difference between a problem statement and a hypothesis

is a . This prediction is . (Remember that we usually can't study everyone in our target population for practical reasons of time and cost, so we are faced with drawing a smaller sample for our study and then 'projecting' the sample results with some degree of confidence back to the target population at large.)

. This is contrast to the two alternative & acceptable forms for the 'curiosity:' .

Go on to Assignment 1: Try Your Hand at Writing Hypotheses or Go back to Understanding Hypotheses

E-mail M. Dereshiwsky at [email protected] Call M. Dereshiwsky at (520) 523-1892

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The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

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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.

Learn about our Editorial Process

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.

On This Page:

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.

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Research Questions vs Hypothesis: What’s The Difference?

by  Antony W

August 1, 2024

You’ll need to come up with a research question or a hypothesis to guide your next research project. But what is a hypothesis in the first place? What is the perfect definition for a research question? And, what’s the difference between the two?

In this guide to research questions vs hypothesis, we’ll look at the definition of each component and the difference between the two.

We’ll also look at when a research question and a hypothesis may be useful and provide you with some tips that you can use to come up with hypothesis and research questions that will suit your research topic . 

Let’s get to it.

What’s a Research Question?

We define a research question as the exact question you want to answer on a given topic or research project. Good research questions should be clear and easy to understand, allow for the collection of necessary data, and be specific and relevant to your field of study.

Research questions are part of heuristic research methods, where researchers use personal experiences and observations to understand a research subject. By using such approaches to explore the question, you should be able to provide an analytical justification of why and how you should respond to the question. 

While it’s common for researchers to focus on one question at a time, more complex topics may require two or more questions to cover in-depth.

When is a Research Question Useful? 

A research question may be useful when and if: 

• There isn’t enough previous research on the topic
• You want to report a wider range out of outcome when doing your research project
• You want to conduct a more open ended inquiries 

Perhaps the biggest drawback with research questions is that they tend to researchers in a position to “fish expectations” or excessively manipulate their findings.

Again, research questions sometimes tend to be less specific, and the reason is that there often no sufficient previous research on the questions.

What’s a Hypothesis? 

A hypothesis is a statement you can approve or disapprove. You develop a hypothesis from a research question by changing the question into a statement.

Primarily applied in deductive research, it involves the use of scientific, mathematical, and sociological findings to agree to or write off an assumption.

Researchers use the null approach for statements they can disapprove. They take a hypothesis and add a “not” to it to make it a working null hypothesis.

A null hypothesis is quite common in scientific methods. In this case, you have to formulate a hypothesis, and then conduct an investigation to disapprove the statement.

If you can disapprove the statement, you develop another hypothesis and then repeat the process until you can’t disapprove the statement.

In other words, if a hypothesis is true, then it must have been repeatedly tested and verified.

The consensus among researchers is that, like research questions, a hypothesis should not only be clear and easy to understand but also have a definite focus, answerable, and relevant to your field of study. 

When is a Hypothesis Useful?

A hypothesis may be useful when or if:

• There’s enough previous research on the topic
• You want to test a specific model or a particular theory
• You anticipate a likely outcome in advance 

The drawback to hypothesis as a scientific method is that it can hinder flexibility, or possibly blind a researcher not to see unanticipated results.

Research Question vs Hypothesis: Which One Should Come First 

Researchers use scientific methods to hone on different theories. So if the purpose of the research project were to analyze a concept, a scientific method would be necessary.

Such a case requires coming up with a research question first, followed by a scientific method.

Since a hypothesis is part of a research method, it will come after the research question.

Research Question vs Hypothesis: What’s the Difference? 

The following are the differences between a research question and a hypothesis.

We look at the differences in purpose and structure, writing, as well as conclusion. 

Research Questions vs Hypothesis: Some Useful Advice 

As much as there are differences between hypothesis and research questions, you have to state either one in the introduction and then repeat the same in the conclusion of your research paper.

Whichever element you opt to use, you should clearly demonstrate that you understand your topic, have achieved the goal of your research project, and not swayed a bit in your research process.

If it helps, start and conclude every chapter of your research project by providing additional information on how you’ve or will address the hypothesis or research question.

You should also include the aims and objectives of coming up with the research question or formulating the hypothesis. Doing so will go a long way to demonstrate that you have a strong focus on the research issue at hand. 

Research Questions vs Hypothesis: Conclusion 

If you need help with coming up with research questions, formulating a hypothesis, and completing your research paper writing , feel free to talk to us. 

About the author 

Antony W is a professional writer and coach at Help for Assessment. He spends countless hours every day researching and writing great content filled with expert advice on how to write engaging essays, research papers, and assignments.

The Research Problem & Statement

What they are & how to write them (with examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, you’re bound to encounter the concept of a “ research problem ” or “ problem statement ” fairly early in your learning journey. Having a good research problem is essential, as it provides a foundation for developing high-quality research, from relatively small research papers to a full-length PhD dissertations and theses.

In this post, we’ll unpack what a research problem is and how it’s related to a problem statement . We’ll also share some examples and provide a step-by-step process you can follow to identify and evaluate study-worthy research problems for your own project.

Overview: Research Problem 101

What is a research problem.

• What is a problem statement?

Where do research problems come from?

• How to find a suitable research problem
• Key takeaways

A research problem is, at the simplest level, the core issue that a study will try to solve or (at least) examine. In other words, it’s an explicit declaration about the problem that your dissertation, thesis or research paper will address. More technically, it identifies the research gap that the study will attempt to fill (more on that later).

Let’s look at an example to make the research problem a little more tangible.

To justify a hypothetical study, you might argue that there’s currently a lack of research regarding the challenges experienced by first-generation college students when writing their dissertations [ PROBLEM ] . As a result, these students struggle to successfully complete their dissertations, leading to higher-than-average dropout rates [ CONSEQUENCE ]. Therefore, your study will aim to address this lack of research – i.e., this research problem [ SOLUTION ].

A research problem can be theoretical in nature, focusing on an area of academic research that is lacking in some way. Alternatively, a research problem can be more applied in nature, focused on finding a practical solution to an established problem within an industry or an organisation. In other words, theoretical research problems are motivated by the desire to grow the overall body of knowledge , while applied research problems are motivated by the need to find practical solutions to current real-world problems (such as the one in the example above).

As you can probably see, the research problem acts as the driving force behind any study , as it directly shapes the research aims, objectives and research questions , as well as the research approach. Therefore, it’s really important to develop a very clearly articulated research problem before you even start your research proposal . A vague research problem will lead to unfocused, potentially conflicting research aims, objectives and research questions .

What is a research problem statement?

As the name suggests, a problem statement (within a research context, at least) is an explicit statement that clearly and concisely articulates the specific research problem your study will address. While your research problem can span over multiple paragraphs, your problem statement should be brief , ideally no longer than one paragraph . Importantly, it must clearly state what the problem is (whether theoretical or practical in nature) and how the study will address it.

Here’s an example of a statement of the problem in a research context:

Rural communities across Ghana lack access to clean water, leading to high rates of waterborne illnesses and infant mortality. Despite this, there is little research investigating the effectiveness of community-led water supply projects within the Ghanaian context. Therefore, this study aims to investigate the effectiveness of such projects in improving access to clean water and reducing rates of waterborne illnesses in these communities.

As you can see, this problem statement clearly and concisely identifies the issue that needs to be addressed (i.e., a lack of research regarding the effectiveness of community-led water supply projects) and the research question that the study aims to answer (i.e., are community-led water supply projects effective in reducing waterborne illnesses?), all within one short paragraph.

Need a helping hand?

Wherever there is a lack of well-established and agreed-upon academic literature , there is an opportunity for research problems to arise, since there is a paucity of (credible) knowledge. In other words, research problems are derived from research gaps . These gaps can arise from various sources, including the emergence of new frontiers or new contexts, as well as disagreements within the existing research.

Let’s look at each of these scenarios:

New frontiers – new technologies, discoveries or breakthroughs can open up entirely new frontiers where there is very little existing research, thereby creating fresh research gaps. For example, as generative AI technology became accessible to the general public in 2023, the full implications and knock-on effects of this were (or perhaps, still are) largely unknown and therefore present multiple avenues for researchers to explore.

New contexts – very often, existing research tends to be concentrated on specific contexts and geographies. Therefore, even within well-studied fields, there is often a lack of research within niche contexts. For example, just because a study finds certain results within a western context doesn’t mean that it would necessarily find the same within an eastern context. If there’s reason to believe that results may vary across these geographies, a potential research gap emerges.

Disagreements – within many areas of existing research, there are (quite naturally) conflicting views between researchers, where each side presents strong points that pull in opposing directions. In such cases, it’s still somewhat uncertain as to which viewpoint (if any) is more accurate. As a result, there is room for further research in an attempt to “settle” the debate.

Of course, many other potential scenarios can give rise to research gaps, and consequently, research problems, but these common ones are a useful starting point. If you’re interested in research gaps, you can learn more here .

How to find a research problem

Given that research problems flow from research gaps , finding a strong research problem for your research project means that you’ll need to first identify a clear research gap. Below, we’ll present a four-step process to help you find and evaluate potential research problems.

If you’ve read our other articles about finding a research topic , you’ll find the process below very familiar as the research problem is the foundation of any study . In other words, finding a research problem is much the same as finding a research topic.

Step 1 – Identify your area of interest

Naturally, the starting point is to first identify a general area of interest . Chances are you already have something in mind, but if not, have a look at past dissertations and theses within your institution to get some inspiration. These present a goldmine of information as they’ll not only give you ideas for your own research, but they’ll also help you see exactly what the norms and expectations are for these types of projects.

At this stage, you don’t need to get super specific. The objective is simply to identify a couple of potential research areas that interest you. For example, if you’re undertaking research as part of a business degree, you may be interested in social media marketing strategies for small businesses, leadership strategies for multinational companies, etc.

Depending on the type of project you’re undertaking, there may also be restrictions or requirements regarding what topic areas you’re allowed to investigate, what type of methodology you can utilise, etc. So, be sure to first familiarise yourself with your institution’s specific requirements and keep these front of mind as you explore potential research ideas.

Step 2 – Review the literature and develop a shortlist

Once you’ve decided on an area that interests you, it’s time to sink your teeth into the literature . In other words, you’ll need to familiarise yourself with the existing research regarding your interest area. Google Scholar is a good starting point for this, as you can simply enter a few keywords and quickly get a feel for what’s out there. Keep an eye out for recent literature reviews and systematic review-type journal articles, as these will provide a good overview of the current state of research.

At this stage, you don’t need to read every journal article from start to finish . A good strategy is to pay attention to the abstract, intro and conclusion , as together these provide a snapshot of the key takeaways. As you work your way through the literature, keep an eye out for what’s missing – in other words, what questions does the current research not answer adequately (or at all)? Importantly, pay attention to the section titled “ further research is needed ”, typically found towards the very end of each journal article. This section will specifically outline potential research gaps that you can explore, based on the current state of knowledge (provided the article you’re looking at is recent).

Take the time to engage with the literature and develop a big-picture understanding of the current state of knowledge. Reviewing the literature takes time and is an iterative process , but it’s an essential part of the research process, so don’t cut corners at this stage.

As you work through the review process, take note of any potential research gaps that are of interest to you. From there, develop a shortlist of potential research gaps (and resultant research problems) – ideally 3 – 5 options that interest you.

Step 3 – Evaluate your potential options

Once you’ve developed your shortlist, you’ll need to evaluate your options to identify a winner. There are many potential evaluation criteria that you can use, but we’ll outline three common ones here: value, practicality and personal appeal.

Value – a good research problem needs to create value when successfully addressed. Ask yourself:

• Who will this study benefit (e.g., practitioners, researchers, academia)?
• How will it benefit them specifically?
• How much will it benefit them?

Practicality – a good research problem needs to be manageable in light of your resources. Ask yourself:

• What data will I need access to?
• What knowledge and skills will I need to undertake the analysis?
• What equipment or software will I need to process and/or analyse the data?
• How much time will I need?
• What costs might I incur?

Personal appeal – a research project is a commitment, so the research problem that you choose needs to be genuinely attractive and interesting to you. Ask yourself:

• How appealing is the prospect of solving this research problem (on a scale of 1 – 10)?
• Why, specifically, is it attractive (or unattractive) to me?
• Does the research align with my longer-term goals (e.g., career goals, educational path, etc)?

Depending on how many potential options you have, you may want to consider creating a spreadsheet where you numerically rate each of the options in terms of these criteria. Remember to also include any criteria specified by your institution . From there, tally up the numbers and pick a winner.

Step 4 – Craft your problem statement

Once you’ve selected your research problem, the final step is to craft a problem statement. Remember, your problem statement needs to be a concise outline of what the core issue is and how your study will address it. Aim to fit this within one paragraph – don’t waffle on. Have a look at the problem statement example we mentioned earlier if you need some inspiration.

Key Takeaways

We’ve covered a lot of ground. Let’s do a quick recap of the key takeaways:

• A research problem is an explanation of the issue that your study will try to solve. This explanation needs to highlight the problem , the consequence and the solution or response.
• A problem statement is a clear and concise summary of the research problem , typically contained within one paragraph.
• Research problems emerge from research gaps , which themselves can emerge from multiple potential sources, including new frontiers, new contexts or disagreements within the existing literature.
• To find a research problem, you need to first identify your area of interest , then review the literature and develop a shortlist, after which you’ll evaluate your options, select a winner and craft a problem statement .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

I APPRECIATE YOUR CONCISE AND MIND-CAPTIVATING INSIGHTS ON THE STATEMENT OF PROBLEMS. PLEASE I STILL NEED SOME SAMPLES RELATED TO SUICIDES.

Very pleased and appreciate clear information.

Your videos and information have been a life saver for me throughout my dissertation journey. I wish I’d discovered them sooner. Thank you!

Very interesting. Thank you. Please I need a PhD topic in climate change in relation to health.

Your posts have provided a clear, easy to understand, motivating literature, mainly when these topics tend to be considered “boring” in some careers.

Thank you, but i am requesting for a topic in records management

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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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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.

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

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

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

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

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

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

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Problem Statement, Conceptual Framework, and Research Question

McGaghie, William C.; Bordage, Georges; Shea, Judy A. *

* Lloyd Lewis, PhD, emeritus professor of the Medical College of Georgia, participated in early meetings of the Task Force and contributed to the earliest draft of this section.

REVIEW CRITERIA

• The introduction builds a logical case and context for the problem statement.
• The problem statement is clear and well articulated.
• The conceptual (theoretical) framework is explicit and justified.
• The research question (research hypothesis where applicable) is clear, concise, and complete.
• The variables being investigated are clearly identified and presented.

ISSUES AND EXAMPLES RELATED TO THE CRITERIA

Introduction.

A scholarly manuscript starts with an Introduction that tells a story. The Introduction orients the reader to the topic of the report, moving from broad concepts to more specific ideas. 1 The Introduction should convince the reader, and all the more the reviewer, that the author has thought the topic through and has developed a tight, “researchable” problem. The Introduction should move logically from the known to the unknown. The actual components of an Introduction (including its length, complexity, and organization) will vary with the type of study being reported, the traditions of the research community or discipline in which it is based, and the style and tradition of the journal receiving the manuscript. It is helpful for the reviewer to evaluate the Introduction by thinking about its overall purpose and its individual components: problem statement, conceptual framework, and research question. Two related articles, “Reference to the Literature” and “Relevance,” follow the present article.

Problem Statement

The Introduction to a research manuscript articulates a problem statement . This essential element conveys the issues and context that gave rise to the study. Two examples of problem statements are: “With the national trend toward more patient care in outpatient settings, the numbers of patients on inpatient wards have declined in many hospitals, contributing to the inadequacy of inpatient wards as the primary setting for teaching students,” 2 and “The process of professional socialization, regardless of the philosophical approach of the educational program, can be stressful … few studies have explored the unique stressors associated with PBL in professional education.” 3 These statements help readers anticipate the goals of each study. In the case of the second example, the Introduction ended with the following statement: “The purpose of this qualitative study was to identify stressors perceived by physiotherapy students during their initial unit of study in a problem-based program.” In laying out the issues and context, the Introduction should not contain broad generalizations or sweeping claims that will not be backed up in the paper's literature review. (See the next article.)

Conceptual Framework

Most research reports cast the problem statement within the context of a conceptual or theoretical framework. 4 A description of this framework contributes to a research report in at least two ways because it (1) identifies research variables, and (2) clarifies relationships among the variables. Linked to the problem statement, the conceptual framework “sets the stage” for presentation of the specific research question that drives the investigation being reported. For example, the conceptual framework and research question would be different for a formative evaluation study than for a summative study, even though their variables might be similar.

Scholars argue that a conceptual or theoretical framework always underlies a research study, even if the framework is not articulated. 5 This may seem incongruous, because many research problems originate from practical educational or clinical activities. Questions often arise such as “I wonder why such an event did not [or did] happen?” For example, why didn't the residents' test-interpretation skills improve after they were given feedback? There are also occasions when a study is undertaken simply to report or describe an event, e.g., pass rates for women versus men on high-stakes examinations such as the United States Medical Licensing Examination (USMLE) Step 1. Nevertheless, it is usually possible to construct at least a brief theoretical rationale for the study. The rationale in the USMLE example may be, for instance, about gender equity and bias and why these are important issues. Frameworks are usually more elaborate and detailed when the topics that are being studied have long scholarly histories (e.g., cognition, psychometrics) where active researchers traditionally embed their empirical work in well-established theories.

Research Question

A more precise and detailed expression of the problem statement cast as a specific research question is usually stated at the end of the Introduction. To illustrate, a recent research report states, “The research addressed three questions. First, do students” pulmonary physiology concept structures change from random patterns before instruction to coherent, interpretable structures after a focused block of instruction? Second, can an MDS [multidimensional scaling] solution account for a meaningful proportion of variance in medical and veterinary students' concept structures? Third, do individual differences in the ways in which medical and veterinary students intellectually organize the pulmonary physiology concepts as captured by MDS correlate with course examination achievement? 6

In experimental research, the logic revealed in the Introduction might result in explicitly stated hypotheses that would include specification of dependent and independent variables. 7 By contrast, much of the research in medical education is not experimental. In such cases it is more typical to state general research questions. For example, “In this [book] section, the meaning of medical competence in the worlds of practicing clinicians is considered through the lens of an ethnographic story. The story is about the evolution of relationships among obstetrical providers and transformations in obstetrical practice in one rural town in California, which I will call ‘Coast Community,’ over the course of a decade.” 8

For some journals, the main study variables (e.g., medical competence) will be defined in the Introduction. Other journals will place this in the Methods section. Whether specific hypotheses or more general research questions are stated, the reviewer (reader) should be able to anticipate what will be revealed in the Methods.

The purpose of the Introduction is to construct a logical “story” that will educate the reader about the study that follows. The order of the components may vary, with the problem statement sometimes coming after the conceptual framework, while in other reports the problem statement may appear in the first paragraph to orient the reader about what to expect. However, in all cases the Introduction will engage, educate, and encourage the reader to finish the manuscript.

Section Description

Review Criteria for Research Manuscripts

Joint Task Force of Academic Medicine and the GEA-RIME Committee

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Frequently asked questions

Should i use a research question, hypothesis, or thesis statement.

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

Frequently asked questions: Writing a research paper

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

Research questions anchor your whole project, so it’s important to spend some time refining them.

In general, they should be:

• Focused and researchable
• Answerable using credible sources
• Complex and arguable
• Feasible and specific
• Relevant and original

All research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

Your research objectives indicate how you’ll try to address your research problem and should be specific:

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

The main guidelines for formatting a paper in Chicago style are to:

• Use a standard font like 12 pt Times New Roman
• Use 1 inch margins or larger
• Apply double line spacing
• Indent every new paragraph ½ inch
• Include a title page
• Place page numbers in the top right or bottom center
• Cite your sources with author-date citations or Chicago footnotes
• Include a bibliography or reference list

To automatically generate accurate Chicago references, you can use Scribbr’s free Chicago reference generator .

The main guidelines for formatting a paper in MLA style are as follows:

• Use an easily readable font like 12 pt Times New Roman
• Set 1 inch page margins
• Include a four-line MLA heading on the first page
• Center the paper’s title
• Use title case capitalization for headings
• Cite your sources with MLA in-text citations
• List all sources cited on a Works Cited page at the end

To format a paper in APA Style , follow these guidelines:

• Use a standard font like 12 pt Times New Roman or 11 pt Arial
• If submitting for publication, insert a running head on every page
• Apply APA heading styles
• Cite your sources with APA in-text citations
• List all sources cited on a reference page at the end

No, it’s not appropriate to present new arguments or evidence in the conclusion . While you might be tempted to save a striking argument for last, research papers follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the results and discussion sections if you are following a scientific structure). The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

The conclusion of a research paper has several key elements you should make sure to include:

• A restatement of the research problem
• A summary of your key arguments and/or findings
• A short discussion of the implications of your research

Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.

This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .

The introduction of a research paper includes several key elements:

• A hook to catch the reader’s interest
• Relevant background on the topic
• Details of your research problem

and your problem statement

• A thesis statement or research question
• Sometimes an overview of the paper

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scientific hypothesis

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• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
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scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

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Q. What is the difference between a thesis statement and a hypothesis statement?

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Answered By: APUS Librarians Last Updated: Apr 15, 2022     Views: 129561

Both the hypothesis statement and the thesis statement answer a research question. 

• A hypothesis is a statement that can be proved or disproved. It is typically used in quantitative research and predicts the relationship between variables.  
• A thesis statement is a short, direct sentence that summarizes the main point or claim of an essay or research paper. It is seen in quantitative, qualitative, and mixed methods research. A thesis statement is developed, supported, and explained in the body of the essay or research report by means of examples and evidence.

Every research study should contain a concise and well-written thesis statement. If the intent of the study is to prove/disprove something, that research report will also contain a hypothesis statement.

NOTE: In some disciplines, the hypothesis is referred to as a thesis statement! This is not accurate but within those disciplines it is understood that "a short, direct sentence that summarizes the main point" will be included.

For more information, see The Research Question and Hypothesis (PDF file from the English Language Support, Department of Student Services, Ryerson University).

How do I write a good thesis statement?

How do I write a good hypothesis statement?

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What is the difference between hypothesis, thesis statement and research goal?

Can someone explain the difference between hypothesis, thesis statement and research goal based on an example?

• terminology

• 1 You should mention which subject you are in. 'Hypothesis' has opposite meanings in maths and physics. –  Jessica B Commented May 31, 2018 at 11:22

2 Answers 2

I had this same question recently and did some research on it. The definitions I found weren't consistent, but from them I derived the following.

Thesis statement -- A definitive statement about the way the world (or your system of interest) works, especially what is most important in causing or influencing the behavior of the system.

"Family expectations has primary significance on the performance in college for Latino girls in the Western US" is an example of a thesis statement.

Research goal -- Expresses what you hope to learn or shed light on in your research. Specifically, the goal should specify what type of results you are hoping to achieve. It contextualizes your work in relation to other research, especially theory. It also feeds into your choice of method.

"My research goal is to develop a theoretical model of cultural influence on college performance, contextualized by gender and ethnicity" is an example of a research goal.

Hypotheses -- What specific conditions or relations do you aim to test or evaluate in your research. Any research that does not include a method for hypothesis testing should not claim to test hypotheses. A hypothesis statement must be specific enough that it is testable by the methods you choose, and also it should be falsifiable -- i.e. it is clear what evidence might prove the hypothesis false, and such evidence should be plausible and possible.

"Low family expectations has a detrimental effect on the college completion rate and time-to-complete for high-achieving Latino girls" is an example of a hypothesis statement.

Notice how there are specific, testable conditions and metrics -- "college completion rates" and "time-to-complete". These conditions should appear as metrics in your research methods -- i.e. instruments and analysis methods.

A thesis statement usually helps guide the research paper. It is a short sentence or summary containing the central idea of the research paper. It helps a reader have a clear glimpse of what the paper is about.

The Hypothesis statement comes in different format but with the intent to help prove or disprove a phenomenon. The hypothesis can help defend, support, explain or disprove, argue against the thesis statement.Usually the hypothesis measures specific issues or variables-two or more and therefore should be testable. The thesis statement creates a background while the hypothesis creates a means to measure the interrelationship.

The research goal takes a look into the future of your study or research paper. |It tries to help you state what the outcomes you seek to achieve by the research work. With a research goal you can set specific milestones to accomplish at the end of the research work.

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• Published: 17 September 2024

Intergenerational effects of a casino-funded family transfer program on educational outcomes in an American Indian community

• Tim A. Bruckner   ORCID: orcid.org/0000-0002-6927-964X 1 , 2 ,
• Brenda Bustos 2 ,
• Kenneth A. Dodge 3 ,
• Jennifer E. Lansford   ORCID: orcid.org/0000-0003-1956-4917 3 ,
• Candice L. Odgers   ORCID: orcid.org/0000-0003-4937-6618 4 &
• William E. Copeland   ORCID: orcid.org/0000-0002-1348-7781 5  

Nature Communications volume  15 , Article number:  8168 ( 2024 ) Cite this article

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• Human behaviour
• Social sciences

Cash transfer policies have been widely discussed as mechanisms to curb intergenerational transmission of socioeconomic disadvantage. In this paper, we take advantage of a large casino-funded family transfer program introduced in a Southeastern American Indian Tribe to generate difference-in-difference estimates of the link between children’s cash transfer exposure and third grade math and reading test scores of their offspring. Here we show greater math (0.25 standard deviation [SD], p =.0148, 95% Confidence Interval [CI]: 0.05, 0.45) and reading (0.28 SD, p  = .0066, 95% CI: 0.08, 0.49) scores among American Indian students whose mother was exposed ten years longer than other American Indian students to the cash transfer during her childhood (or relative to the non-American Indian student referent group). Exploratory analyses find that a mother’s decision to pursue higher education and delay fertility appears to explain some, but not all, of the relation between cash transfers and children’s test scores. In this rural population, large cash transfers have the potential to reduce intergenerational cycles of poverty-related educational outcomes.

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Introduction.

Parents’ wealth plays a substantial role in their children’s life chances 1 , 2 . In the United States, 13 million children live in families with incomes below the poverty line 3 . Extensive literature finds that these children show an increased risk of poor physical and cognitive outcomes 4 , 5 , 6 , 7 , 8 , 9 as well as lower socioeconomic status attainment in adulthood 10 , 11 . Increasing recognition of the strong intergenerational transmission of disadvantage, and the relatively high fraction of children living in poverty in the US 12 , has led to a variety of interventions which aim to improve life outcomes for low-income children. Some scholars and policymakers, for instance, have proposed direct cash transfers (e.g., a child tax credit) to boost the financial resources of low-income families with children 12 , 13 , 14 .

Accumulating evidence 15 , including from the Great Smoky Mountains Study (GSMS) in rural North Carolina which began recruitment before a “natural experiment,” supports causal long-term benefits of a large family cash transfer during childhood. In the late 1990s, a Southeastern American Indian Tribe underwent a natural experiment by way of the introduction of a casino on their lands. Under the terms of an agreement with the tribe, the casino allocated a percentage of profits in acute lump sums to all enrolled members. Gaming proved profitable; since 1996, per capita payments to members have averaged approximately $5000 per year. These disbursements raised income levels of an entire community that previously exhibited a high rate of poverty. GSMS findings indicate improved educational attainment 13 , health 16 and financial well-being into adulthood among American Indian participants whose families received cash transfers during their childhood 17 . Importantly, findings appear stronger with increasing duration of time that their American Indian families received the transfers while the child lived at home 17 . This result coheres with work in economics which finds that early childhood investments offer greater long-term gains to human capital than do investments later in life 18 .

Whereas many interventions aim to improve outcomes for low-income children, few examine whether their effects persist into the next generation. In this study, we exploit the quasi-random timing of the cash transfer during childhood among the tribe to test whether the next generation of children show human capital gains. We use the second generation’s math and reading test score data in third grade—a reliable predictor of later-life educational attainment 19 , 20 and the earliest year in which standardized educational outcomes are obtained—as a gauge of intergenerational effects. In addition, unlike earlier work, we focus on the population base of American Indians that had children (rather than a selected cohort) which permits not only increased study power but also external validity to the affected region.

In this work, we use American Indian race/ethnicity as a proxy for tribal membership and find improved third grade math and reading scores among American Indian students whose mother was exposed longer to the cash transfer during her childhood. A mother’s decision to pursue higher education and delay fertility explains some, but not all, of the discovered relation. In this rural population, large cash transfers have the potential to enhance human capital of the next generation.

Exposure and sample characteristics

Consistent with prior work, we used American Indian race/ethnicity in Jackson, Swain, and Graham counties in North Carolina as a proxy for the Eastern Band of Cherokee. These residents received the large family cash transfer beginning in 1996. By contrast, non- American Indian residents in these counties received no cash transfer. Figure  1 provides a timeline of the cash transfers to American Indian families, the timing of births, and the data linkage to third grade test scores.

Casino payments begin in 1996 and are disbursed to adults (G1). The young children of G1 (i.e., G2) grow to childbearing age, and 2000 is the first birth year of their children (G3) for whom we retrieved third grade reading and math test scores from 2008 to 2017. G2 women who were relatively young in 1996 –when G1 received the first Casino payment– are considered more exposed to the cash transfers than are G2 women who were at or above 18 years of age in 1996.

Using state administrative records housed at the North Carolina Education Research Data Center (NCERDC), we accessed the linked North Carolina Birth file to math ( N  = 4289) and reading test scores ( N  = 4254) for third grade public school students in the three treated counties, from 2008 to 2017. Whereas mean scores for non- American Indian children ( N  = 3549) lie slightly above the state mean, those for American Indian children ( N  = 740) fall, on average, 0.39 standard deviations ( SD ) below the state mean (Fig.  2 ).

Third grade math ( a ) and reading ( b ) scores among children born to American Indian (orange bar) and non-American Indian mothers (blue bar), Jackson, Swain, and Graham counties, for test years 2008–2017. The orange bars represent the proportion of a z-score to children of American Indian mothers. The blue bars represent the proportion of a z-score to children of non- American Indian mothers.

Table  1 describes maternal and birth characteristics of the children with valid third grade test scores. American Indian mothers tend to report lower completed education, younger age at birth, and lower frequency of being married than do non- American Indian mothers. By contrast, the prevalence of preterm (<37 weeks completed gestational age at delivery) and/or low weight (<2500 g) delivery is lower among births to American Indian mothers (vs. non-American Indian mothers). These patterns appear consistent with the broader literature describing racial/ethnic differences, which indicates minimal bias in the NCERDC algorithm used to link birth records to third grade test scores.

Regression for third grade math and reading scores

We employed a “difference-in-difference” (DiD) regression strategy to isolate potential benefits of the family cash transfer on educational outcomes of children born to American Indian mothers who were relatively young in 1996—the first year of the family cash transfer program. This approach uses two control populations (e.g., non- American Indian children as well as children born to American Indian mothers who were relatively older, around age 17 in 1996) to adjust for unmeasured confounding and other threats to validity. Our DiD specification is a time-varying treatment effect design in which duration of exposure to the cash transfer as a child serves as the “intensity” of exposure for American Indian mothers 21 . We examine the influence of the cash transfer by regressing children’s test scores on time exposed to the cash transfer and American Indian status, and then testing whether the relation between test scores and time exposed to the cash transfer differs by American Indian status. Here, duration of time exposed before age 18 is a continuous variable (range: 0–15 years; see Supplementary Table  1 ). A person over 18 in 1996 receives a “0” duration value and we retain them in the sample. Importantly, our dataset also permits a test of the parallel trends assumption in the pre-treatment period (see Supplementary Tables  2 and 3 ).

Results from the DiD regressions (Model 1 column in Table  2 for Math; Model 1 column in Table  3 for Reading) show a positive relation between test scores and the interaction term of American Indian race/ethnicity and childhood remaining at the start of the family cash transfer. The positive relation reaches conventional levels of statistical detection (i.e., p  < 0.05) for both reading ( p  = 0.0014, 95% CI: 0.013, 0.055) and math ( p  = 0.0055, 95% CI: 0.009, 0.050) scores. The strength of the relation is slightly larger (i.e., ~17%) for reading relative to math. Inclusion of child age at time of test (with a squared and cubed term; see Model 2 column in Tables  2 and 3 ) and infant sex slightly attenuates main results but does not affect statistical inference. Figure  3 (math) and Figure  4  (reading) illustrate the regression results of Model 2 in Tables  2 and 3 by showing fitted third grade test scores by American Indian status and category of duration exposure. Within the context of the declining trend in third grade test scores in this rural population (which mirrors national trends 22 ), the race-based disparity in test scores narrows for American Indian children whose mothers had a relatively greater duration exposure.

Within the context of the declining trend in third grade math test scores in this rural population (which mirrors national trends), the race-based disparity in test scores narrows for American Indian children whose mothers had a relatively greater duration exposure. American Indian scores are represented by the orange line. Non-American Indian scores are represented by the blue line.

Summary of findings

To give the reader a sense of the magnitude of the findings, a child whose American Indian mother with ten years of exposure to the family cash transfer before age 18 years scores 0.25 SD higher in math, and 0.28 SD higher in reading, relative to a child whose American Indian mother had no exposure to the family cash transfer before age 18 years (per coefficients in Model 2 column). This value, while smaller than the observed American Indian/non- American Indian gap in test scores at third grade, is greater than the average score gap between a child whose mother graduated from high school and a child whose mother did not graduate from high school. This value is similar in magnitude to $1000 per pupil per year investments in early childhood education interventions in North Carolina 23 . When scaled to other early childhood educational interventions 23 , the magnitude of the test score increases equates to an additional half school year of learning. Furthermore, these results appear consistent with a continued educational benefit, of moderate magnitude, that affects not only the generation of parents (G2; see Akee et al. 13 ) but also their children.

The discovered support for our hypothesis as well as recent published literature 24 led us to explore whether life course decisions and behaviors of the mother, which precede the child’s birth, may help to explain test score gains among children whose mothers were exposed to the cash transfer for longer periods of time. A mother’s decision to, for instance, pursue higher education, marry, delay fertility, or refrain from smoking during pregnancy all could plausibly lead to improvements in child’s test scores. Results from the exploration (Model 3, Tables  2 and 3 ) indicate that several of these variables predict children’s test scores. Inclusion of these variables, moreover, attenuates the interaction term by ~20%. The interaction term, however, reaches conventional levels of statistical detection for both math and reading, which indicates that these factors may not fully account for American Indian children’s gain in test scores.

Within the context of the declining trend in third grade reading test scores in this rural population (which mirrors national trends), the race-based disparity in test scores narrows for American Indian children whose mothers had a relatively greater duration exposure. American Indian scores are represented by the orange line. Non- American Indian scores are represented by the blue line.

Sensitivity analyses

We conducted several additional checks to assess robustness of results. First, to support the validity of the DiD model, we tested the parallel trends assumption in the pre-treatment period 21 by interacting a time-invariant treatment indicator (American Indian status) with the age of the mother in 1996 minus 18 years of age, and then testing whether the coefficient of the interaction term (i.e., American Indian*pre_treatment) rejects the null for the periods prior to treatment. Results of the American Indian*pre_treatment coefficient in the pre-treatment period do not reject the null for either math or reading test scores (see Supplementary Tables  2 and 3 ), which supports parallel trends in the pre-treatment period.

Second, we restricted the analysis to mothers (G2) who received between 0 and 12 years of duration exposure by 1996 to rule out the possibility that outliers in exposure drive results. Inference for both math and reading did not change (Supplementary Tables  4 and 5 ). Third, we restricted the mother’s (G2) age of delivering children to 16–35 years. We arrived at this range by inspecting the age distribution of mothers at the time of the child’s (G3’s) birth, by American Indian status, and dropping the maternal ages for which fewer than 10 participants fell into that cell. This sensitivity check rules out the possibility that high “outliers” in maternal age drive results. Findings remain similar to those in columns 2 of Tables  2 and 3 , albeit with less precision owing to dropping 8% (math) and 11% (reading) of observations after these restrictions (Supplementary Tables  6 and 7 ). Fourth, to rule out the possibility that trends over time in test scores (such as declines reported nationally 22 and in rural areas 25 ) drive results, we controlled for test year in several ways (including a continuous year variable and, separately, test year indicator variables) and re-ran analyses. This time control also adjusts for any potential response to the 2007-2009 Great Recession. Inference for the American Indian*duration coefficient does not change (Supplementary Tables  8 – 11 ).

We investigated whether childhood investments, in the form of family cash transfers, could improve human capital outcomes in the next generation of children. We focused on a Southeastern American Indian tribe in rural North Carolina who, via a natural experiment by the introduction of a successful casino, received a large cash transfer. Findings indicate statistically significant increases in both reading and math third grade test scores among students born to American Indian mothers with more years of exposure to the cash transfers as children. Results, which control for general changes in the region over time that could have benefited American Indian and non- American Indian students equally, support the hypothesis that large early-life investments show human capital benefits into subsequent generations.

Many American Indian (G2) mothers who were very young in 1996 (i.e., <5 years old) have children that are scheduled to attend third grade after 2017—the last year in which we could link test score information. This circumstance means that our analysis includes very few (G2) mothers who had early-life exposure (i.e., from infancy to age 5) to the cash transfer. Our results may therefore underestimate the potentially larger benefit of cash transfers (especially before age 5 years among G2) that may accrue to the subsequent generation of American Indian children and produce large returns to health and education 26 , 27 .

The magnitude of the statistically significant test score increases in reading and math for children born to American Indian mothers seems reasonable in relation to prior interventions in North Carolina 23 . The slightly larger benefits observed for reading, moreover, cohere with the notion that non-school factors play a substantial role. The education literature generally finds that reading skills develop in much broader (i.e., non-school) settings relative to math skills 28 , 29 , 30 . This work implies that our discovered results likely do not arise from unmeasured factors in which American Indian mothers (but not non- American Indian mothers) chose high-performing schools for their children. We also note, importantly, that non-American Indian children show a steep declining trend over time in test scores, and that American Indian children do not show increases in the absolute level of test scores with increased exposure to the cash transfer. National studies similarly find declining trends in test scores over this time period 22 , as well as persistently lower test scores among white and American Indian children in rural areas 25 , 31 of the US (vs. suburban and urban areas). Explanations for these geographic patterns and time-trends remain elusive. We encourage more careful research in this area to understand the broader national educational landscape within which the cash transfer accrues to American Indian families and children in this rural population.

Since the introduction of the casino in the late 1990s, the Tribe constructed several new facilities including healthcare centers and educational academies. The New Kituwah Academy 32 , for instance, is a private facility (accredited in 2015) which offers, among other programs, dual-immersion elementary school education focused on preserving the Cherokee language, culture, traditions, and history. Whereas American Indian children enrolled in this Academy would not appear in our dataset (i.e., NCERDC linked test scores only for public school-enrolled children), this resource as well as others may benefit human capital especially among American Indian children. Although we have no reason to believe that these benefits covary with the number of childhood years remaining at the start of the family cash transfer, our methods cannot rule out this explanation. We, however, note that much of the infrastructure improvements on Tribal lands remain available to all residents regardless of race/ethnicity. Therefore, our DiD analyses help to control for this rival explanation.

Whereas our findings are among the first to document statistically significant intergenerational test score improvements—25 years after the inception of large family cash transfers—several caveats deserve mention. First, the magnitude of the gains to American Indian children depicts a narrowing of the differences between American Indian and non- American Indian math and reading scores since the onset of the cash transfer in 1996. Despite the higher American Indian math and reading scores, the large American Indian/non- American Indian score gap in math (0.46 SD) and reading (0.54 SD) scores did not close during this time. The latter is as expected considering cash transfers alone are unlikely to rectify the education effects of multi-generational discrimination among American Indian and non- American Indian populations 33 . This discrimination includes past and present unequal treatment as well as structural factors that may lead to a higher prevalence of predictors of low educational attainment among American Indian populations (e.g., poverty, residing near low quality schools, high levels of teen pregnancy; see Demmert and colleagues) 30 , 34

Second, NCERDC could not link the full population of births in this region to their third-grade test score. Non-matches are attributed to moves out of state, private school attendance, name changes, or errors in spelling on records. Third, substantial missing/unknown paternity on the birth file did not permit an examination of whether having an American Indian father who received the cash transfer, or having two American Indian parents (vs. solely an American Indian mother) that received the cash transfer, confers stronger intergenerational associations. Fourth, given the nature of the timing of cash transfers to this population, we cannot determine which factor (child age at initiation of cash transfer or duration of cash transfer exposure before 18 years) seems most relevant in designing new interventions. Fifth, some other work examining this large cash transfer to this population shows adverse outcomes, such as risk of accidental death during months of large casino payments 35 , 36 . This circumstance indicates that any policy discussion about the value of family cash transfers to the next generation should include a careful assessment of their costs and benefits to all generations as well as an assessment of the type (e.g., in-kind vs. cash) and frequency (e.g., lump sum or monthly payment) of the transfer.

Whereas the population-based nature of our linked datasets provides a larger sample size than do cohort studies of this population (i.e., GSMS), the birth and test score data lack contextual variables that may illuminate mechanistic pathways. American Indian mothers with more years of exposure to the family cash transfer as children could, for instance, make a variety of life course decisions that ultimately benefit their children. Previous work on this population finds that fertility 37 , attitudes around fertility timing 38 as well as educational attainment 13 may change after the introduction of the family cash transfer. Recent work also finds that American Indian mothers exposed for a longer duration to the cash transfer show improved maternal/infant health at birth 24 . These pathways, as well as prenatal investments or changes in parenting quality, could account for gains in children’s test scores. We await the availability of additional contextual data, as well as a richer set of school-level variables (e.g., attendance, test scores at later ages) in coming years.

Within the context of the secular decline in third grade test scores in this rural population (Supplementary Figs.  1 and 2 ), the American Indian / non- American Indian disparity in test scores narrows as mothers of American Indian children have a relatively greater duration of exposure. Whereas we infer that this finding arises from the benefit of the cash transfer to American Indian families, we cannot rule out the possibility of unmeasured confounding. Such a confounder would have to correlate positively with our exposure (but not be caused by it), occur only among American Indian families (but not among non- American Indian families), and vary positively with third grade test scores. School-based investments particular to American Indian children that concentrate in recent years, or broader employment gains to American Indian families that concentrate in recent years, could meet these criteria. We, however, know of no such trend in school-based investments unique to American Indian children in public schools. In addition, both American Indian and non- American Indian adults show employment gains following the opening of the casino, which minimizes the plausibility that this factor introduces bias.

The casino opening led to several community improvements besides the cash transfer to tribal members. The tribe designated half of the gaming revenues to community investments, including behavioral health, drug abuse prevention, health care, education, and social services 39 , 40 . In addition, the casino itself is the largest employer in the region and boosts other local businesses 41 . These improvements may lead to gains in health and functioning for all American Indian members (regardless of age) as well as non- American Indian individuals in the study region.

The establishment of the cash transfer payments among this population in the 1990s substantially raised median income in a community that previously exhibited a high poverty rate. Between the years of 1995 and 2000, the percent of American Indian families below the poverty line fell from almost 60% to less than 25% 42 . This circumstance, coupled with accumulating literature documenting improved adult health among recipients who were at earlier childhood ages at the onset of the family cash transfer 17 , compelled us to examine the potential intergenerational benefits among those who were young in 1995 and later decided to have children. An intuitive follow-up question involves whether these intergenerational associations would persist, or even become stronger, among those who were in infancy or under age five at the inception of the family cash transfer in the 1990s and later had children of their own. For American Indian females born in this region in 1995, we can expect their children to have completed third grade and test scores to be available by 2050. In the near term, however, we encourage replication in other settings in the US to determine external validity. A more complete picture of educational outcomes (e.g., subject-matter test scores other than reading and math, school attendance, social and emotional well-being), which we aim to collect in future work, may also better capture academic ability. Other extensions of this work should identify potential pathways in which less impoverished childhood environments affect later-life adult school choice, fertility decisions, and parental investments that in turn enhance human capital of the next generation.

Study population

We examined American Indians in Jackson, Swain, and Graham counties in North Carolina as a proxy for the Eastern Band of Cherokee. According to the 2020 Census, American Indian residents comprise 14.8% of the population in these three counties. No other federally recognized, state recognized, or even unrecognized Tribes claim lands in the western North Carolina area, and the Eastern Band of Cherokee have historically been the only Tribe in this region of western North Carolina. Previous studies have used the census indicator of American Indians as a proxy for Eastern Band of Cherokee in this region 42 . These American Indians residents received the large family cash transfer beginning in 1996. By contrast, non- American Indians residents in these counties received no cash transfer but (as with the American Indian population) experienced the broader economic and infrastructural changes to that region. We therefore use children born to non- American Indians residents of Jackson, Swain, and Graham counties as a comparison group when examining the relation between the family cash transfer and educational outcomes among American Indians residents’ children.

Inclusion and ethics statement

This study was completed using education and birth records from a number of counties in western North Carolina. The data for the current manuscript were obtained from the North Carolina Education Research Data Center (NCERDC), which houses data files from State of North Carolina administrative records 43 . Through data use agreements between Duke University and the State of North Carolina, the NCERDC receives state data files with identified records, merges files as needed, de-identifies the merged files, and then provides access to de-identified files to researchers. None of the NCERDC staff members who worked on the current data set are researchers or authors of the current study. The NCERDC is described here: https://childandfamilypolicy.duke.edu/north-carolina-education-research-data/ . This study is relevant to the educational functioning of families receiving the cash transfer in western North Carolina, but this was not determined in collaboration with local partners. The roles and responsibilities for compiling the data were agreed upon by collaborators ahead of time.

This study was approved by the IRB at Duke University which is located in North Carolina but not specifically in western North Carolina. Also, the research does not result in discrimination as it was focused on a quasi-experiment design resulting from the introduction of a community-wide transfer. The Southeastern American Indian Tribe which co-generated (along with the casino) the cash transfer has promoted this transfer as a public good. We have taken local and regional research relevant to our study into account by citing prior studies of this cash transfer.

Variables and data

Starting in the third grade, North Carolina conducts end-of-grade standards-based achievement tests for math and reading for all students enrolled in public school. The reading and mathematics tests align with the North Carolina Standard Course of Study 44 . We used third grade test scores as our key dependent variable because education scholars view these measures as a stable indicator of student achievement and a reliable predictor of longer-term educational outcomes, not only nationally but also in North Carolina 23 , 45 . Test scores by third grade predict both likelihood of high school graduation and college attendance 19 , 20 . We standardized each raw score to Z-score values using the mean and standard deviation (SD) of all third-grade scores in North Carolina for that test year. This standardization permits direct comparison of student scores across years because it controls for variation over time in difficulty or scaling of the state tests (e.g., if mean test scores show a trend over time, the Z-score values [normed within each test year] are less subject to such trends).

We acquired third grade math and reading test scores among infants born in Jackson, Swain, and Graham counties using linked administrative data files from the Duke University North Carolina Education Research Data Center (NCERDC). The NCERDC receives educational administrative data files from the North Carolina Department of Public Instruction (NC DPI), which collects files submitted annually by each of 115 school districts. To identify the child’s county of birth, NCERDC links individual birth records from the Birth File of the North Carolina Office of Vital Records for all children born in the state with education records from NC DPI. The sample includes only children born in North Carolina and then enrolled (by third grade) in a public elementary school in the state. This process necessarily excludes children who enroll in a private school as well as those whose families moved out of North Carolina by third grade. Over 200 peer-reviewed publications use NCERDC-linked data, which attests to the quality and coverage of the dataset 46 .

Beginning in 2008, in our study region NCERDC reports a match rate of >74% between birth records and third grade test scores. 2017 represents the last year for which we have matched data available at the time of our study. Our test population includes over 4000 American Indian and non- American Indian children who have a valid third grade test score from 2008 to 2017—and who were born from 1998 to 2009 in Jackson, Swain, and Graham counties.

Prior literature finds a positive relation between American Indian later-life health and the number of years during which the individual was exposed to the family cash transfer before reaching age 18 years 17 . This relation coheres with the notion that the duration of the family cash transfer during childhood can exert a positive influence later in life. We, similarly, reasoned that additional benefits could include life-course maternal investments and behaviors which in turn may improve the next generation’s educational outcomes. For this reason, and consistent with prior literature 17 , 42 , we used as the primary exposure the number of years before age 18 that the index individual’s family received the cash transfer.

The Birth File contains several variables that may control for confounding bias but do not plausibly lie on the causal pathway between family cash transfer and the next generation of children’s test scores. These variables, which show associations with test scores, include infant sex and child age (i.e., date of birth). We retrieved these variables from the birth file and used them (as well as other variables in the birth file [maternal education, maternal age], described below in Analysis section) as controls for potential confounding. We determined infant sex based on sex assigned at birth, as recorded on the birth certificate.

All data analyses were conducted using SAS version 9.4. Examination of American Indian and non-American Indian cohorts at varying ages at the inception of the family cash transfer in 1996 confers the methodological benefit of using the family cash transfer as a “natural experiment” which randomly assigns income to American Indian families. We employ a “difference-in-difference” (DiD) regression strategy to isolate potential benefits of the family cash transfer on educational outcomes of children born to American Indian mothers who were relatively young in 1996—the first year of the family cash transfer program. This approach uses a series of control populations to adjust for unmeasured confounding and other threats to validity. It remains plausible, for instance, that the level of social, educational, and economic resources increased over time in Jackson, Swain, and Graham counties in ways that benefited younger-age cohorts in 1996 (relative to older-age cohorts in 1996). This circumstance could result in improved math and reading test scores of children born to younger (relative to older) cohorts. Such a circumstance would confound our test if we falsely attributed this positive relation to the duration of the family cash transfer in childhood.

Our DiD regression approach minimizes the problem of unmeasured confounding. This strategy compares the test scores outcome of children born to American Indian mothers who were young in 1996 to that of children born to American Indian mothers who were relatively older in 1996. Importantly, we also adjust for general cohort differences in access to social, educational, and economic resources in Jackson, Swain, and Graham counties.

The key features of a DiD design involve (i.) comparison of outcomes between two alternative treatment regimes (i.e., treatment and control), (ii.) the availability of pre-treatment and post-treatment time periods in both the treatment and control group, and (iii.) a well-defined study population 21 . We augment this standard DiD with a time-varying treatment effects design, also called DiD with treatment as intensity of exposure. This design assumes that the relation of the treatment to the outcome increases with longer duration of exposure to the treatment. In our case, duration of exposure to the cash transfer as a child serves as the intensity of exposure for American Indian mothers.

The DiD approach (shown below) minimizes the problem of unmeasured confounding. This strategy compares the test scores outcome ( θ , representing third grade math or reading Z-score) of children born to American Indian mothers who were young in 1996 to that of children born to American Indian mothers who were relatively older in 1996. Importantly, we also adjust for general cohort differences in access to social, educational, and economic resources in Jackson, Swain, and Graham counties by subtracting the difference in test scores observed between children born to non-American Indian mothers who were young in 1996 and non-American Indian mothers who were relatively older (i.e., around age 17) in 1996.

Social scientists have employed this approach to examine the effect of large “shocks” on children’s outcomes 28 , 47 , 48 , 49 .

Estimation of the equation above entails pooling data for American Indian and non-American Indian births in Jackson, Swain, and Graham counties, and regressing the third grade test score outcomes from 2008 to 2017 (Z-score for math, and Z-score for reading) on a dichotomous indicator capturing (1) American Indian race/ethnicity (as measured by mother’s race/ethnicity from the Birth file), (2) a continuous indicator of childhood years remaining before age 18 at the start of the family cash transfer and the two-way interaction between American Indian race/ethnicity and childhood years remaining at the start of the family cash transfer. The estimate of interest is the coefficient on the two-way interaction term, which captures the difference in test score outcome between American Indian children born to residents who were relatively young in 1996 and those who were older in 1996, net of that same difference in non-American Indian children. Specifically, we examine the influence of the cash transfer by regressing Y (children’s test scores) on X 1 (time exposed before age 18; continuous) and X 2 (American Indian status), and then testing whether the relation between Y and X 1 differs by American Indian Status (X 2 ). The DiD regression also includes controls for the child’s age in months at third grade test and assigned sex at birth. We applied generalized estimating equation regressions 50 using maximum likelihood estimators to predict the two continuous outcomes (PROC GENMOD in SAS). The test score data (for both math and reading) meet the assumptions for use of these methods. We used two-tailed tests for all statistical analyses.

If we discovered support for a positive relation between the interaction term and Z-score of test (i.e., more childhood years remaining at the start of family cash transfer varies positively with subsequent generation’s third grade test score), we then explored potential pathways of this association. Such an exploration included the addition of maternal education, maternal behavior during pregnancy, and infant health information contained in the Birth File. In addition, as a falsification check we examined the assumption of parallel trends in a DiD framework by testing pre-treatment trends between the treated group and the control group prior to the treatment. To do so, we interacted a time-invariant treatment indicator (American Indian status) with the age of the mother in 1996 minus 18 years of age—but only among mothers 18 years or older in 1996 and therefore never exposed as a child to the cash transfer treatment—and then tested whether the coefficient of this interaction term (American Indian*pre-treatment) rejected the null for both children’s math and reading test scores (see Supplementary Tables  2 and 3 ). Failure to reject the null would satisfy the parallel trends assumption in the pre-treatment period.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The individual-level linked birth records and education outcomes, deriving from existing administrative records, are housed by the NCERDC and derive from existing administrative records. The individual-level raw data are available under restricted access given the usage of personal identifiable information, the state of North Carolina’s restrictions on dissemination without prior consent, and the regulations set by the IRB protocol (Protocol: Pro00090215 with Duke University). The raw data are protected and are not available due to privacy laws. Request for raw data can be made to the NCERDC here: https://childandfamilypolicy.duke.edu/north-carolina-education-research-data/ . Data are only provided to researchers who meet the requirements of the NCERDC Data Use Agreement which stipulates primary affiliation with an institution of higher education, non-profit organization, or government agency within the United States. Additional information can be found at the link provided above. In addition, to comply with open science requirements and that of NCERDC, the processed group-level data used in this study are available within the Figshare database 51 and are available here: https://doi.org/10.6084/m9.figshare.26288080.v1 . These data include the covariance matrix of the data analyzed along with a vector of means, standard deviations, and number of observations, separately by American Indian and non-American Indian participants. This information allows interested readers to re-create the regression analyses. The data file also provides the summary data points used to create all figures.

Code availability

The SAS program code is available upon request to the first Author, who can provide the code via email.

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Acknowledgements

This work was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (5R01HD093651-05)(K.A.D.).

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Candice L. Odgers

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T.A.B. contributed to the conceptualization, methodology and formal analysis. B.B. contributed to the formal analysis and visualization. K.A.D. and J.E.L. contributed to the acquisition of data. C.L.O. and W.E.C. contributed to the interpretation of data. All authors contributed to writing, reviewing, and editing of the manuscript.

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Correspondence to Tim A. Bruckner .

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Bruckner, T.A., Bustos, B., Dodge, K.A. et al. Intergenerational effects of a casino-funded family transfer program on educational outcomes in an American Indian community. Nat Commun 15 , 8168 (2024). https://doi.org/10.1038/s41467-024-52428-w

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Received : 13 January 2023

Accepted : 06 September 2024

Published : 17 September 2024

DOI : https://doi.org/10.1038/s41467-024-52428-w

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