there is sufficient evidence that supports the alternative hypothesis, rather than evidence for the possibility that there is no such relationship. The alternative hypothesis is usually the research hypothesis of a study and is based on the literature, previous observations, and widely known theories. 

Null Hypothesis

The hypothesis that describes the other possible outcome, that is, that your variables are not related, is the null hypothesis ( H0 ). Based on your findings, you choose between the two hypotheses—usually that means that if your prediction was correct, you reject the null hypothesis and accept the alternative. Make sure, however, that you are not getting lost at this step of the thinking process: If your prediction is that there will be no difference or change, then you are trying to find support for the null hypothesis and reject H1. 

Directional Hypothesis

While the null hypothesis is obviously “static”, the alternative hypothesis can specify a direction for the observed relationship between variables—for example, that mice with higher expression levels of a certain protein are more active than those with lower levels. This is then called a one-tailed hypothesis. 

Another example for a directional one-tailed alternative hypothesis would be that 

H1: Attending private classes before important exams has a positive effect on performance. 

Your null hypothesis would then be that

H0: Attending private classes before important exams has no/a negative effect on performance.

Nondirectional Hypothesis

A nondirectional hypothesis does not specify the direction of the potentially observed effect, only that there is a relationship between the studied variables—this is called a two-tailed hypothesis. For instance, if you are studying a new drug that has shown some effects on pathways involved in a certain condition (e.g., anxiety) in vitro in the lab, but you can’t say for sure whether it will have the same effects in an animal model or maybe induce other/side effects that you can’t predict and potentially increase anxiety levels instead, you could state the two hypotheses like this:

H1: The only lab-tested drug (somehow) affects anxiety levels in an anxiety mouse model.

You then test this nondirectional alternative hypothesis against the null hypothesis:

H0: The only lab-tested drug has no effect on anxiety levels in an anxiety mouse model.

How to Write a Hypothesis for a Research Paper

Now that we understand the important distinctions between different kinds of research hypotheses, let’s look at a simple process of how to write a hypothesis.

Writing a Hypothesis Step:1

Ask a question, based on earlier research. Research always starts with a question, but one that takes into account what is already known about a topic or phenomenon. For example, if you are interested in whether people who have pets are happier than those who don’t, do a literature search and find out what has already been demonstrated. You will probably realize that yes, there is quite a bit of research that shows a relationship between happiness and owning a pet—and even studies that show that owning a dog is more beneficial than owning a cat ! Let’s say you are so intrigued by this finding that you wonder: 

What is it that makes dog owners even happier than cat owners? 

Let’s move on to Step 2 and find an answer to that question.

Writing a Hypothesis Step 2:

Formulate a strong hypothesis by answering your own question. Again, you don’t want to make things up, take unicorns into account, or repeat/ignore what has already been done. Looking at the dog-vs-cat papers your literature search returned, you see that most studies are based on self-report questionnaires on personality traits, mental health, and life satisfaction. What you don’t find is any data on actual (mental or physical) health measures, and no experiments. You therefore decide to make a bold claim come up with the carefully thought-through hypothesis that it’s maybe the lifestyle of the dog owners, which includes walking their dog several times per day, engaging in fun and healthy activities such as agility competitions, and taking them on trips, that gives them that extra boost in happiness. You could therefore answer your question in the following way:

Dog owners are happier than cat owners because of the dog-related activities they engage in.

Now you have to verify that your hypothesis fulfills the two requirements we introduced at the beginning of this resource article: falsifiability and testability . If it can’t be wrong and can’t be tested, it’s not a hypothesis. We are lucky, however, because yes, we can test whether owning a dog but not engaging in any of those activities leads to lower levels of happiness or well-being than owning a dog and playing and running around with them or taking them on trips.  

Writing a Hypothesis Step 3:

Make your predictions and define your variables. We have verified that we can test our hypothesis, but now we have to define all the relevant variables, design our experiment or data analysis, and make precise predictions. You could, for example, decide to study dog owners (not surprising at this point), let them fill in questionnaires about their lifestyle as well as their life satisfaction (as other studies did), and then compare two groups of active and inactive dog owners. Alternatively, if you want to go beyond the data that earlier studies produced and analyzed and directly manipulate the activity level of your dog owners to study the effect of that manipulation, you could invite them to your lab, select groups of participants with similar lifestyles, make them change their lifestyle (e.g., couch potato dog owners start agility classes, very active ones have to refrain from any fun activities for a certain period of time) and assess their happiness levels before and after the intervention. In both cases, your independent variable would be “ level of engagement in fun activities with dog” and your dependent variable would be happiness or well-being . 

Examples of a Good and Bad Hypothesis

Let’s look at a few examples of good and bad hypotheses to get you started.

Good Hypothesis Examples

Bad Hypothesis Examples

Tips for Writing a Research Hypothesis

If you understood the distinction between a hypothesis and a prediction we made at the beginning of this article, then you will have no problem formulating your hypotheses and predictions correctly. To refresh your memory: We have to (1) look at existing evidence, (2) come up with a hypothesis, (3) make a prediction, and (4) design an experiment. For example, you could summarize your dog/happiness study like this:

(1) While research suggests that dog owners are happier than cat owners, there are no reports on what factors drive this difference. (2) We hypothesized that it is the fun activities that many dog owners (but very few cat owners) engage in with their pets that increases their happiness levels. (3) We thus predicted that preventing very active dog owners from engaging in such activities for some time and making very inactive dog owners take up such activities would lead to an increase and decrease in their overall self-ratings of happiness, respectively. (4) To test this, we invited dog owners into our lab, assessed their mental and emotional well-being through questionnaires, and then assigned them to an “active” and an “inactive” group, depending on… 

Note that you use “we hypothesize” only for your hypothesis, not for your experimental prediction, and “would” or “if – then” only for your prediction, not your hypothesis. A hypothesis that states that something “would” affect something else sounds as if you don’t have enough confidence to make a clear statement—in which case you can’t expect your readers to believe in your research either. Write in the present tense, don’t use modal verbs that express varying degrees of certainty (such as may, might, or could ), and remember that you are not drawing a conclusion while trying not to exaggerate but making a clear statement that you then, in a way, try to disprove . And if that happens, that is not something to fear but an important part of the scientific process.

Similarly, don’t use “we hypothesize” when you explain the implications of your research or make predictions in the conclusion section of your manuscript, since these are clearly not hypotheses in the true sense of the word. As we said earlier, you will find that many authors of academic articles do not seem to care too much about these rather subtle distinctions, but thinking very clearly about your own research will not only help you write better but also ensure that even that infamous Reviewer 2 will find fewer reasons to nitpick about your manuscript. 

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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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How to Write a Hypothesis: A Step-by-Step Guide

Introduction

An overview of the research hypothesis, different types of hypotheses, variables in a hypothesis, how to formulate an effective research hypothesis, designing a study around your hypothesis.

The scientific method can derive and test predictions as hypotheses. Empirical research can then provide support (or lack thereof) for the hypotheses. Even failure to find support for a hypothesis still represents a valuable contribution to scientific knowledge. Let's look more closely at the idea of the hypothesis and the role it plays in research.

As much as the term exists in everyday language, there is a detailed development that informs the word "hypothesis" when applied to research. A good research hypothesis is informed by prior research and guides research design and data analysis , so it is important to understand how a hypothesis is defined and understood by researchers.

What is the simple definition of a hypothesis?

A hypothesis is a testable prediction about an outcome between two or more variables . It functions as a navigational tool in the research process, directing what you aim to predict and how.

What is the hypothesis for in research?

In research, a hypothesis serves as the cornerstone for your empirical study. It not only lays out what you aim to investigate but also provides a structured approach for your data collection and analysis.

Essentially, it bridges the gap between the theoretical and the empirical, guiding your investigation throughout its course.

What is an example of a hypothesis?

If you are studying the relationship between physical exercise and mental health, a suitable hypothesis could be: "Regular physical exercise leads to improved mental well-being among adults."

This statement constitutes a specific and testable hypothesis that directly relates to the variables you are investigating.

What makes a good hypothesis?

A good hypothesis possesses several key characteristics. Firstly, it must be testable, allowing you to analyze data through empirical means, such as observation or experimentation, to assess if there is significant support for the hypothesis. Secondly, a hypothesis should be specific and unambiguous, giving a clear understanding of the expected relationship between variables. Lastly, it should be grounded in existing research or theoretical frameworks , ensuring its relevance and applicability.

Understanding the types of hypotheses can greatly enhance how you construct and work with hypotheses. While all hypotheses serve the essential function of guiding your study, there are varying purposes among the types of hypotheses. In addition, all hypotheses stand in contrast to the null hypothesis, or the assumption that there is no significant relationship between the variables .

Here, we explore various kinds of hypotheses to provide you with the tools needed to craft effective hypotheses for your specific research needs. Bear in mind that many of these hypothesis types may overlap with one another, and the specific type that is typically used will likely depend on the area of research and methodology you are following.

Null hypothesis

The null hypothesis is a statement that there is no effect or relationship between the variables being studied. In statistical terms, it serves as the default assumption that any observed differences are due to random chance.

For example, if you're studying the effect of a drug on blood pressure, the null hypothesis might state that the drug has no effect.

Alternative hypothesis

Contrary to the null hypothesis, the alternative hypothesis suggests that there is a significant relationship or effect between variables.

Using the drug example, the alternative hypothesis would posit that the drug does indeed affect blood pressure. This is what researchers aim to prove.

Simple hypothesis

A simple hypothesis makes a prediction about the relationship between two variables, and only two variables.

For example, "Increased study time results in better exam scores." Here, "study time" and "exam scores" are the only variables involved.

Complex hypothesis

A complex hypothesis, as the name suggests, involves more than two variables. For instance, "Increased study time and access to resources result in better exam scores." Here, "study time," "access to resources," and "exam scores" are all variables.

This hypothesis refers to multiple potential mediating variables. Other hypotheses could also include predictions about variables that moderate the relationship between the independent variable and dependent variable .

Directional hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. For example, "Eating more fruits and vegetables leads to a decrease in heart disease."

Here, the direction of heart disease is explicitly predicted to decrease, due to effects from eating more fruits and vegetables. All hypotheses typically specify the expected direction of the relationship between the independent and dependent variable, such that researchers can test if this prediction holds in their data analysis .

Statistical hypothesis

A statistical hypothesis is one that is testable through statistical methods, providing a numerical value that can be analyzed. This is commonly seen in quantitative research .

For example, "There is a statistically significant difference in test scores between students who study for one hour and those who study for two."

Empirical hypothesis

An empirical hypothesis is derived from observations and is tested through empirical methods, often through experimentation or survey data . Empirical hypotheses may also be assessed with statistical analyses.

For example, "Regular exercise is correlated with a lower incidence of depression," could be tested through surveys that measure exercise frequency and depression levels.

Causal hypothesis

A causal hypothesis proposes that one variable causes a change in another. This type of hypothesis is often tested through controlled experiments.

For example, "Smoking causes lung cancer," assumes a direct causal relationship.

Associative hypothesis

Unlike causal hypotheses, associative hypotheses suggest a relationship between variables but do not imply causation.

For instance, "People who smoke are more likely to get lung cancer," notes an association but doesn't claim that smoking causes lung cancer directly.

Relational hypothesis

A relational hypothesis explores the relationship between two or more variables but doesn't specify the nature of the relationship.

For example, "There is a relationship between diet and heart health," leaves the nature of the relationship (causal, associative, etc.) open to interpretation.

Logical hypothesis

A logical hypothesis is based on sound reasoning and logical principles. It's often used in theoretical research to explore abstract concepts, rather than being based on empirical data.

For example, "If all men are mortal and Socrates is a man, then Socrates is mortal," employs logical reasoning to make its point.

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In any research hypothesis, variables play a critical role. These are the elements or factors that the researcher manipulates, controls, or measures. Understanding variables is essential for crafting a clear, testable hypothesis and for the stages of research that follow, such as data collection and analysis.

In the realm of hypotheses, there are generally two types of variables to consider: independent and dependent. Independent variables are what you, as the researcher, manipulate or change in your study. It's considered the cause in the relationship you're investigating. For instance, in a study examining the impact of sleep duration on academic performance, the independent variable would be the amount of sleep participants get.

Conversely, the dependent variable is the outcome you measure to gauge the effect of your manipulation. It's the effect in the cause-and-effect relationship. The dependent variable thus refers to the main outcome of interest in your study. In the same sleep study example, the academic performance, perhaps measured by exam scores or GPA, would be the dependent variable.

Beyond these two primary types, you might also encounter control variables. These are variables that could potentially influence the outcome and are therefore kept constant to isolate the relationship between the independent and dependent variables . For example, in the sleep and academic performance study, control variables could include age, diet, or even the subject of study.

By clearly identifying and understanding the roles of these variables in your hypothesis, you set the stage for a methodologically sound research project. It helps you develop focused research questions, design appropriate experiments or observations, and carry out meaningful data analysis . It's a step that lays the groundwork for the success of your entire study.

Crafting a strong, testable hypothesis is crucial for the success of any research project. It sets the stage for everything from your study design to data collection and analysis . Below are some key considerations to keep in mind when formulating your hypothesis:

• Be specific : A vague hypothesis can lead to ambiguous results and interpretations . Clearly define your variables and the expected relationship between them.
• Ensure testability : A good hypothesis should be testable through empirical means, whether by observation , experimentation, or other forms of data analysis.
• Ground in literature : Before creating your hypothesis, consult existing research and theories. This not only helps you identify gaps in current knowledge but also gives you valuable context and credibility for crafting your hypothesis.
• Use simple language : While your hypothesis should be conceptually sound, it doesn't have to be complicated. Aim for clarity and simplicity in your wording.
• State direction, if applicable : If your hypothesis involves a directional outcome (e.g., "increase" or "decrease"), make sure to specify this. You also need to think about how you will measure whether or not the outcome moved in the direction you predicted.
• Keep it focused : One of the common pitfalls in hypothesis formulation is trying to answer too many questions at once. Keep your hypothesis focused on a specific issue or relationship.
• Account for control variables : Identify any variables that could potentially impact the outcome and consider how you will control for them in your study.
• Be ethical : Make sure your hypothesis and the methods for testing it comply with ethical standards , particularly if your research involves human or animal subjects.

Designing your study involves multiple key phases that help ensure the rigor and validity of your research. Here we discuss these crucial components in more detail.

Literature review

Starting with a comprehensive literature review is essential. This step allows you to understand the existing body of knowledge related to your hypothesis and helps you identify gaps that your research could fill. Your research should aim to contribute some novel understanding to existing literature, and your hypotheses can reflect this. A literature review also provides valuable insights into how similar research projects were executed, thereby helping you fine-tune your own approach.

Research methods

Choosing the right research methods is critical. Whether it's a survey, an experiment, or observational study, the methodology should be the most appropriate for testing your hypothesis. Your choice of methods will also depend on whether your research is quantitative, qualitative, or mixed-methods. Make sure the chosen methods align well with the variables you are studying and the type of data you need.

Preliminary research

Before diving into a full-scale study, it’s often beneficial to conduct preliminary research or a pilot study . This allows you to test your research methods on a smaller scale, refine your tools, and identify any potential issues. For instance, a pilot survey can help you determine if your questions are clear and if the survey effectively captures the data you need. This step can save you both time and resources in the long run.

Data analysis

Finally, planning your data analysis in advance is crucial for a successful study. Decide which statistical or analytical tools are most suited for your data type and research questions . For quantitative research, you might opt for t-tests, ANOVA, or regression analyses. For qualitative research , thematic analysis or grounded theory may be more appropriate. This phase is integral for interpreting your results and drawing meaningful conclusions in relation to your research question.

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Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

How to Write a Hypothesis

If I [do something], then [this] will happen.

This basic statement/formula should be pretty familiar to all of you as it is the starting point of almost every scientific project or paper. It is a hypothesis – a statement that showcases what you “think” will happen during an experiment. This assumption is made based on the knowledge, facts, and data you already have.

How do you write a hypothesis? If you have a clear understanding of the proper structure of a hypothesis, you should not find it too hard to create one. However, if you have never written a hypothesis before, you might find it a bit frustrating. In this article from EssayPro - custom essay writing services , we are going to tell you everything you need to know about hypotheses, their types, and practical tips for writing them.

Hypothesis Definition

According to the definition, a hypothesis is an assumption one makes based on existing knowledge. To elaborate, it is a statement that translates the initial research question into a logical prediction shaped on the basis of available facts and evidence. To solve a specific problem, one first needs to identify the research problem (research question), conduct initial research, and set out to answer the given question by performing experiments and observing their outcomes. However, before one can move to the experimental part of the research, they should first identify what they expect to see for results. At this stage, a scientist makes an educated guess and writes a hypothesis that he or she is going to prove or refute in the course of their study.

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A hypothesis can also be seen as a form of development of knowledge. It is a well-grounded assumption put forward to clarify the properties and causes of the phenomena being studied.

As a rule, a hypothesis is formed based on a number of observations and examples that confirm it. This way, it looks plausible as it is backed up with some known information. The hypothesis is subsequently proved by turning it into an established fact or refuted (for example, by pointing out a counterexample), which allows it to attribute it to the category of false statements.

As a student, you may be asked to create a hypothesis statement as a part of your academic papers. Hypothesis-based approaches are commonly used among scientific academic works, including but not limited to research papers, theses, and dissertations.

Note that in some disciplines, a hypothesis statement is called a thesis statement. However, its essence and purpose remain unchanged – this statement aims to make an assumption regarding the outcomes of the investigation that will either be proved or refuted.

Characteristics and Sources of a Hypothesis

Now, as you know what a hypothesis is in a nutshell, let’s look at the key characteristics that define it:

• It has to be clear and accurate in order to look reliable.
• It has to be specific.
• There should be scope for further investigation and experiments.
• A hypothesis should be explained in simple language—while retaining its significance.
• If you are making a relational hypothesis, two essential elements you have to include are variables and the relationship between them.

The main sources of a hypothesis are:

• Scientific theories.
• Observations from previous studies and current experiences.
• The resemblance among different phenomena.
• General patterns that affect people’s thinking process.

Types of Hypothesis

Basically, there are two major types of scientific hypothesis: alternative and null.

• Alternative Hypothesis

This type of hypothesis is generally denoted as H1. This statement is used to identify the expected outcome of your research. According to the alternative hypothesis definition, this type of hypothesis can be further divided into two subcategories:

• Directional — a statement that explains the direction of the expected outcomes. Sometimes this type of hypothesis is used to study the relationship between variables rather than comparing between the groups.
• Non-directional — unlike the directional alternative hypothesis, a non-directional one does not imply a specific direction of the expected outcomes.

Now, let’s see an alternative hypothesis example for each type:

Directional: Attending more lectures will result in improved test scores among students. Non-directional: Lecture attendance will influence test scores among students.

Notice how in the directional hypothesis we specified that the attendance of more lectures will boost student’s performance on tests, whereas in the non-directional hypothesis we only stated that there is a relationship between the two variables (i.e. lecture attendance and students’ test scores) but did not specify whether the performance will improve or decrease.

• Null Hypothesis

This type of hypothesis is generally denoted as H0. This statement is the complete opposite of what you expect or predict will happen throughout the course of your study—meaning it is the opposite of your alternative hypothesis. Simply put, a null hypothesis claims that there is no exact or actual correlation between the variables defined in the hypothesis.

To give you a better idea of how to write a null hypothesis, here is a clear example: Lecture attendance has no effect on student’s test scores.

Both of these types of hypotheses provide specific clarifications and restatements of the research problem. The main difference between these hypotheses and a research problem is that the latter is just a question that can’t be tested, whereas hypotheses can.

Based on the alternative and null hypothesis examples provided earlier, we can conclude that the importance and main purpose of these hypotheses are that they deliver a rough description of the subject matter. The main purpose of these statements is to give an investigator a specific guess that can be directly tested in a study. Simply put, a hypothesis outlines the framework, scope, and direction for the study. Although null and alternative hypotheses are the major types, there are also a few more to keep in mind:

Research Hypothesis — a statement that is used to test the correlation between two or more variables.

For example: Eating vitamin-rich foods affects human health.

Simple Hypothesis — a statement used to indicate the correlation between one independent and one dependent variable.

For example: Eating more vegetables leads to better immunity.

Complex Hypothesis — a statement used to indicate the correlation between two or more independent variables and two or more dependent variables.

For example: Eating more fruits and vegetables leads to better immunity, weight loss, and lower risk of diseases.

Associative and Causal Hypothesis — an associative hypothesis is a statement used to indicate the correlation between variables under the scenario when a change in one variable inevitably changes the other variable. A causal hypothesis is a statement that highlights the cause and effect relationship between variables.

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Hypothesis vs Prediction

When speaking of hypotheses, another term that comes to mind is prediction. These two terms are often used interchangeably, which can be rather confusing. Although both a hypothesis and prediction can generally be defined as “guesses” and can be easy to confuse, these terms are different. The main difference between a hypothesis and a prediction is that the first is predominantly used in science, while the latter is most often used outside of science.

Simply put, a hypothesis is an intelligent assumption. It is a guess made regarding the nature of the unknown (or less known) phenomena based on existing knowledge, studies, and/or series of experiments, and is otherwise grounded by valid facts. The main purpose of a hypothesis is to use available facts to create a logical relationship between variables in order to provide a more precise scientific explanation. Additionally, hypotheses are statements that can be tested with further experiments. It is an assumption you make regarding the flow and outcome(s) of your research study.

A prediction, on the contrary, is a guess that often lacks grounding. Although, in theory, a prediction can be scientific, in most cases it is rather fictional—i.e. a pure guess that is not based on current knowledge and/or facts. As a rule, predictions are linked to foretelling events that may or may not occur in the future. Often, a person who makes predictions has little or no actual knowledge of the subject matter he or she makes the assumption about.

Another big difference between these terms is in the methodology used to prove each of them. A prediction can only be proven once. You can determine whether it is right or wrong only upon the occurrence or non-occurrence of the predicted event. A hypothesis, on the other hand, offers scope for further testing and experiments. Additionally, a hypothesis can be proven in multiple stages. This basically means that a single hypothesis can be proven or refuted numerous times by different scientists who use different scientific tools and methods.

To give you a better idea of how a hypothesis is different from a prediction, let’s look at the following examples:

Hypothesis: If I eat more vegetables and fruits, then I will lose weight faster.

This is a hypothesis because it is based on generally available knowledge (i.e. fruits and vegetables include fewer calories compared to other foods) and past experiences (i.e. people who give preference to healthier foods like fruits and vegetables are losing weight easier). It is still a guess, but it is based on facts and can be tested with an experiment.

Prediction: The end of the world will occur in 2023.

This is a prediction because it foretells future events. However, this assumption is fictional as it doesn’t have any actual grounded evidence supported by facts.

Based on everything that was said earlier and our examples, we can highlight the following key takeaways:

• A hypothesis, unlike a prediction, is a more intelligent assumption based on facts.
• Hypotheses define existing variables and analyze the relationship(s) between them.
• Predictions are most often fictional and lack grounding.
• A prediction is most often used to foretell events in the future.
• A prediction can only be proven once – when the predicted event occurs or doesn’t occur. 
• A hypothesis can remain a hypothesis even if one scientist has already proven or disproven it. Other scientists in the future can obtain a different result using other methods and tools.

We also recommend that you read about some informative essay topics .

Now, as you know what a hypothesis is, what types of it exist, and how it differs from a prediction, you are probably wondering how to state a hypothesis. In this section, we will guide you through the main stages of writing a good hypothesis and provide handy tips and examples to help you overcome this challenge:

1. Define Your Research Question

Here is one thing to keep in mind – regardless of the paper or project you are working on, the process should always start with asking the right research question. A perfect research question should be specific, clear, focused (meaning not too broad), and manageable.

Example: How does eating fruits and vegetables affect human health?

2. Conduct Your Basic Initial Research

As you already know, a hypothesis is an educated guess of the expected results and outcomes of an investigation. Thus, it is vital to collect some information before you can make this assumption.

At this stage, you should find an answer to your research question based on what has already been discovered. Search for facts, past studies, theories, etc. Based on the collected information, you should be able to make a logical and intelligent guess.

3. Formulate a Hypothesis

Based on the initial research, you should have a certain idea of what you may find throughout the course of your research. Use this knowledge to shape a clear and concise hypothesis.

Based on the type of project you are working on, and the type of hypothesis you are planning to use, you can restate your hypothesis in several different ways:

Non-directional: Eating fruits and vegetables will affect one’s human physical health. Directional: Eating fruits and vegetables will positively affect one’s human physical health. Null: Eating fruits and vegetables will have no effect on one’s human physical health.

4. Refine Your Hypothesis

Finally, the last stage of creating a good hypothesis is refining what you’ve got. During this step, you need to define whether your hypothesis:

• Has clear and relevant variables;
• Identifies the relationship between its variables;
• Is specific and testable;
• Suggests a predicted result of the investigation or experiment.

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Hypothesis Examples

Following a step-by-step guide and tips from our essay writers for hire , you should be able to create good hypotheses with ease. To give you a starting point, we have also compiled a list of different research questions with one hypothesis and one null hypothesis example for each:

Ask Pros to Make a Perfect Hypothesis for You!

Sometimes, coping with a large academic load is just too much for a student to handle. Papers like research papers and dissertations can take too much time and effort to write, and, often, a hypothesis is a necessary starting point to get the task on track. Writing or editing a hypothesis is not as easy as it may seem. However, if you need help with forming it, the team at EssayPro is always ready to come to your rescue! If you’re feeling stuck, or don’t have enough time to cope with other tasks, don’t hesitate to send us you rewrite my essay for me or any other request.

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15 Hypothesis Examples

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A hypothesis is defined as a testable prediction , and is used primarily in scientific experiments as a potential or predicted outcome that scientists attempt to prove or disprove (Atkinson et al., 2021; Tan, 2022).

In my types of hypothesis article, I outlined 13 different hypotheses, including the directional hypothesis (which makes a prediction about an effect of a treatment will be positive or negative) and the associative hypothesis (which makes a prediction about the association between two variables).

This article will dive into some interesting examples of hypotheses and examine potential ways you might test each one.

Hypothesis Examples

1. “inadequate sleep decreases memory retention”.

Field: Psychology

Type: Causal Hypothesis A causal hypothesis explores the effect of one variable on another. This example posits that a lack of adequate sleep causes decreased memory retention. In other words, if you are not getting enough sleep, your ability to remember and recall information may suffer.

How to Test:

To test this hypothesis, you might devise an experiment whereby your participants are divided into two groups: one receives an average of 8 hours of sleep per night for a week, while the other gets less than the recommended sleep amount.

During this time, all participants would daily study and recall new, specific information. You’d then measure memory retention of this information for both groups using standard memory tests and compare the results.

Should the group with less sleep have statistically significant poorer memory scores, the hypothesis would be supported.

Ensuring the integrity of the experiment requires taking into account factors such as individual health differences, stress levels, and daily nutrition.

Relevant Study: Sleep loss, learning capacity and academic performance (Curcio, Ferrara & De Gennaro, 2006)

2. “Increase in Temperature Leads to Increase in Kinetic Energy”

Field: Physics

Type: Deductive Hypothesis The deductive hypothesis applies the logic of deductive reasoning – it moves from a general premise to a more specific conclusion. This specific hypothesis assumes that as temperature increases, the kinetic energy of particles also increases – that is, when you heat something up, its particles move around more rapidly.

This hypothesis could be examined by heating a gas in a controlled environment and capturing the movement of its particles as a function of temperature.

You’d gradually increase the temperature and measure the kinetic energy of the gas particles with each increment. If the kinetic energy consistently rises with the temperature, your hypothesis gets supporting evidence.

Variables such as pressure and volume of the gas would need to be held constant to ensure validity of results.

3. “Children Raised in Bilingual Homes Develop Better Cognitive Skills”

Field: Psychology/Linguistics

Type: Comparative Hypothesis The comparative hypothesis posits a difference between two or more groups based on certain variables. In this context, you might propose that children raised in bilingual homes have superior cognitive skills compared to those raised in monolingual homes.

Testing this hypothesis could involve identifying two groups of children: those raised in bilingual homes, and those raised in monolingual homes.

Cognitive skills in both groups would be evaluated using a standard cognitive ability test at different stages of development. The examination would be repeated over a significant time period for consistency.

If the group raised in bilingual homes persistently scores higher than the other, the hypothesis would thereby be supported.

The challenge for the researcher would be controlling for other variables that could impact cognitive development, such as socio-economic status, education level of parents, and parenting styles.

Relevant Study: The cognitive benefits of being bilingual (Marian & Shook, 2012)

4. “High-Fiber Diet Leads to Lower Incidences of Cardiovascular Diseases”

Field: Medicine/Nutrition

Type: Alternative Hypothesis The alternative hypothesis suggests an alternative to a null hypothesis. In this context, the implied null hypothesis could be that diet has no effect on cardiovascular health, which the alternative hypothesis contradicts by suggesting that a high-fiber diet leads to fewer instances of cardiovascular diseases.

To test this hypothesis, a longitudinal study could be conducted on two groups of participants; one adheres to a high-fiber diet, while the other follows a diet low in fiber.

After a fixed period, the cardiovascular health of participants in both groups could be analyzed and compared. If the group following a high-fiber diet has a lower number of recorded cases of cardiovascular diseases, it would provide evidence supporting the hypothesis.

Control measures should be implemented to exclude the influence of other lifestyle and genetic factors that contribute to cardiovascular health.

Relevant Study: Dietary fiber, inflammation, and cardiovascular disease (King, 2005)

5. “Gravity Influences the Directional Growth of Plants”

Field: Agronomy / Botany

Type: Explanatory Hypothesis An explanatory hypothesis attempts to explain a phenomenon. In this case, the hypothesis proposes that gravity affects how plants direct their growth – both above-ground (toward sunlight) and below-ground (towards water and other resources).

The testing could be conducted by growing plants in a rotating cylinder to create artificial gravity.

Observations on the direction of growth, over a specified period, can provide insights into the influencing factors. If plants consistently direct their growth in a manner that indicates the influence of gravitational pull, the hypothesis is substantiated.

It is crucial to ensure that other growth-influencing factors, such as light and water, are uniformly distributed so that only gravity influences the directional growth.

6. “The Implementation of Gamified Learning Improves Students’ Motivation”

Field: Education

Type: Relational Hypothesis The relational hypothesis describes the relation between two variables. Here, the hypothesis is that the implementation of gamified learning has a positive effect on the motivation of students.

To validate this proposition, two sets of classes could be compared: one that implements a learning approach with game-based elements, and another that follows a traditional learning approach.

The students’ motivation levels could be gauged by monitoring their engagement, performance, and feedback over a considerable timeframe.

If the students engaged in the gamified learning context present higher levels of motivation and achievement, the hypothesis would be supported.

Control measures ought to be put into place to account for individual differences, including prior knowledge and attitudes towards learning.

Relevant Study: Does educational gamification improve students’ motivation? (Chapman & Rich, 2018)

7. “Mathematics Anxiety Negatively Affects Performance”

Field: Educational Psychology

Type: Research Hypothesis The research hypothesis involves making a prediction that will be tested. In this case, the hypothesis proposes that a student’s anxiety about math can negatively influence their performance in math-related tasks.

To assess this hypothesis, researchers must first measure the mathematics anxiety levels of a sample of students using a validated instrument, such as the Mathematics Anxiety Rating Scale.

Then, the students’ performance in mathematics would be evaluated through standard testing. If there’s a negative correlation between the levels of math anxiety and math performance (meaning as anxiety increases, performance decreases), the hypothesis would be supported.

It would be crucial to control for relevant factors such as overall academic performance and previous mathematical achievement.

8. “Disruption of Natural Sleep Cycle Impairs Worker Productivity”

Field: Organizational Psychology

Type: Operational Hypothesis The operational hypothesis involves defining the variables in measurable terms. In this example, the hypothesis posits that disrupting the natural sleep cycle, for instance through shift work or irregular working hours, can lessen productivity among workers.

To test this hypothesis, you could collect data from workers who maintain regular working hours and those with irregular schedules.

Measuring productivity could involve examining the worker’s ability to complete tasks, the quality of their work, and their efficiency.

If workers with interrupted sleep cycles demonstrate lower productivity compared to those with regular sleep patterns, it would lend support to the hypothesis.

Consideration should be given to potential confounding variables such as job type, worker age, and overall health.

9. “Regular Physical Activity Reduces the Risk of Depression”

Field: Health Psychology

Type: Predictive Hypothesis A predictive hypothesis involves making a prediction about the outcome of a study based on the observed relationship between variables. In this case, it is hypothesized that individuals who engage in regular physical activity are less likely to suffer from depression.

Longitudinal studies would suit to test this hypothesis, tracking participants’ levels of physical activity and their mental health status over time.

The level of physical activity could be self-reported or monitored, while mental health status could be assessed using standard diagnostic tools or surveys.

If data analysis shows that participants maintaining regular physical activity have a lower incidence of depression, this would endorse the hypothesis.

However, care should be taken to control other lifestyle and behavioral factors that could intervene with the results.

Relevant Study: Regular physical exercise and its association with depression (Kim, 2022)

10. “Regular Meditation Enhances Emotional Stability”

Type: Empirical Hypothesis In the empirical hypothesis, predictions are based on amassed empirical evidence . This particular hypothesis theorizes that frequent meditation leads to improved emotional stability, resonating with numerous studies linking meditation to a variety of psychological benefits.

Earlier studies reported some correlations, but to test this hypothesis directly, you’d organize an experiment where one group meditates regularly over a set period while a control group doesn’t.

Both groups’ emotional stability levels would be measured at the start and end of the experiment using a validated emotional stability assessment.

If regular meditators display noticeable improvements in emotional stability compared to the control group, the hypothesis gains credit.

You’d have to ensure a similar emotional baseline for all participants at the start to avoid skewed results.

11. “Children Exposed to Reading at an Early Age Show Superior Academic Progress”

Type: Directional Hypothesis The directional hypothesis predicts the direction of an expected relationship between variables. Here, the hypothesis anticipates that early exposure to reading positively affects a child’s academic advancement.

A longitudinal study tracking children’s reading habits from an early age and their consequent academic performance could validate this hypothesis.

Parents could report their children’s exposure to reading at home, while standardized school exam results would provide a measure of academic achievement.

If the children exposed to early reading consistently perform better acadically, it gives weight to the hypothesis.

However, it would be important to control for variables that might impact academic performance, such as socioeconomic background, parental education level, and school quality.

12. “Adopting Energy-efficient Technologies Reduces Carbon Footprint of Industries”

Field: Environmental Science

Type: Descriptive Hypothesis A descriptive hypothesis predicts the existence of an association or pattern related to variables. In this scenario, the hypothesis suggests that industries adopting energy-efficient technologies will resultantly show a reduced carbon footprint.

Global industries making use of energy-efficient technologies could track their carbon emissions over time. At the same time, others not implementing such technologies continue their regular tracking.

After a defined time, the carbon emission data of both groups could be compared. If industries that adopted energy-efficient technologies demonstrate a notable reduction in their carbon footprints, the hypothesis would hold strong.

In the experiment, you would exclude variations brought by factors such as industry type, size, and location.

13. “Reduced Screen Time Improves Sleep Quality”

Type: Simple Hypothesis The simple hypothesis is a prediction about the relationship between two variables, excluding any other variables from consideration. This example posits that by reducing time spent on devices like smartphones and computers, an individual should experience improved sleep quality.

A sample group would need to reduce their daily screen time for a pre-determined period. Sleep quality before and after the reduction could be measured using self-report sleep diaries and objective measures like actigraphy, monitoring movement and wakefulness during sleep.

If the data shows that sleep quality improved post the screen time reduction, the hypothesis would be validated.

Other aspects affecting sleep quality, like caffeine intake, should be controlled during the experiment.

Relevant Study: Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep (Waller et al., 2021)

14. Engaging in Brain-Training Games Improves Cognitive Functioning in Elderly

Field: Gerontology

Type: Inductive Hypothesis Inductive hypotheses are based on observations leading to broader generalizations and theories. In this context, the hypothesis deduces from observed instances that engaging in brain-training games can help improve cognitive functioning in the elderly.

A longitudinal study could be conducted where an experimental group of elderly people partakes in regular brain-training games.

Their cognitive functioning could be assessed at the start of the study and at regular intervals using standard neuropsychological tests.

If the group engaging in brain-training games shows better cognitive functioning scores over time compared to a control group not playing these games, the hypothesis would be supported.

15. Farming Practices Influence Soil Erosion Rates

Type: Null Hypothesis A null hypothesis is a negative statement assuming no relationship or difference between variables. The hypothesis in this context asserts there’s no effect of different farming practices on the rates of soil erosion.

Comparing soil erosion rates in areas with different farming practices over a considerable timeframe could help test this hypothesis.

If, statistically, the farming practices do not lead to differences in soil erosion rates, the null hypothesis is accepted.

However, if marked variation appears, the null hypothesis is rejected, meaning farming practices do influence soil erosion rates. It would be crucial to control for external factors like weather, soil type, and natural vegetation.

The variety of hypotheses mentioned above underscores the diversity of research constructs inherent in different fields, each with its unique purpose and way of testing.

While researchers may develop hypotheses primarily as tools to define and narrow the focus of the study, these hypotheses also serve as valuable guiding forces for the data collection and analysis procedures, making the research process more efficient and direction-focused.

Hypotheses serve as a compass for any form of academic research. The diverse examples provided, from Psychology to Educational Studies, Environmental Science to Gerontology, clearly demonstrate how certain hypotheses suit specific fields more aptly than others.

It is important to underline that although these varied hypotheses differ in their structure and methods of testing, each endorses the fundamental value of empiricism in research. Evidence-based decision making remains at the heart of scholarly inquiry, regardless of the research field, thus aligning all hypotheses to the core purpose of scientific investigation.

Testing hypotheses is an essential part of the scientific method . By doing so, researchers can either confirm their predictions, giving further validity to an existing theory, or they might uncover new insights that could potentially shift the field’s understanding of a particular phenomenon. In either case, hypotheses serve as the stepping stones for scientific exploration and discovery.

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J. W., & Williams, R. A. (2021).  SAGE research methods foundations . SAGE Publications Ltd.

Curcio, G., Ferrara, M., & De Gennaro, L. (2006). Sleep loss, learning capacity and academic performance.  Sleep medicine reviews ,  10 (5), 323-337.

Kim, J. H. (2022). Regular physical exercise and its association with depression: A population-based study short title: Exercise and depression.  Psychiatry Research ,  309 , 114406.

King, D. E. (2005). Dietary fiber, inflammation, and cardiovascular disease.  Molecular nutrition & food research ,  49 (6), 594-600.

Marian, V., & Shook, A. (2012, September). The cognitive benefits of being bilingual. In Cerebrum: the Dana forum on brain science (Vol. 2012). Dana Foundation.

Tan, W. C. K. (2022). Research Methods: A Practical Guide For Students And Researchers (Second Edition) . World Scientific Publishing Company.

Waller, N. A., Zhang, N., Cocci, A. H., D’Agostino, C., Wesolek‐Greenson, S., Wheelock, K., … & Resnicow, K. (2021). Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep. Child: care, health and development, 47 (5), 618-626.

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Green Flags in a Relationship
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Signs you're Burnt Out, Not Lazy
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Toxic Things Parents Say to their Children
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Red Flags Early in a Relationship

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Hypothesis Examples

A hypothesis is a prediction of the outcome of a test. It forms the basis for designing an experiment in the scientific method . A good hypothesis is testable, meaning it makes a prediction you can check with observation or experimentation. Here are different hypothesis examples.

Null Hypothesis Examples

The null hypothesis (H 0 ) is also known as the zero-difference or no-difference hypothesis. It predicts that changing one variable ( independent variable ) will have no effect on the variable being measured ( dependent variable ). Here are null hypothesis examples:

• Plant growth is unaffected by temperature.
• If you increase temperature, then solubility of salt will increase.
• Incidence of skin cancer is unrelated to ultraviolet light exposure.
• All brands of light bulb last equally long.
• Cats have no preference for the color of cat food.
• All daisies have the same number of petals.

Sometimes the null hypothesis shows there is a suspected correlation between two variables. For example, if you think plant growth is affected by temperature, you state the null hypothesis: “Plant growth is not affected by temperature.” Why do you do this, rather than say “If you change temperature, plant growth will be affected”? The answer is because it’s easier applying a statistical test that shows, with a high level of confidence, a null hypothesis is correct or incorrect.

Research Hypothesis Examples

A research hypothesis (H 1 ) is a type of hypothesis used to design an experiment. This type of hypothesis is often written as an if-then statement because it’s easy identifying the independent and dependent variables and seeing how one affects the other. If-then statements explore cause and effect. In other cases, the hypothesis shows a correlation between two variables. Here are some research hypothesis examples:

• If you leave the lights on, then it takes longer for people to fall asleep.
• If you refrigerate apples, they last longer before going bad.
• If you keep the curtains closed, then you need less electricity to heat or cool the house (the electric bill is lower).
• If you leave a bucket of water uncovered, then it evaporates more quickly.
• Goldfish lose their color if they are not exposed to light.
• Workers who take vacations are more productive than those who never take time off.

Is It Okay to Disprove a Hypothesis?

Yes! You may even choose to write your hypothesis in such a way that it can be disproved because it’s easier to prove a statement is wrong than to prove it is right. In other cases, if your prediction is incorrect, that doesn’t mean the science is bad. Revising a hypothesis is common. It demonstrates you learned something you did not know before you conducted the experiment.

Test yourself with a Scientific Method Quiz .

• Mellenbergh, G.J. (2008). Chapter 8: Research designs: Testing of research hypotheses. In H.J. Adèr & G.J. Mellenbergh (eds.), Advising on Research Methods: A Consultant’s Companion . Huizen, The Netherlands: Johannes van Kessel Publishing.
• Popper, Karl R. (1959). The Logic of Scientific Discovery . Hutchinson & Co. ISBN 3-1614-8410-X.
• Schick, Theodore; Vaughn, Lewis (2002). How to think about weird things: critical thinking for a New Age . Boston: McGraw-Hill Higher Education. ISBN 0-7674-2048-9.
• Tobi, Hilde; Kampen, Jarl K. (2018). “Research design: the methodology for interdisciplinary research framework”. Quality & Quantity . 52 (3): 1209–1225. doi: 10.1007/s11135-017-0513-8

Related Posts

How to Write a Hypothesis? Types and Examples 

All research studies involve the use of the scientific method, which is a mathematical and experimental technique used to conduct experiments by developing and testing a hypothesis or a prediction about an outcome. Simply put, a hypothesis is a suggested solution to a problem. It includes elements that are expressed in terms of relationships with each other to explain a condition or an assumption that hasn’t been verified using facts. 1 The typical steps in a scientific method include developing such a hypothesis, testing it through various methods, and then modifying it based on the outcomes of the experiments.  

A research hypothesis can be defined as a specific, testable prediction about the anticipated results of a study. 2 Hypotheses help guide the research process and supplement the aim of the study. After several rounds of testing, hypotheses can help develop scientific theories. 3 Hypotheses are often written as if-then statements. 

Here are two hypothesis examples: 

Dandelions growing in nitrogen-rich soils for two weeks develop larger leaves than those in nitrogen-poor soils because nitrogen stimulates vegetative growth. 4  

If a company offers flexible work hours, then their employees will be happier at work. 5  

Table of Contents

• What is a hypothesis? 
• Types of hypotheses 
• Characteristics of a hypothesis 
• Functions of a hypothesis 
• How to write a hypothesis 
• Hypothesis examples 
• Frequently asked questions 

What is a hypothesis?

A hypothesis expresses an expected relationship between variables in a study and is developed before conducting any research. Hypotheses are not opinions but rather are expected relationships based on facts and observations. They help support scientific research and expand existing knowledge. An incorrectly formulated hypothesis can affect the entire experiment leading to errors in the results so it’s important to know how to formulate a hypothesis and develop it carefully.

A few sources of a hypothesis include observations from prior studies, current research and experiences, competitors, scientific theories, and general conditions that can influence people. Figure 1 depicts the different steps in a research design and shows where exactly in the process a hypothesis is developed. 4  

There are seven different types of hypotheses—simple, complex, directional, nondirectional, associative and causal, null, and alternative. 

Types of hypotheses

The seven types of hypotheses are listed below: 5 , 6,7  

• Simple : Predicts the relationship between a single dependent variable and a single independent variable. 

Example: Exercising in the morning every day will increase your productivity.  

• Complex : Predicts the relationship between two or more variables. 

Example: Spending three hours or more on social media daily will negatively affect children’s mental health and productivity, more than that of adults.  

• Directional : Specifies the expected direction to be followed and uses terms like increase, decrease, positive, negative, more, or less. 

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

• Non-directional : Does not predict the exact direction, nature, or magnitude of the relationship between two variables but rather states the existence of a relationship. This hypothesis may be used when there is no underlying theory or if findings contradict prior research. 

Example: Cats and dogs differ in the amount of affection they express.  

• Associative and causal : An associative hypothesis suggests an interdependency between variables, that is, how a change in one variable changes the other.  

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

A causal hypothesis, on the other hand, expresses a cause-and-effect association between variables. 

Example: Long-term alcohol use causes liver damage.  

• Null : Claims that the original hypothesis is false by showing that there is no relationship between the variables. 

Example: Sleep duration does not have any effect on productivity.  

• Alternative : States the opposite of the null hypothesis, that is, a relationship exists between two variables. 

Example: Sleep duration affects productivity.  

Characteristics of a hypothesis

So, what makes a good hypothesis? Here are some important characteristics of a hypothesis. 8,9  

• Testable : You must be able to test the hypothesis using scientific methods to either accept or reject the prediction. 
• Falsifiable : It should be possible to collect data that reject rather than support the hypothesis. 
• Logical : Hypotheses shouldn’t be a random guess but rather should be based on previous theories, observations, prior research, and logical reasoning. 
• Positive : The hypothesis statement about the existence of an association should be positive, that is, it should not suggest that an association does not exist. Therefore, the language used and knowing how to phrase a hypothesis is very important. 
• Clear and accurate : The language used should be easily comprehensible and use correct terminology. 
• Relevant : The hypothesis should be relevant and specific to the research question. 
• Structure : Should include all the elements that make a good hypothesis: variables, relationship, and outcome. 

Functions of a hypothesis

The following list mentions some important functions of a hypothesis: 1  

• Maintains the direction and progress of the research. 
• Expresses the important assumptions underlying the proposition in a single statement. 
• Establishes a suitable context for researchers to begin their investigation and for readers who are referring to the final report. 
• Provides an explanation for the occurrence of a specific phenomenon. 
• Ensures selection of appropriate and accurate facts necessary and relevant to the research subject. 

To summarize, a hypothesis provides the conceptual elements that complete the known data, conceptual relationships that systematize unordered elements, and conceptual meanings and interpretations that explain the unknown phenomena. 1  

How to write a hypothesis

Listed below are the main steps explaining how to write a hypothesis. 2,4,5  

• Make an observation and identify variables : Observe the subject in question and try to recognize a pattern or a relationship between the variables involved. This step provides essential background information to begin your research.  

For example, if you notice that an office’s vending machine frequently runs out of a specific snack, you may predict that more people in the office choose that snack over another. 

• Identify the main research question : After identifying a subject and recognizing a pattern, the next step is to ask a question that your hypothesis will answer.  

For example, after observing employees’ break times at work, you could ask “why do more employees take breaks in the morning rather than in the afternoon?” 

• Conduct some preliminary research to ensure originality and novelty : Your initial answer, which is your hypothesis, to the question is based on some pre-existing information about the subject. However, to ensure that your hypothesis has not been asked before or that it has been asked but rejected by other researchers you would need to gather additional information.  

For example, based on your observations you might state a hypothesis that employees work more efficiently when the air conditioning in the office is set at a lower temperature. However, during your preliminary research you find that this hypothesis was proven incorrect by a prior study. 

• Develop a general statement : After your preliminary research has confirmed the originality of your proposed answer, draft a general statement that includes all variables, subjects, and predicted outcome. The statement could be if/then or declarative.  
• Finalize the hypothesis statement : Use the PICOT model, which clarifies how to word a hypothesis effectively, when finalizing the statement. This model lists the important components required to write a hypothesis. 

P opulation: The specific group or individual who is the main subject of the research 

I nterest: The main concern of the study/research question 

C omparison: The main alternative group 

O utcome: The expected results  

T ime: Duration of the experiment 

Once you’ve finalized your hypothesis statement you would need to conduct experiments to test whether the hypothesis is true or false. 

Hypothesis examples

The following table provides examples of different types of hypotheses. 10 ,11  

Key takeaways  

Here’s a summary of all the key points discussed in this article about how to write a hypothesis. 

• A hypothesis is an assumption about an association between variables made based on limited evidence, which should be tested. 
• A hypothesis has four parts—the research question, independent variable, dependent variable, and the proposed relationship between the variables.   
• The statement should be clear, concise, testable, logical, and falsifiable. 
• There are seven types of hypotheses—simple, complex, directional, non-directional, associative and causal, null, and alternative. 
• A hypothesis provides a focus and direction for the research to progress. 
• A hypothesis plays an important role in the scientific method by helping to create an appropriate experimental design. 

Frequently asked questions

Hypotheses and research questions have different objectives and structure. The following table lists some major differences between the two. 9  

Here are a few examples to differentiate between a research question and hypothesis. 

Yes, here’s a simple checklist to help you gauge the effectiveness of your hypothesis. 9   1. When writing a hypothesis statement, check if it:  2. Predicts the relationship between the stated variables and the expected outcome.  3. Uses simple and concise language and is not wordy.  4. Does not assume readers’ knowledge about the subject.  5. Has observable, falsifiable, and testable results. 

As mentioned earlier in this article, a hypothesis is an assumption or prediction about an association between variables based on observations and simple evidence. These statements are usually generic. Research objectives, on the other hand, are more specific and dictated by hypotheses. The same hypothesis can be tested using different methods and the research objectives could be different in each case.     For example, Louis Pasteur observed that food lasts longer at higher altitudes, reasoned that it could be because the air at higher altitudes is cleaner (with fewer or no germs), and tested the hypothesis by exposing food to air cleaned in the laboratory. 12 Thus, a hypothesis is predictive—if the reasoning is correct, X will lead to Y—and research objectives are developed to test these predictions. 

Null hypothesis testing is a method to decide between two assumptions or predictions between variables (null and alternative hypotheses) in a statistical relationship in a sample. The null hypothesis, denoted as H 0 , claims that no relationship exists between variables in a population and any relationship in the sample reflects a sampling error or occurrence by chance. The alternative hypothesis, denoted as H 1 , claims that there is a relationship in the population. In every study, researchers need to decide whether the relationship in a sample occurred by chance or reflects a relationship in the population. This is done by hypothesis testing using the following steps: 13   1. Assume that the null hypothesis is true.  2. Determine how likely the sample relationship would be if the null hypothesis were true. This probability is called the p value.  3. If the sample relationship would be extremely unlikely, reject the null hypothesis and accept the alternative hypothesis. If the relationship would not be unlikely, accept the null hypothesis. 

To summarize, researchers should know how to write a good hypothesis to ensure that their research progresses in the required direction. A hypothesis is a testable prediction about any behavior or relationship between variables, usually based on facts and observation, and states an expected outcome.  

We hope this article has provided you with essential insight into the different types of hypotheses and their functions so that you can use them appropriately in your next research project. 

References  

• Dalen, DVV. The function of hypotheses in research. Proquest website. Accessed April 8, 2024. https://www.proquest.com/docview/1437933010?pq-origsite=gscholar&fromopenview=true&sourcetype=Scholarly%20Journals&imgSeq=1  
• McLeod S. Research hypothesis in psychology: Types & examples. SimplyPsychology website. Updated December 13, 2023. Accessed April 9, 2024. https://www.simplypsychology.org/what-is-a-hypotheses.html  
• Scientific method. Britannica website. Updated March 14, 2024. Accessed April 9, 2024. https://www.britannica.com/science/scientific-method  
• The hypothesis in science writing. Accessed April 10, 2024. https://berks.psu.edu/sites/berks/files/campus/HypothesisHandout_Final.pdf  
• How to develop a hypothesis (with elements, types, and examples). Indeed.com website. Updated February 3, 2023. Accessed April 10, 2024. https://www.indeed.com/career-advice/career-development/how-to-write-a-hypothesis  
• Types of research hypotheses. Excelsior online writing lab. Accessed April 11, 2024. https://owl.excelsior.edu/research/research-hypotheses/types-of-research-hypotheses/  
• What is a research hypothesis: how to write it, types, and examples. Researcher.life website. Published February 8, 2023. Accessed April 11, 2024. https://researcher.life/blog/article/how-to-write-a-research-hypothesis-definition-types-examples/  
• Developing a hypothesis. Pressbooks website. Accessed April 12, 2024. https://opentext.wsu.edu/carriecuttler/chapter/developing-a-hypothesis/  
• What is and how to write a good hypothesis in research. Elsevier author services website. Accessed April 12, 2024. https://scientific-publishing.webshop.elsevier.com/manuscript-preparation/what-how-write-good-hypothesis-research/  
• How to write a great hypothesis. Verywellmind website. Updated March 12, 2023. Accessed April 13, 2024. https://www.verywellmind.com/what-is-a-hypothesis-2795239  
• 15 Hypothesis examples. Helpfulprofessor.com Published September 8, 2023. Accessed March 14, 2024. https://helpfulprofessor.com/hypothesis-examples/ 
• Editage insights. What is the interconnectivity between research objectives and hypothesis? Published February 24, 2021. Accessed April 13, 2024. https://www.editage.com/insights/what-is-the-interconnectivity-between-research-objectives-and-hypothesis  
• Understanding null hypothesis testing. BCCampus open publishing. Accessed April 16, 2024. https://opentextbc.ca/researchmethods/chapter/understanding-null-hypothesis-testing/#:~:text=In%20null%20hypothesis%20testing%2C%20this,said%20to%20be%20statistically%20significant  

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How to Write a Hypothesis: Step-by-Step Guide and Examples

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Each academic research revolves around specific statement or problem — a research hypothesis. 

A hypothesis is a suggested prediction for a phenomenon or observed event, based on prior knowledge or research. It is a tentative statement that can be tested through further investigation and analysis. A hypothesis usually takes the form of a statement that suggests a relationship between two or more variables.

Every research project, be it a a term paper, research paper or a dissertation, should begin with defining a hypothesis. While this may seem simple, in reality beginners face a lot of problems. This includes difficulty with formulating a hypothesis accurately and capturing the main idea. In this blog post, we will tell you how to write a hypothesis so it is accurate and correct.

What Is a Research Hypothesis: Expanded Definition

A research hypothesis is a statement or assumption that answers a question you asked earlier but haven't tested yet. In fact, this is basis of your work which you use to prove or reject your assumption. Major research projects most often deal with several hypotheses. These relate to various aspects of an issue under study. Thus, you will divide assumptions by research sectors and study them in a segmented manner. When making an assignment, one must work based on an existing theory and gained knowledge. One must also take into account that it must be testable. That is, it can be rejected or confirmed with methods of scientific research. Hypothesis example may look like this:

In your work, you must prove or reject this hypothesis by providing survey results. Show some statistical analysis , study of reports and other processed data.

Remeber that you can hire a paper writer who will integrate survey outcomes and conduct statistical analysis in your research paper hypothesis. 

Variables in Hypotheses

To make a qualitative guess, you should consider variables in your hypothesis. They can be divided into independent and dependent ones. In fact, you must establish causal relationship between two or more variables. Independent ( confounding variable ) is what researcher can control or change, i.e. initial condition. Dependent ( extraneous variable ) is what researcher studies. It is observed in created conditions. Before you start learning how to write an assignment with independent and dependent variables, you should define the main idea of your work. For example, you take an assumption that eating hedgehog meat reduces risk of cardiovascular disease. Independent variable is hedgehog meat consumption, which is cause. Improvement in cardiovascular health is a dependent variable – an intended effect.

How to Write a Hypothesis: 5 Simple Writing Steps

Novice researchers most frequently ask how to write a hypothesis statement. This is a complex process that includes compilation of laconic predictions. These are based on conducted experiments. We can support you in this task. We have developed 5 steps for researchers so they can write a high-quality and comprehensive assignment.

Step 1. Generate a Question Before Writing Your Hypothesis

At the first stage of writing a hypothesis for a research paper you must define a research question that you need to answer. It should be focused on particular problem. Try to make it specific and yet suitable for research within framework of your project. To write quality assignment, you must use 6 classic statements. Thus, you must clarify: who, what, where, when, why and how. You must make question understandable in terms of positioning problem. Example of correct hypothesis:

Step 2. Gather Preliminary Research for Your Hypothesis

Before writing a research hypothesis, conduct some preliminary research to find out if your assumption is working and can be proved. You will get the key insights through observations or experiments. You can also use results of your colleagues who have already studied this issue. Thus, you will build a concept with formulated variables. You will study them and identify relationships between them.

Step 3. Write a Strong Hypothesis

With results of preliminary preparation and research questions, you can study how to write a strong hypothesis . First of all, highlight the main testing problem. You must formulate it as briefly as possible. Try to avoid stretching statements in an attempt to make paper longer. Be as clear as possible, avoid vague judgments. For example:

This is not good option. It is better to apply hypothesis in the form of:

This is a clear sentence that is devoid of unnecessary details. It allows you to immediately see an expected effect. Get practical help in writing research paper if you wish for more quality.

Step 4. Refine Your Research Hypothesis

Make sure a hypothesis for a research proposal formulated correctly. You must check if it has following elements:

• Dependent and independent variables.
• An object or phenomenon for testing.
• Expected outcome of study that you plan to work through. This must be part of an experiment or an observation.

This way, you will specify question under study. You also will be able to verify it if needed. That is, you will move from general to particular.

Step 5. Write a Null Hypothesis

You may need to write a null hypothesis. Why and when, you may ask? When you use this method for processing specific statistics. You should specify if you plan to prove your point on its basis. In fact, it is clear position that doesn’t establish links between variables. For example, this statement is null hypothesis:

It is basis for presenting one's own opinion. It allows to build an evidence base stemming from researcher's evidence.

What Is the Difference Between a Null Hypothesis and an Alternative Hypothesis

To better understand how to write null and alternative hypothesis that will form backbone of study, examine testable statements. Based on results, null hypothesis is prepared. It is a statement with no connection between variables. At the same time, scientists usually work with an alternative hypothesis. Here, they have already found a connection between phenomena. Ever considered custom research paper writing service ? So, the above statement about frequency of doctor visits can be modified to research of: 

Hypothesis Examples

Quite often, researchers find it difficult to formulate basis for  writing a research paper . Therefore, some examples of hypothesis will be useful for them. This will correspond to if-then connections. With their help you will also briefly outline the main part of current research. We will help you in formulating an assignment and offer several working options:

Tips on Writing a Hypothesis

It’s difficult to start writing a hypothesis for a research proposal. Especially for aspiring academics! After all, it is important that an assignment is clear and specific. It must also be viable for further development. Here are some tips to help you formulate your statement:

• Analyze interesting aspects. Review current studies and problems on the selected topic. Highlight what you wanted to explore, perhaps it will be a concept close to your previous works.
• Clarify the details. Spend time on preliminary analysis. You must also highlight controversial aspects and contemporary issues. Sometimes, even well-researched phenomena can be promising.
• Focus on your own work. It’s always easier to continue than to start anew. At the same time, you might not have considered all the theses in the previous study.
• Make the variables clear. Avoid ambiguous statements.

Sounds a bit difficult? College paper help is there for you.

How to Write a Scientific Hypothesis: Final Thoughts

So, if you've come this far, you should already know how to how to write a hypothesis step by step. Before starting writing, analyze the problem and the topic. You should highlight the thesis that can be developed further. We recommend going through the following steps:

• Define the question you expect to receive an answer to.
• Do some preliminary research.
• Write it strongly.
• Refine it with variables, subject and phenomenon, and expected result.
• Make a null hypothesis and consider a different option.

Let our assistants write your hypothesis for you! Choose a paper writer to your liking, send them your requirements and get a great paper in no-time!

Frequently Asked Questions About Writing a Good Hypothesis

1. how can i improve my hypothesis.

To make the hypothesis working and of high quality, be sure you select both independent and dependent variables and add them to the statement. Examine the relationships of these elements. Think if you can prove them and explain them in further research.

2. Is there a maximum number of hypotheses that is allowed in one research paper?

You can write as many hypotheses as you want for your paper, because it all depends on your view on the topic and the desire to develop it in several directions. The main thing is that your project shouldn't be overloaded with too many hypotheses and that you pay enough attention to each of them.

3. How do I test my hypothesis?

It’s easy to test the statement before you write a hypothesis for a research proposal. Do an experiment: ask your question and try answering it. If you succeed, this assignment can be used for more detailed study.

4. How long is a hypothesis?

While writing the hypothesis, you must make it as direct as possible and, at the same time, clear it of extraneous judgments. Typically, it's 20 words long. We don’t recommend exceeding this volume, so as not to face difficulties in interpretation.

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Research Paper Guide

How To Write A Hypothesis

Last updated on: Feb 8, 2023

How to Write a Hypothesis - A Step-by-Step Guide

By: Nathan D.

Reviewed By: Rylee W.

Published on: Jul 16, 2019

A hypothesis is generally a statement that a researcher has to test through scientific methods subjectively. Unlike a thesis statement, a hypothesis does not require a researcher to prove it right in any circumstance.

It is a statement that is developed prior to research, experiment, or data collection. In simple words, it is a proposed explanation for any idea, study, or phenomenon.

For  research paper writing , thesis, case studies, or dissertation, you will have to write a hypothesis first. Continue reading the article to learn how to write a good hypothesis effectively.

On this Page

What is a Hypothesis?

A hypothesis is a proposed or supposition explanation that a researcher forms based on limited reference about a specific phenomenon. This statement is further investigated to analyze its validity and significance.

A hypothesis statement is an initial point from where an investigation begins. Moreover, it translates the major research question into a prediction.

In professional terms, a hypothesis is an idea whose merit requires evaluation and interpretation. For this purpose, the researcher needs to define the specifics of the hypothesis in operational terms.

It requires a researcher to study in detail whether to approve it or disapprove of it. In this process, the hypothesis either becomes a part of the theory, or a theory itself.

Functions of Hypothesis

Learning the correct writing procedure is not enough if you are not aware of the basic functions that your hypothesis performs. To make your hypothesis stand out, understand the below-given functions

• First and foremost, a hypothesis contributes to making your research, observation, and experiment possible
• This helps in starting the basic investigation about the subject
• It verifies the observation
• It provides the right direction to your inquiries

Components of a Hypothesis

Like other sentences and statements, a hypothesis has major components that play a significant role in making it impactful. It is essential to learn about these parts when you are researching academics.

The following are the different components of the hypothesis:

How to Write a Hypothesis?

Just like every other formal or academic task, writing a hypothesis includes a process. Although there are no set of rules to follow while developing a hypothesis.

However, it is recommended that you follow some steps to ensure a quality statement. These steps will make it easier for you to formulate a strong hypothesis to provide a great direction for your research.

Following is a step-by-step- procedure to write a hypothesis.

1. Develop a Question

When writing a hypothesis, the first thing is to develop a research question that you want to answer in your research. The question that you will formulate should be specific, focused, and researchable within the constraints of your assignment.

2. Conduct a Basic Research

The initial answer to your research question will be spontaneous and based on pre-existing knowledge about the subject. Search for theories and information to form a basic assumption that you will investigate further.

At this stage of creating a hypothesis, a researcher can develop a conceptual framework to identify the variables and their relationship.

3. Develop a Rough Hypothesis

Formulate a rough statement on the available knowledge to provide an idea about what to expect from your research. Brainstorm the answer for this raw question and present it into a clear and concise sentence.

4. Refine the Statement Made

Now that you have a rough statement in hand, it is time to refine and make it a testable hypothesis. There are several ways to shape your hypothesis, but you can arrange your statement keeping in view the parts.

Make sure that your refined statement must contain the following things:

• Relevant variables
• The group being studied
• The predicted result of the research or experiment

5. Phrasing the Hypothesis

The hypothesis can be phrased in three ways. Depending on the requirement of the research and the field type, select a phrasing pattern.

• To phrase the hypothesis, identify the variables, use a simple prediction pattern of “if...then” form. Present the independent variable first and then the dependent variable in your hypothesis statement.
• When developing a hypothesis for academic research, you can choose correlation and effect phrasing. In this way, you directly present the predicted relationship between the two variables.
• If the statement compares the two groups, the paraphrasing of the hypothesis can be done by stating the expected difference.

6. Write a Null Hypothesis

If your research is based on statistical testing of the hypothesis, you will have to present a null hypothesis. The null hypothesis states that there is no relationship or association between the two variables.

How to Write a Null Hypothesis?

There are two types of hypotheses, the null hypothesis, and the alternative hypothesis. A null hypothesis states that there is no difference between certain characteristics of a population while an alternative hypothesis states otherwise.

So, how does a null hypothesis work? Below is a four-step process to come up with a null hypothesis.

• The analyst will come up with two hypotheses and test them.
• Next, he formulates an analysis plan and decides the ways through which those hypotheses would be analyzed.
• The sample data and hypotheses are evaluated and analyzed.
• The final step is to analyze the acquired results and decide whether the null hypothesis is correct or not.

Other than null analysis, alternative hypotheses are also used. An alternative hypothesis is opposite to the null hypothesis and they are independent of each other.

What kind of Sources should I Add to my Hypothesis

It is important to look for credible and relevant sources of information while writing a hypothesis for your  research proposal . A researcher has to consult these sources to check the reliability and validity of your primary idea.

In case you are wondering what sources will work best for your hypothesis, check out the following:

• Find relevant phenomena that have some resemblance to yours
• Evaluate the studies and observations from the past
• Analyze what the current time has to say about the idea
• Search the competitor’s ideas and opinions
• Analyze scientific theories
• Dig deeper into the patterns that influence people and their thinking

Types of Hypothesis

Depending on the field and  research methods  to collect data, hypotheses can have different types. When writing a research paper, it is essential to know all the types well to form a strong and relevant hypothesis.

Following are the six main types of hypothesis:

• Simple Hypothesis  - A simple hypothesis is a statement that shows a relationship between two variables; an independent and dependent variable. For example, doing exercise can help you lose weight faster. Here doing exercise is an independent variable while losing weight is dependent.
• Complex Hypothesis  - A complex hypothesis presents a relationship between two or more dependent and independent variables. For example, exercising and eating lots of vegetables can reduce weight and other fatal diseases such as heart disease.
• Directional Hypothesis  - A directional hypothesis is a statement that presents the researcher’s commitment to a particular result. Moreover, the relationship between different variables also predicts its nature. For example, people who are sleep-deprived for 24 hours will have more cold symptoms than those who oversleep.
• Non-Directional Hypothesis  - A non-directional hypothesis is used when there is no theory involved. It shows an existing relationship between two variables without highlighting the exact relationship’s nature.
• Null Hypothesis  - A null hypothesis states that there is no relationship between the two variables. Similarly, it also contended that there is not enough information to state the scientific hypothesis. The ‘H0’ symbol denotes this hypothesis.
• Alternative Hypothesis  - It is a statement that the research forms when he disapproves the null hypothesis. As the name suggests, it is an alternative statement to your null hypothesis highlighting the relationship between the variables. It is denoted by ‘H1’.
• Associative and Causal Hypothesis  - In an associative hypothesis, a change in one variable results in a difference in the other variable. On the other hand, the causal hypothesis presents a cause and effect interaction between the two variables.

Characteristics of a Good Hypothesis

Professional writers believe that a hypothesis has certain features that help it become stronger and more effective. These characteristics include:

• To make the hypothesis credible, it should be clear and precise
• If you have chosen a hypothesis type that will state the relationship between the two variables, it should be obvious
• A strong hypothesis is specific and has clear scope for conducting more studies and tests
• The explanation of the hypothesis must be simple. Keep in mind that the simplicity of the hypothesis has nothing to do with its significance

Only a strong hypothesis will motivate the readers to read the entire paper. So make sure that you carefully develop a hypothesis for your research.

Hypothesis Examples

If you are writing a paper for the first time, it is suggested by professionals to go through a few examples. It will help you understand the pattern in which you should be working.

Below-given are examples of how hypotheses are developed for different research experiments.

HOW TO WRITE A HYPOTHESIS FOR RESEARCH PAPER

HOW TO WRITE A HYPOTHESIS FOR SCIENTIFIC RESEARCH

Going through these examples will help you understand better which course of action should be chosen for your research. If it is still difficult for you to look for sources and write a compelling hypothesis, get help from professionals.

5StarEssays.com is a professional ‘ write my essay for me? ’ service that provides different academic writing services. Whether you are looking for an expert to write a compelling essay or any form of paper, we have your back.

Our experts draft all your assignments carefully, ensuring that each part is well-written and structured. Get your assignment today by simply placing your order at the most affordable price.

Frequently Asked Questions

How do you turn a question into a hypothesis.

You can change and transform a question into a hypothesis by changing it into a statement.

Is a hypothesis a prediction?

No, a hypothesis is not a prediction but rather a possibility. The researcher ‘hopes’ to obtain a certain kind of result through the experimentation. This possibility or expected results are the hypothesis.

Can a hypothesis be a question?

No, a hypothesis is and should be a statement and not a question.

Do all research papers have a hypothesis?

No, some research papers are based on exploratory research, which is used to develop the hypothesis. So, such a research paper does not need a hypothesis.

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Hypothesis: Basic Research Guidelines & Examples

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A hypothesis refers to a simple statement that predicts the findings of a research study. Basically, researchers develop propositions to provide tentative answers to research questions that address different aspects of a study objective. In writing, a scholar must use existing theories and knowledge to create a valid assumption. Besides, a researcher focuses on testing supposed claims through different methods, like experiments, observations, and statistical analysis of obtained data. In practice, the findings from a study can either support or refute a premise under examination. Then, when writing a suggestion, scholars should conduct adequate research on a specific topic, brainstorm for ideas, draft an assertion, revise a draft claim, and write a final sentence in simple language. Moreover, these steps lead to a valid development of accurate and precise propositions that identify relationships between independent and dependent variables. In practice, one should rely on a cause-and-effect theory when developing a hypothesis.

General Aspects

A good hypothesis suggests a sentence as a statement that gives a particular prediction about the findings of a research study. Basically, people make a specific hypothesis, which acts as a tentative answer to a research question. However, a proposition may lack scientific or scholarly proof. Then, a reasonable claim must address different aspects of a question under analysis. In writing, people must base their propositions on existing theories and knowledge. Besides, such a statement has to be testable through various methods, like experiments, observations, and statistical analysis. In practice, the findings from a study can either support or refute a working thesis. Therefore, writing a study assumption refers to a simple and clear statement that tries to predict the results of research.

What Is a Hypothesis and Its Purpose

According to its definition, a hypothesis is a testable statement or prediction about a specific phenomenon or a relationship between two or more variables, forming a unique basis for scientific investigation. In principle, such a statement is formulated based on observations, existing knowledge, and theoretical frameworks. For example, the main purpose of writing a hypothesis is to establish a specific direction for a particular study, enabling researchers to design experiments and collect data in a structured manner (Reichardt, 2022). Moreover, by testing a defined assertion through experimentation and analysis, people can determine whether their predictions hold true, contributing to a broader understanding of a discussed topic. This process of hypothesis testing is fundamental to a scientific method, as it allows for a particular validation, refinement, or rejection of theoretical concepts (Dillard & Flenner, 2021). In turn, the length of a hypothesis depends on academic levels and scopes of research, while general writing guidelines are:

High School

• Length: 1 sentence
• Word Count: 10-15 words
• Detail: A simple, clear, and testable statement about a specific relationship between variables.
• Example: “If plants are watered with different types of liquids, then the growth rate will vary.”

College (Undergraduate)

• Word Count: 15-30 words
• Detail: More detailed, specifying defined variables and expected outcomes with writing some contextual background.
• Example: “Students who study in a quiet environment will perform better on exams than those who study in a noisy environment due to fewer distractions and improved concentration.”

University (Advanced Undergraduate or Honors Thesis)

• Word Count: 20-35 words
• Detail: Includes specific variables, a rationale based on preliminary research, and a clear expected outcome.
• Example: “Exposure to natural light in the workplace will increase employee productivity compared to artificial lighting, as natural light has been shown to improve mood and energy levels.”

Master’s Thesis

• Length: 1-2 sentences
• Word Count: 25-40 words
• Detail: Detailed and precise writing, including variables, expected relationship, and grounding in existing literature or theoretical framework.
• Example: “Implementing a flipped classroom model in undergraduate biology courses will result in higher student engagement and academic performance, as this model promotes active learning and individualized instruction, which have been positively correlated with student outcomes in previous studies.”

Ph.D. Dissertation

• Length: 2-4 sentences (or more, if necessary)
• Word Count: 30-70+ words
• Detail: Highly detailed, specifying complex relationships between multiple variables, grounded in extensive literature review and theoretical framework, with writing about clear and expected outcomes.
• Example: “Incorporating machine learning algorithms into predictive models for climate change will significantly improve an overall accuracy of long-term weather forecasts. This assumption is based on a premise that machine learning can identify innovative patterns in large datasets that traditional statistical methods may miss, thus providing more reliable predictions of climatic phenomena.”

Note: Some writing components of a hypothesis can be added, deleted, or combined with each other, and such a statement is usually 1 sentence long. For example, the three main parts of a hypothesis statement are an independent variable, a dependent variable, and a predicted relationship between them (Lund, 2021). In a research paper, a standard hypothesis is typically found in an introduction section, where such a statement outlines an expected relationship between variables and sets a particular stage for an entire study. Further on, a hypothesis is a testable prediction about a unique relationship between variables, while a research question is a broad query that guides an entire investigation into a specific topic (Misra et al., 2021). Moreover, there is a direct relationship between a hypothesis and research objectives, as the former provides a specific, testable prediction that aligns with and helps to achieve broader goals outlined by the latter. In writing, a basic checklist to evaluate an overall effectiveness of any hypothesis includes ensuring a research assertion is a clear, specific, and testable statement that is based on existing knowledge and covers both independent and dependent variables (Rubin & Donkin, 2022). Finally, to start a hypothesis, people begin by writing an “If” statement that clearly identifies an independent variable, followed by a “then” statement predicting a specific outcome or effect on a dependent variable.

Independent and Dependent Variables

A hypothesis in some studies must contain independent and dependent variables. Basically, hypothesis testing is a statistical method that people use to determine a specific connection between suggestions and their alternative outcomes to understand what is true or not and write about them. For example. experimental and correlational studies examine relationships between two or more variables (Sharang, 2020). In turn, independent elements refer to factors people can control or change. Besides, this aspect refers to factors scholars observe or measure for their writing. Then, a null hypothesis of experimental and correlational studies must predict relationships between dependent and independent variables. Moreover, such predictions should not be guesses but should contain evidence from research studies.

There are different types of hypotheses people can develop for writing their studies. In this case, common types of hypotheses include:

• A simple hypothesis refers to predictions of relationships between independent and dependent variables.
• A complex hypothesis predicts relationships between two or more independent and dependent variables.
• An empirical hypothesis is a working prediction that exists when a person tests a specific theory by using observations and experiments. Basically, this type of assertion goes through some trial and error methods to obtain the necessary findings and write about them. In some instances, people may change some aspects around other elements.
• A null hypothesis , denoted as H 0 , exists when a person believes a relationship does not exist between independent and dependent variables. Basically, this statement may exist when an individual lacks adequate information to make a scientific prediction. Besides, inferences made from the findings attempt to disapprove or discredit a null theory.
• An alternative hypothesis , denoted as H 1 , attempts to disapprove a null proposition. In this case, people attempt to discover or affirm an alternative proposition.
• A logical hypothesis refers to a proposed explanation of a concept that contains limited evidence. In writing, investigators intend to turn a reasonable assumption into an empirical claim. Besides, they put theories or postulate them to a particular testing process.
• A statistical hypothesis is a claim related to studies that examine a section of a specific population. In this case, people identify a sample population and study their behaviors related to a given research question. 

Steps on How to Write a Good Hypothesis

To write a good hypothesis, people clearly define specific independent and dependent variables and formulate a testable prediction about a particular relationship between them, often structured as an “If [independent variable], then [dependent variable]” statement. As such, researchers should focus on developing and writing reasonable assertation statements for their studies. For example, one should consider different factors that relate to existing studies or theories (Sharang, 2020). In writing, some predictions should pertain to research data and provide tentative answers to study questions. Hence, the following are essential writing steps a person should consider when developing a proposition.

Step 1: Researching

A first step in developing a hypothesis is to research and gather details related to writing an intended topic. Basically, researching allows a scholar to gain more knowledge concerning issues and factors and how variables change. For example, to form a hypothesis sentence, people start wording by identifying independent and dependent variables, reviewing existing literature, and then creating a clear, testable prediction that outlines an expected relationship between defined elements (Reichardt, 2022). Besides, this step will enable people to become familiar with the expected results. As a result, an entire writing process influences a relevant theory’s development.

Step 2: Asking Questions

A person should develop research questions before developing a main claim. For instance, investigators should create scientific questions that relate to studied and identified elements (Dillard & Flenner, 2021). In writing, brainstorming enhances a particular ability to determine relationships between independent and dependent variables. Basically, successful scholars remain focused on writing about one cause-and-effect theory to ensure they develop accurate ideas for a prediction. Therefore, a second writing step in developing a proposition is to brainstorm questions that reveal a specific relationship between independent and dependent elements. 

Step 3: Use Clear Language

Scholars should use simple and clear language when developing any suggestion for writing a study. For instance, one should draft concise predictions that answer developed research questions (Sharang, 2020). In practice, one should write a hypothesis in a particular form of a direct proposition that an action leads to a specific result. Futher on, the three main words that should be in a hypothesis statement are “if,” “then,” and “because.” Moreover, a person should not state a supposition as a question but as an affirmative statement that predicts outcomes from a particular course of action. Therefore, a third step in developing a new theory involves selecting a simple language for writing scientific predictions. 

Step 4. Revising a Statement

A scholar should revise a draft hypothesis to ensure writing any prediction makes a testable thesis through research and experimentation. For instance, a person should review a prediction to ensure such a sentence captures relationships between at least two elements (Dillard & Flenner, 2021). Hence, a scholar must revise a drafted proposition to ensure this statement captures a testable relationship between independent and dependent variables. In writing, some examples of sentence starters for beginning a hypothesis statement are:

• If [independent variable] is introduced or modified, then we anticipate [dependent variable] will exhibit a measurable change in terms of … .
• It is hypothesized that a particular presence or alteration of [independent element] will significantly affect [dependent element] by causing … .
• We predict that variations in [independent aspect] will result in corresponding changes in [dependent aspect], specifically in key aspects of … .
• A formulated assumption posits that altering [independent variable] will lead to observable differences in [dependent variable], particularly in relation to … .
• Based on existing theories and previous research, it is expected that modifying [independent element] will impact [dependent element] by … .
• We hypothesize an increase or decrease in [independent aspect], which will have a direct influence on [dependent aspect], leading to … .
• It is proposed that a particular manipulation of [independent variable] will result in [specific outcome] within [dependent variable] due to a specific mechanism of … .
• If [independent element] is systematically varied, then [dependent element] will demonstrate a change characterized by … .
• A current premise suggests that specific changes in [independent aspect] will cause predictable alterations in [dependent aspect], which can be measured by … .
• We expect that, by introducing [independent variable], there will be a significant impact on [dependent variable], as evidenced by changes in … .

• Research question – How does divorce affect sociological development among young children?
• H 0 – Challenges that lead to divorce hurt young children’s social development, which affects their ability to interact with other people. 
• H 1 – Most children manage to cope with domestic challenges that lead to divorce, enabling them to realize healthy sociological development.
• Research question – How did tenebrism influence baroque art during the 16 th and 17 th centuries?
• H 0 – A particular origin of tenebrism had a positive impact on a dynamic appearance of baroque art.
• H 1 – Baroque art emerged as a unique art that did not have any form of external influence.
• Research question – To what extent does geological activity affect the Earth?
• H 0 – A specific movement of tectonic plates beneath the Earth’s surface results in volcanic eruptions and faults that lead to mountains and lift valleys.  
• H 1 – Mountains and valleys are natural features with little connection with geological activities like a particular movement of tectonic plates beneath the Earth’s surface.
• Research question – Do animals have rights and welfare in society?
• H 0 – Wild and domestic animals are living creatures with a right to care and protection by humans.
• H 1 – Wild and domestic animals are subordinate to humans, which implies they do not have a right to care and protection.  
• Research question – Does a specific consumption of genetically modified plants cause health complications in humans?
• H 0 – Genetically modified foods are safe for human consumption and do not pose any possible health risks.
• H 1 – Genetically modified foods interfere with healthy cell development, which leads to health complications.

Indigenous Studies

• Research question – What role does culture play among Indigenous communities?
• H 0 – Cultural practices among Aboriginals promote their identity and contribute to the members’ overall well-being.
• H 1 – cultural practices among Aboriginals do not significantly contribute to an overall quality of their lives.
• Research question – Does fascism exist in the twenty-first century?
• H 0 – Established forms of democracy in the twenty-first century do not allow political leaders to implement all the fascism elements.
• H 1 – Some political leaders in the twenty-first century adopt radical policies that promote a particular existence of fascism.
• Research question – Do neutrons have mass?
• H 0 – Neutrons are small particles that have masses.
• H 1 – Neutrons are small particles whose weight remains insignificant.

Health Studies

• Research question – How do evidence-based treatment approaches enhance an overall quality of current treatments?
• H 0 – Evidence-based treatment methods allow doctors to gather adequate and accurate information about patients, which helps them to tailor treatment and care approaches to meet people’s needs.
• H 1 – Evidence-based approaches do not enhance an overall quality of current treatments since they lead to inconsistency in the care and medications given to a patient.

Environmental Studies

• Research question – To what extent do human activities contribute to global warming?
• H 0 – Most human activities release greenhouse gases into the atmosphere, which results in a particular rise in average temperatures.
• H 1 – Most human activities release insignificant amounts of greenhouse gases into the atmosphere, contributing to global warming.

What to Include

Common Mistakes

• Being Too Vague: A hypothesis statement must be specific and clearly define corresponding variables and expected outcomes.
• Not Being Testable: A prediction should be formulated in a way that can be empirically tested through experiments or observations.
• Lack of Clarity: Avoid writing in ambiguous language since a scientific assertion should be clear and straightforward.
• Being Too Complex: Keep a statement focused on a single relationship between variables to avoid confusion.
• Ignoring Prior Research: Writing a good premise is based on existing knowledge and theories, while ignoring these concepts can lead to redundant or invalid studies.
• Using Subjective Terms: Avoid writing terms that are open to interpretation because any prediction should be objective and measurable.
• Making Assumptions: Do not assume outcomes without evidence since any assertion should be based on logical reasoning.
• Not Aligning With a Research Question: Ensure a formulated prediction directly addresses a study question posed.
• Being Too Broad: Writing a hypothesis statement should be narrow enough to be manageable within a specific scope of research.
• Neglecting to Include Variables: Clearly identify and define both independent and dependent variables in a given assertion.

Writing Tips

In its simple definition, a basic hypothesis gives a specific prediction about the findings of a research paper or study. Basically, people develop scientific predictions to provide tentative answers to study questions (Reichardt, 2022). In turn, some of the factors one must consider when writing an assumption statement include:

• conduct adequate research on a specific topic;
• brainstorm for ideas;
• draft a statement;
• revise a draft proposition;
• write a final assertion in simple language.

A hypothesis is a statement predicting a specific outcome of a research study, which is based on existing theories, literature, and knowledge. Basically, writing such a statement includes independent and dependent variables and must be testable through experiments, observations, or statistical analysis. Further on, common types of hypotheses include simple, complex, empirical, null, alternative, logical, and statistical writing formats. To develop a good hypothesis, one should research a specific topic, ask relevant questions, use clear language, and revise a formulated statement to ensure its writing captures a direct relationship between variables. Finally, accurate assumptions help in identifying cause-and-effect relationships in research.

Dillard, A., & Flenner, J. (2021). Crush hypothesis testing . Happy Hypotenuse Publishing.

Lund, T. (2021). Research problems and hypotheses in empirical research. Scandinavian Journal of Educational Research , 66 (7), 1183–1193. https://doi.org/10.1080/00313831.2021.1982765

Misra, D. P., Gasparyan, A. Y., Zimba, O., Yessirkepov, M., Agarwal, V., & Kitas, G. D. (2021). Formulating hypotheses for different study designs. Journal of Korean Medical Science , 36 (50), 1–9. https://doi.org/10.3346/jkms.2021.36.e338

Reichardt, C. S. (2022). The method of multiple hypotheses: A guide for professional and academic researchers . Routledge, Taylor & Francis Group.

Rubin, M., & Donkin, C. (2022). Exploratory hypothesis tests can be more compelling than confirmatory hypothesis tests. Philosophical Psychology , 1–29. https://doi.org/10.1080/09515089.2022.2113771

Sharang, S. (2020). Research methodology techniques: Understanding how to write, present and defend any research report . Stephen Sharang.

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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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What Is a Hypothesis and How Do I Write One?

General Education

Think about something strange and unexplainable in your life. Maybe you get a headache right before it rains, or maybe you think your favorite sports team wins when you wear a certain color. If you wanted to see whether these are just coincidences or scientific fact, you would form a hypothesis, then create an experiment to see whether that hypothesis is true or not.

But what is a hypothesis, anyway? If you’re not sure about what a hypothesis is--or how to test for one!--you’re in the right place. This article will teach you everything you need to know about hypotheses, including: 

• Defining the term “hypothesis” 
• Providing hypothesis examples 
• Giving you tips for how to write your own hypothesis

So let’s get started!

What Is a Hypothesis?

Merriam Webster defines a hypothesis as “an assumption or concession made for the sake of argument.” In other words, a hypothesis is an educated guess . Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it’s true or not. Keep in mind that in science, a hypothesis should be testable. You have to be able to design an experiment that tests your hypothesis in order for it to be valid. 

As you could assume from that statement, it’s easy to make a bad hypothesis. But when you’re holding an experiment, it’s even more important that your guesses be good...after all, you’re spending time (and maybe money!) to figure out more about your observation. That’s why we refer to a hypothesis as an educated guess--good hypotheses are based on existing data and research to make them as sound as possible.

Hypotheses are one part of what’s called the scientific method .  Every (good) experiment or study is based in the scientific method. The scientific method gives order and structure to experiments and ensures that interference from scientists or outside influences does not skew the results. It’s important that you understand the concepts of the scientific method before holding your own experiment. Though it may vary among scientists, the scientific method is generally made up of six steps (in order):

• Observation
• Asking questions
• Forming a hypothesis
• Analyze the data
• Communicate your results

You’ll notice that the hypothesis comes pretty early on when conducting an experiment. That’s because experiments work best when they’re trying to answer one specific question. And you can’t conduct an experiment until you know what you’re trying to prove!

Independent and Dependent Variables 

After doing your research, you’re ready for another important step in forming your hypothesis: identifying variables. Variables are basically any factor that could influence the outcome of your experiment . Variables have to be measurable and related to the topic being studied.

There are two types of variables:  independent variables and dependent variables. I ndependent variables remain constant . For example, age is an independent variable; it will stay the same, and researchers can look at different ages to see if it has an effect on the dependent variable. 

Speaking of dependent variables... dependent variables are subject to the influence of the independent variable , meaning that they are not constant. Let’s say you want to test whether a person’s age affects how much sleep they need. In that case, the independent variable is age (like we mentioned above), and the dependent variable is how much sleep a person gets. 

Variables will be crucial in writing your hypothesis. You need to be able to identify which variable is which, as both the independent and dependent variables will be written into your hypothesis. For instance, in a study about exercise, the independent variable might be the speed at which the respondents walk for thirty minutes, and the dependent variable would be their heart rate. In your study and in your hypothesis, you’re trying to understand the relationship between the two variables.

Elements of a Good Hypothesis

The best hypotheses start by asking the right questions . For instance, if you’ve observed that the grass is greener when it rains twice a week, you could ask what kind of grass it is, what elevation it’s at, and if the grass across the street responds to rain in the same way. Any of these questions could become the backbone of experiments to test why the grass gets greener when it rains fairly frequently.

As you’re asking more questions about your first observation, make sure you’re also making more observations . If it doesn’t rain for two weeks and the grass still looks green, that’s an important observation that could influence your hypothesis. You'll continue observing all throughout your experiment, but until the hypothesis is finalized, every observation should be noted.

Finally, you should consult secondary research before writing your hypothesis . Secondary research is comprised of results found and published by other people. You can usually find this information online or at your library. Additionally, m ake sure the research you find is credible and related to your topic. If you’re studying the correlation between rain and grass growth, it would help you to research rain patterns over the past twenty years for your county, published by a local agricultural association. You should also research the types of grass common in your area, the type of grass in your lawn, and whether anyone else has conducted experiments about your hypothesis. Also be sure you’re checking the quality of your research . Research done by a middle school student about what minerals can be found in rainwater would be less useful than an article published by a local university.

Writing Your Hypothesis

Once you’ve considered all of the factors above, you’re ready to start writing your hypothesis. Hypotheses usually take a certain form when they’re written out in a research report.

When you boil down your hypothesis statement, you are writing down your best guess and not the question at hand . This means that your statement should be written as if it is fact already, even though you are simply testing it.

The reason for this is that, after you have completed your study, you'll either accept or reject your if-then or your null hypothesis. All hypothesis testing examples should be measurable and able to be confirmed or denied. You cannot confirm a question, only a statement! 

In fact, you come up with hypothesis examples all the time! For instance, when you guess on the outcome of a basketball game, you don’t say, “Will the Miami Heat beat the Boston Celtics?” but instead, “I think the Miami Heat will beat the Boston Celtics.” You state it as if it is already true, even if it turns out you’re wrong. You do the same thing when writing your hypothesis.

Additionally, keep in mind that hypotheses can range from very specific to very broad.  These hypotheses can be specific, but if your hypothesis testing examples involve a broad range of causes and effects, your hypothesis can also be broad.  

The Two Types of Hypotheses

Now that you understand what goes into a hypothesis, it’s time to look more closely at the two most common types of hypothesis: the if-then hypothesis and the null hypothesis.

#1: If-Then Hypotheses

First of all, if-then hypotheses typically follow this formula:

If ____ happens, then ____ will happen.

The goal of this type of hypothesis is to test the causal relationship between the independent and dependent variable. It’s fairly simple, and each hypothesis can vary in how detailed it can be. We create if-then hypotheses all the time with our daily predictions. Here are some examples of hypotheses that use an if-then structure from daily life: 

• If I get enough sleep, I’ll be able to get more work done tomorrow.
• If the bus is on time, I can make it to my friend’s birthday party. 
• If I study every night this week, I’ll get a better grade on my exam. 

In each of these situations, you’re making a guess on how an independent variable (sleep, time, or studying) will affect a dependent variable (the amount of work you can do, making it to a party on time, or getting better grades). 

You may still be asking, “What is an example of a hypothesis used in scientific research?” Take one of the hypothesis examples from a real-world study on whether using technology before bed affects children’s sleep patterns. The hypothesis read s:

“We hypothesized that increased hours of tablet- and phone-based screen time at bedtime would be inversely correlated with sleep quality and child attention.”

It might not look like it, but this is an if-then statement. The researchers basically said, “If children have more screen usage at bedtime, then their quality of sleep and attention will be worse.” The sleep quality and attention are the dependent variables and the screen usage is the independent variable. (Usually, the independent variable comes after the “if” and the dependent variable comes after the “then,” as it is the independent variable that affects the dependent variable.) This is an excellent example of how flexible hypothesis statements can be, as long as the general idea of “if-then” and the independent and dependent variables are present.

#2: Null Hypotheses

Your if-then hypothesis is not the only one needed to complete a successful experiment, however. You also need a null hypothesis to test it against. In its most basic form, the null hypothesis is the opposite of your if-then hypothesis . When you write your null hypothesis, you are writing a hypothesis that suggests that your guess is not true, and that the independent and dependent variables have no relationship .

One null hypothesis for the cell phone and sleep study from the last section might say: 

“If children have more screen usage at bedtime, their quality of sleep and attention will not be worse.” 

In this case, this is a null hypothesis because it’s asking the opposite of the original thesis! 

Conversely, if your if-then hypothesis suggests that your two variables have no relationship, then your null hypothesis would suggest that there is one. So, pretend that there is a study that is asking the question, “Does the amount of followers on Instagram influence how long people spend on the app?” The independent variable is the amount of followers, and the dependent variable is the time spent. But if you, as the researcher, don’t think there is a relationship between the number of followers and time spent, you might write an if-then hypothesis that reads:

“If people have many followers on Instagram, they will not spend more time on the app than people who have less.”

In this case, the if-then suggests there isn’t a relationship between the variables. In that case, one of the null hypothesis examples might say:

“If people have many followers on Instagram, they will spend more time on the app than people who have less.”

You then test both the if-then and the null hypothesis to gauge if there is a relationship between the variables, and if so, how much of a relationship. 

4 Tips to Write the Best Hypothesis

If you’re going to take the time to hold an experiment, whether in school or by yourself, you’re also going to want to take the time to make sure your hypothesis is a good one. The best hypotheses have four major elements in common: plausibility, defined concepts, observability, and general explanation.

#1: Plausibility

At first glance, this quality of a hypothesis might seem obvious. When your hypothesis is plausible, that means it’s possible given what we know about science and general common sense. However, improbable hypotheses are more common than you might think. 

Imagine you’re studying weight gain and television watching habits. If you hypothesize that people who watch more than  twenty hours of television a week will gain two hundred pounds or more over the course of a year, this might be improbable (though it’s potentially possible). Consequently, c ommon sense can tell us the results of the study before the study even begins.

Improbable hypotheses generally go against  science, as well. Take this hypothesis example: 

“If a person smokes one cigarette a day, then they will have lungs just as healthy as the average person’s.” 

This hypothesis is obviously untrue, as studies have shown again and again that cigarettes negatively affect lung health. You must be careful that your hypotheses do not reflect your own personal opinion more than they do scientifically-supported findings. This plausibility points to the necessity of research before the hypothesis is written to make sure that your hypothesis has not already been disproven.

#2: Defined Concepts

The more advanced you are in your studies, the more likely that the terms you’re using in your hypothesis are specific to a limited set of knowledge. One of the hypothesis testing examples might include the readability of printed text in newspapers, where you might use words like “kerning” and “x-height.” Unless your readers have a background in graphic design, it’s likely that they won’t know what you mean by these terms. Thus, it’s important to either write what they mean in the hypothesis itself or in the report before the hypothesis.

Here’s what we mean. Which of the following sentences makes more sense to the common person?

If the kerning is greater than average, more words will be read per minute.

If the space between letters is greater than average, more words will be read per minute.

For people reading your report that are not experts in typography, simply adding a few more words will be helpful in clarifying exactly what the experiment is all about. It’s always a good idea to make your research and findings as accessible as possible. 

Good hypotheses ensure that you can observe the results. 

#3: Observability

In order to measure the truth or falsity of your hypothesis, you must be able to see your variables and the way they interact. For instance, if your hypothesis is that the flight patterns of satellites affect the strength of certain television signals, yet you don’t have a telescope to view the satellites or a television to monitor the signal strength, you cannot properly observe your hypothesis and thus cannot continue your study.

Some variables may seem easy to observe, but if you do not have a system of measurement in place, you cannot observe your hypothesis properly. Here’s an example: if you’re experimenting on the effect of healthy food on overall happiness, but you don’t have a way to monitor and measure what “overall happiness” means, your results will not reflect the truth. Monitoring how often someone smiles for a whole day is not reasonably observable, but having the participants state how happy they feel on a scale of one to ten is more observable. 

In writing your hypothesis, always keep in mind how you'll execute the experiment.

#4: Generalizability 

Perhaps you’d like to study what color your best friend wears the most often by observing and documenting the colors she wears each day of the week. This might be fun information for her and you to know, but beyond you two, there aren’t many people who could benefit from this experiment. When you start an experiment, you should note how generalizable your findings may be if they are confirmed. Generalizability is basically how common a particular phenomenon is to other people’s everyday life.

Let’s say you’re asking a question about the health benefits of eating an apple for one day only, you need to realize that the experiment may be too specific to be helpful. It does not help to explain a phenomenon that many people experience. If you find yourself with too specific of a hypothesis, go back to asking the big question: what is it that you want to know, and what do you think will happen between your two variables?

Hypothesis Testing Examples

We know it can be hard to write a good hypothesis unless you’ve seen some good hypothesis examples. We’ve included four hypothesis examples based on some made-up experiments. Use these as templates or launch pads for coming up with your own hypotheses.

Experiment #1: Students Studying Outside (Writing a Hypothesis)

You are a student at PrepScholar University. When you walk around campus, you notice that, when the temperature is above 60 degrees, more students study in the quad. You want to know when your fellow students are more likely to study outside. With this information, how do you make the best hypothesis possible?

You must remember to make additional observations and do secondary research before writing your hypothesis. In doing so, you notice that no one studies outside when it’s 75 degrees and raining, so this should be included in your experiment. Also, studies done on the topic beforehand suggested that students are more likely to study in temperatures less than 85 degrees. With this in mind, you feel confident that you can identify your variables and write your hypotheses:

If-then: “If the temperature in Fahrenheit is less than 60 degrees, significantly fewer students will study outside.”

Null: “If the temperature in Fahrenheit is less than 60 degrees, the same number of students will study outside as when it is more than 60 degrees.”

These hypotheses are plausible, as the temperatures are reasonably within the bounds of what is possible. The number of people in the quad is also easily observable. It is also not a phenomenon specific to only one person or at one time, but instead can explain a phenomenon for a broader group of people.

To complete this experiment, you pick the month of October to observe the quad. Every day (except on the days where it’s raining)from 3 to 4 PM, when most classes have released for the day, you observe how many people are on the quad. You measure how many people come  and how many leave. You also write down the temperature on the hour. 

After writing down all of your observations and putting them on a graph, you find that the most students study on the quad when it is 70 degrees outside, and that the number of students drops a lot once the temperature reaches 60 degrees or below. In this case, your research report would state that you accept or “failed to reject” your first hypothesis with your findings.

Experiment #2: The Cupcake Store (Forming a Simple Experiment)

Let’s say that you work at a bakery. You specialize in cupcakes, and you make only two colors of frosting: yellow and purple. You want to know what kind of customers are more likely to buy what kind of cupcake, so you set up an experiment. Your independent variable is the customer’s gender, and the dependent variable is the color of the frosting. What is an example of a hypothesis that might answer the question of this study?

Here’s what your hypotheses might look like: 

If-then: “If customers’ gender is female, then they will buy more yellow cupcakes than purple cupcakes.”

Null: “If customers’ gender is female, then they will be just as likely to buy purple cupcakes as yellow cupcakes.”

This is a pretty simple experiment! It passes the test of plausibility (there could easily be a difference), defined concepts (there’s nothing complicated about cupcakes!), observability (both color and gender can be easily observed), and general explanation ( this would potentially help you make better business decisions ).

Experiment #3: Backyard Bird Feeders (Integrating Multiple Variables and Rejecting the If-Then Hypothesis)

While watching your backyard bird feeder, you realized that different birds come on the days when you change the types of seeds. You decide that you want to see more cardinals in your backyard, so you decide to see what type of food they like the best and set up an experiment. 

However, one morning, you notice that, while some cardinals are present, blue jays are eating out of your backyard feeder filled with millet. You decide that, of all of the other birds, you would like to see the blue jays the least. This means you'll have more than one variable in your hypothesis. Your new hypotheses might look like this: 

If-then: “If sunflower seeds are placed in the bird feeders, then more cardinals will come than blue jays. If millet is placed in the bird feeders, then more blue jays will come than cardinals.”

Null: “If either sunflower seeds or millet are placed in the bird, equal numbers of cardinals and blue jays will come.”

Through simple observation, you actually find that cardinals come as often as blue jays when sunflower seeds or millet is in the bird feeder. In this case, you would reject your “if-then” hypothesis and “fail to reject” your null hypothesis . You cannot accept your first hypothesis, because it’s clearly not true. Instead you found that there was actually no relation between your different variables. Consequently, you would need to run more experiments with different variables to see if the new variables impact the results.

Experiment #4: In-Class Survey (Including an Alternative Hypothesis)

You’re about to give a speech in one of your classes about the importance of paying attention. You want to take this opportunity to test a hypothesis you’ve had for a while: 

If-then: If students sit in the first two rows of the classroom, then they will listen better than students who do not.

Null: If students sit in the first two rows of the classroom, then they will not listen better or worse than students who do not.

You give your speech and then ask your teacher if you can hand out a short survey to the class. On the survey, you’ve included questions about some of the topics you talked about. When you get back the results, you’re surprised to see that not only do the students in the first two rows not pay better attention, but they also scored worse than students in other parts of the classroom! Here, both your if-then and your null hypotheses are not representative of your findings. What do you do?

This is when you reject both your if-then and null hypotheses and instead create an alternative hypothesis . This type of hypothesis is used in the rare circumstance that neither of your hypotheses is able to capture your findings . Now you can use what you’ve learned to draft new hypotheses and test again! 

Key Takeaways: Hypothesis Writing

The more comfortable you become with writing hypotheses, the better they will become. The structure of hypotheses is flexible and may need to be changed depending on what topic you are studying. The most important thing to remember is the purpose of your hypothesis and the difference between the if-then and the null . From there, in forming your hypothesis, you should constantly be asking questions, making observations, doing secondary research, and considering your variables. After you have written your hypothesis, be sure to edit it so that it is plausible, clearly defined, observable, and helpful in explaining a general phenomenon.

Writing a hypothesis is something that everyone, from elementary school children competing in a science fair to professional scientists in a lab, needs to know how to do. Hypotheses are vital in experiments and in properly executing the scientific method . When done correctly, hypotheses will set up your studies for success and help you to understand the world a little better, one experiment at a time.

What’s Next?

If you’re studying for the science portion of the ACT, there’s definitely a lot you need to know. We’ve got the tools to help, though! Start by checking out our ultimate study guide for the ACT Science subject test. Once you read through that, be sure to download our recommended ACT Science practice tests , since they’re one of the most foolproof ways to improve your score. (And don’t forget to check out our expert guide book , too.)

If you love science and want to major in a scientific field, you should start preparing in high school . Here are the science classes you should take to set yourself up for success.

If you’re trying to think of science experiments you can do for class (or for a science fair!), here’s a list of 37 awesome science experiments you can do at home

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Ashley Sufflé Robinson has a Ph.D. in 19th Century English Literature. As a content writer for PrepScholar, Ashley is passionate about giving college-bound students the in-depth information they need to get into the school of their dreams.

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What Are Examples of a Hypothesis?

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A hypothesis is an explanation for a set of observations. Hypothesis examples can help you understand how this scientific method works.

Although you could state a scientific hypothesis in various ways, most hypotheses are either "If, then" statements or forms of the null hypothesis. The null hypothesis is sometimes called the "no difference" hypothesis. The null hypothesis is good for experimentation because it's simple to disprove. If you disprove a null hypothesis, that is evidence for a relationship between the variables you are examining.

Hypotheses Examples: Null

• All daisies have the same number of petals.
• Hyperactivity is unrelated to eating sugar.
• The number of pets in a household is unrelated to the number of people living in it.
• A person's preference for a shirt is unrelated to its color.

Hypotheses Examples: If, Then

• If you get at least 6 hours of sleep, you will do better on tests than if you get less sleep.
• If you drop a ball, it will fall toward the ground.
• If you drink coffee before going to bed, then it will take longer to fall asleep.
• If you cover a wound with a bandage, then it will heal with less scarring.

Improving a Hypothesis to Make It Testable

You may wish to revise your first hypothesis to make it easier to design an experiment to test. For example, let's say you have a bad breakout the morning after eating a lot of greasy food. You may wonder if there is a correlation between eating greasy food and getting pimples. You propose the hypothesis example:

Eating greasy food causes pimples.

Next, you need to design an experiment to test this hypothesis. Let's say you decide to eat greasy food every day for a week and record the effect on your face. Then, as a control, you'll avoid greasy food for the next week and see what happens. Now, this is not a good experiment because it does not take into account other factors such as hormone levels, stress, sun exposure, exercise, or any number of other variables that might conceivably affect your skin.

The problem is that you cannot assign cause to your effect . If you eat french fries for a week and suffer a breakout, can you definitely say it was the grease in the food that caused it? Maybe it was the salt. Maybe it was the potato. Maybe it was unrelated to diet. You can't prove your hypothesis. It's much easier to disprove a hypothesis.

So, let's restate the hypothesis to make it easier to evaluate the data:

Getting pimples is unaffected by eating greasy food.

So, if you eat fatty food every day for a week and suffer breakouts and then don't break out the week that you avoid greasy food, you can be pretty sure something is up. Can you disprove the hypothesis? Probably not, since it is so hard to assign cause and effect. However, you can make a strong case that there is some relationship between diet and acne.

If your skin stays clear for the entire test, you may decide to accept your hypothesis . Again, you didn't prove or disprove anything, which is fine

• Null Hypothesis Examples
• The Role of a Controlled Variable in an Experiment
• Random Error vs. Systematic Error
• What Is a Testable Hypothesis?
• What Are the Elements of a Good Hypothesis?
• Scientific Hypothesis Examples
• What Is a Hypothesis? (Science)
• Scientific Method Vocabulary Terms
• Scientific Method Flow Chart
• Understanding Simple vs Controlled Experiments
• Six Steps of the Scientific Method
• What Is an Experimental Constant?
• What Is the Difference Between a Control Variable and Control Group?
• What Is a Controlled Experiment?
• Scientific Variable
• DRY MIX Experiment Variables Acronym

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How To Write A Hypothesis Guide And Detailed Instructions

Whether you’re studying for a college degree, MBA, or Ph.D., developing a hypothesis for your research is mandatory. You must know how to write a good hypothesis to impress your professors. Now, how should a hypothesis be written?

This is where some students get confused and exhausted. You already know that you’re to formulate a hypothesis around something testable. But you don’t know how to create hypotheses based on previous observations that you would later explain in your paper or journal.

In this article, you’ll learn what a hypothesis is, how to make a hypothesis, examples of how to write hypothesis statement, and how to go about yours.

What Is A Hypothesis?

A hypothesis is a statement that is not proven, and it’s an assumption that you’ll base your research on. They must be testable: they must have answers that can be checked with experiments and evidence.

The theory around your hypothesis becomes valid when it’s proven to be true through experiments. Scientists have rules for writing that make their chemistry, physics, and biology research reproducible.

An essential part is that they must understand the experiments of others so that they can build on them and improve them. These rules define how scientists write about science. This rule applies to hypotheses, too.

Why Do You Need A Hypothesis?

Writing a good hypothesis is a key part of any scientific exploration. It allows a broad and open-ended question that compels you to investigate. There are many other reasons, including:

It’s different from a theory because a theory is something like:

“The earth orbits around the sun.”

This is not testable because we know that it’s true. A theory is more like an explanation for why something happens, while a hypothesis is a guess about what will happen and why it would.

A hypothesis is a statement of the relationship you’ve observed in a pair of variables. The easiest way to think about it is that the hypothesis is your testable statement for your research project.

You would typically use your background knowledge and experience as a researcher to come up with this statement before you set out to collect data. A good hypothesis will give you insight into what kind of data you need to collect to answer the question (or provide evidence).

For example:

“People who live in cities have higher stress levels than those who live in rural areas because there are more people around them all day long!”

This hypothesis would then lead us to ask questions like “How do we measure stress?” or “What factors contribute to stress?” You’ll provide answers to these questions with the paper.

A hypothesis can be proven or disproven throughout an experiment. The most common way to disprove a hypothesis is through statistical significance testing. This entails using probability and data analysis to show that there’s no practical difference between the two compared groups.

The hypothesis is a testable statement about how the world works. It’s also a way to properly arrange and structure your data. Without a hypothesis, you won’t even know what to set your scientific experiment on. A hypothesis is what you’ll use to predict what will happen in the future, and the data you collect during the research will help validate or disprove this.

In science, you’re always trying to figure out why things happen the way they do and what factors affect them. When you know how something works, “why do some people get sick while others don’t?” You might make up a hypothesis to test your idea: “People who are exposed to germs get flu symptoms.” Here’s how to start a hypothesis as the answer lets you determine whether your idea is right or wrong; an experiment then validates (or disproves) it.

Now that you know why you need to formulate a testable hypothesis, learn how to write a research hypothesis with tangible examples.

How To Write A Hypothesis

Before you start your experiments in the lab, it’s important to take some time to think about what you’re trying to achieve. After all, you can’t know your research destination until you plan it beforehand. This is why mastering how to state a hypothesis gives room for healthy predictions. Here’s how you formulate hypothesis:

Your first step is to determine what you want to investigate. You can start with a question you’d like to answer or a problem that needs solving.For example, if you’re a teacher trying to improve your students’ reading skills, you might ask:

“What techniques can I use for my students to boost reading comprehension scores on their standardized tests?”

This could also be stated as “Do test-taking strategies lead to improved standardized test scores?”

Once your question pops in your mind, especially while reflecting on a scientific paper you’ve read or a documentary you saw, write it down and commence research.

You need some facts to state a hypothesis and prove it. It might be tricky to get these facts, and you’ll want to look for relevant and irrelevant information.

Relevant information is directly related to your hypothesis. For example, your relevant sources would be academic, examination, and psychology journals, quantitative data or news outlets for the above statement.

Irrelevant information is any other kind of data, and this could be random news outlets or interviews that could help bolster what your assumptions are.

Use the word “because” to indicate that your variable causes or explains another variable. For example: If we are testing whether exercise leads to weight loss, our sentence might look like this:

“Consistent gym practice causes weight loss because it burns calories and gets the body in shape.”

You need to identify if your hypothesis is testable or if it’s an opinion you can’t prove. You can’t test what you don’t know or can’t prove. So you’d need to rewrite your hypothesis if you think it’s not testable.

Your hypothesis should be clear, concise, testable, specific, and relevant. The best way to do this is to write a brief summary of your hypothesis in the form: “If X happens, then Y will happen.”

Here’s a sample hypothesis:

“If I add 15 minutes to my sitting time everyday, then my body mass index (BMI) will reduce by 5 points in three months.”

Now that you’ve defined your idea, it’s time for the actual experiment to determine whether it’ll work.

How To Write A Hypothesis Statement: Example Of A Hypothesis

There are numerous examples of a hypothesis statement you can take a clue from. A scientific hypothesis examines two variables that need evidence-based research to be considered valid. For example:

“If I increase the amount of water applied to a plant garden, then it will make it grow faster.”

You have identified the independent and dependent variables in this statement. The independent variable is “amount of water applied,” and the dependent variable is “grow faster.” You also included a control group, which is important in scientific experiments to eliminate bias from other factors that could influence your results.

In this case, you are comparing how much growth there would be if you increase the amount of water versus how much growth there would be if you do not increase it.

You then need to research the topic in detail and design an experiment before you can write your report. The first step is to decide what you’re going to measure, how you’ll measure it, and how many times you’ll do this so that it’s accurate.

Once you’ve measured your experiment, interpreting the results can be challenging. You should look at graphs or charts of your data to see if any patterns or trends might indicate a cause-and-effect relationship between two things (like applying more water to the plant garden and faster growth).

After looking at the results of your experiment and deciding whether or not they support your original hypothesis, use this new knowledge in your conclusion. Write up something like:

“Based on my findings, it’s clear that applying more water to any plant garden would make the plant garden grow faster and greener.”

Then, write an introduction section where you can explain why this project interests/matters/is relevant to your reader. At this point, your hypothesis is no longer an educated guess. It started as one (with the observation or thoughts/idea) and ended as verifiable.

Format For Hypothesis: How Should A Hypothesis Be Written?

The usual format of a hypothesis is If – (then) – because.

Because we have the idea that if a hypothesis is formatted as an if-then statement, it’s clear what the hypothesis is about. This can be helpful for your readers and yourself if you ever need to come back and look at your work.

So, now that you know how to format it correctly (and why) let’s look at some hypothesis examples.

“If snow falls, then I’ll catch a cold when I get outside because cold can be a result of heavy snow.”

“If anyone in my family eats cake, then we will feel sick because the cake contains ingredients we are allergic to.”

“Some grasses never grow because they’re stumped every day.”

All these show that two variables must come together in the sentence. The variables must also be a probability the research attempts to solve to make them valid statements.

How To Know Your Hypothesis Is Good

Now that you know how to create a hypothesis, you need to know if it’s good through these pointers:

State a Hypothesis as Clearly as Possible You can choose precise words that are neither ambiguous nor too technical. You should also avoid jargon and words with multiple meanings to keep your language simple and clear. Don’t use fancy or pretentious words unless they’re absolutely necessary for the meaning you want to convey, and make sure you’ve used them in their correct context. In addition, use a tone of voice appropriate to the audience. A scientific paper may need more formal language than an article for popular consumption. A Good Hypothesis Should Explain the Bond Between Multiple Variables The main purpose of forming a hypothesis is to explain the relationship between multiple variables clearly. The relationship should be testable for it to be proven. This is, why if X leads to Y, what is in between that connects X and Y? This must reflect in the hypothesis as it’s the factor that’ll be experimented. A Hypothesis should Be Testable This means that your hypothesis should be a statement that can be proven or disproven with an experiment. You want to make sure your hypothesis is specific enough to guide you towards the right experiment but not so specific that it eliminates any other possible outcomes of your experiment. Also, a hypothesis should not make claims about unobservable things (like feelings or thoughts). Instead, focus on observable results (things we can see) like measurements and observations from experiments conducted by scientists over time.If your hypothesis isn’t testable, then it needs to be reformulated.

What Should You Do If Your Hypothesis Is Incorrect?

You need to reformulate your thesis if it’s incorrect. You may have to reevaluate the problem or look at it differently. It’s also possible that you need to test your hypothesis with a different method of experimentation.

Here are some ideas from the best scientific thesis writing help experts:

Try Another Approach: Try looking at your hypothesis from a different angle, or consider changing up your methods entirely (for example, instead of asking people what they think will happen in the future and then testing their opinions against reality, you could run an experiment where participants predict events and then actually follow up on those predictions). Share Your Idea with a Third Party: Your hypothesis can be tested by allowing a third party to observe the results of your attempt to prove or disprove the statement. For example, if you’re testing whether peanuts can be made into peanut butter using only as few steps as possible, have someone else make it for you or observe them make it.

Document how you made your product and recorded any necessary changes along the way. This will help you know what works and doesn’t so that you’ll make changes to the whole idea.

Get Hypothesis Writing Help

Writing a hypothesis is smart work. You need professionals who know how to write a scientific hypothesis and journal that reflect the experiment supporting the hypothesis. You need professionals who are also expert writers and can offer writing help online.

We offer some of the best writing helpers online, with fast with turnovers. Our writers create the best hypothesis scenario with the possibility to ace any experiment at a cheap price. They will offer writing help if you need these professionals to help write a good hypothesis for you. After all, you need to complete your degrees stronger than you started. A great paper by professionals can seal that deal, and our master thesis writing service is here to help.

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