how to write a hypothesis in economics

The Young Economist’s Short Guide to Writing Economic Research

Attributes of writing economics.

• The discourse is often mathematical, with lots of formulas, lemmas, and proofs.
• Writing styles vary widely. Some authors are very dry and technical while a few are quite eloquent.

Economics writing is different from many other types of writing. It is essentially technical, and the primary goal is to achieve clarity. A clear presentation will allow the strength of your underlying analysis and the quality of your research to shine through.

Unlike prose writing in other disciplines, economics research takes time. Successful papers are not cranked out the night before a due date.

General Guidelines for Quality Research

Getting started.

The hardest part of any writing assignment is starting. Economics research usually begins with a strong understanding of literature, and papers require a section that summarizes and applies previous literature to what the paper at hand. This is the best way to start.

Your writing will demonstrate that you understand the findings that relate to the topic.

Economists use the first few paragraphs to set up research questions and the model and data they use to think about it. Sure, it can be dry, but this format ensures the write and reader have strong grasp on the subject and structure of the work that follows.

Clear and Concise Work

Clarity is hard to achieve, but revising and reworking a paper ensures it is easy to read

• Organize your ideas into an argument with the help of an outline.
• Define the important terms you will use
• State your hypothesis and proceed deductively to reach your conclusions
• Avoid excess verbiage
• Edit yourself, remove what is not needed, and keep revising until you get down to a simple, efficient way of communicating
• Use the active voice
• Put statements in positive form
• Omit needless words (concise writing is clear writing)
• In summaries, generally stick to one tense

Time Management

Poor time management can wreck the best-planned papers. Deadlines are key to successful research papers.

• Start the project by finding your topic
• Begin your research
• Start and outline
• Write a draft
• Revise and polish

The Language of Economic Analysis

Economic theory has become very mathematical. Most PhD students are mathematicians, not simply economics majors. This means most quality economic research requires a strong use of mathematical language. Economic analysis is characterized by the use of models, simplified representations of how economic phenomena work. A model’s predictions about the future or the past are essentially empirical hypotheses. Since economics is not easily tested in controlled experiments, research requires data from the real world (census reports, balance sheets), and statistical methods (regressions and econometrics) to test the predictive power of models and hypotheses based on those models.

The Writing Process

Finding a topic.

There are a million ways to find a topic. It may be that you are writing for a specific subfield of economics, so topics are limited and thus easier to pick. However, must research starts organically, from passive reading or striking news articles. Make sure to find something that interests you. Be sure to find a niche and make a contribution to the subfield.

You will also need a project that can be done within the parameters of the assignment (length, due date, access to research materials). A profoundly interesting topic may not be manageable given the time and other constraints you face. The key is to just be practical.

Be sure to start your research as soon as possible. Your topic will evolve along the way, and the question you begin with may become less interesting as new information draws you in other directions. It is perfectly fine to shape your topic based on available data, but don’t get caught up in endlessly revising topics.

Finding and Using Sources

There are two types of economic sources: empirical data (information that is or can be easily translated into numerical form), and academic literature (books and articles that help you organize your ideas).

Economic data is compiled into a number of useful secondary sources:

• Economic Report of the President
• Statistical Abstract of the United States
• National Longitudinal Survey
• Census data
• Academic journals

The Outline

A good outline acts as an agenda for the things you want to accomplish:

• Introduction: Pose an interesting question or problem
• Literature Review: Survey the literature on your topic
• Methods/Data: Formulate your hypothesis and describe your data
• Results: Present your results with the help of graphs and charts
• Discussion: Critique your method and/or discuss any policy implications
• Conclusions: Summarize what you have done; pose questions for further research

Writing a Literature Review

The literature review demonstrates your familiarity with scholarly work on your topic and lays the foundations for your paper. The particular issues you intent to raise, the terms you will employ, and the approach you will take should be defined with reference to previous scholarly works.

Presenting a Hypothesis

Formulate a question, problem or conjecture, and describe the approach you will take to answer, solve, or test it. In presenting your hypothesis, you need to discuss the data set you are using and the type of regression you will run. You should say where you found the data, and use a table, graph, or simple statistics to summarize them. In term papers, it may not be possible to reach conclusive results. Don’t be afraid to state this clearly and accurately. It is okay to have an inconclusive paper, but it is not okay to make overly broad and unsupported statements.

Presenting Results

There are essentially two decisions to make: (1) How many empirical results should be presented, and (2) How should these results be described in the text?

• Focus only on what is important and be as clear as possible. Both smart and dumb readers will appreciate you pointing things out directly and clearly.
• Less is usually more: Reporting a small group of relevant results is better than covering every possible statistical analysis that could be made on the data.
• Clearly and precisely describe your tables, graphs, and figures in the text of your results section. The first and last sentence in a paragraph describing a result should be “big picture” statements, describing how the results in the table, graph or figure fit into the overall theme of the paper.

Discussing Results

The key to discussing results is to stay clear of making value judgments, and rely instead on economic facts and analyses. It is not the job of an economist to draw policy conclusions, even if the research supports strong evidence in a particular direction.

Referencing Sources

As with any research paper, source referencing depends on the will of a professor a discourse community. However, economists generally use soft references in the literature review section and then cite sources in conventional formats at the end of papers.

This guide was made possible by the excellent work of Robert Neugeboren and Mireille Jacobson of Harvard University and Paul Dudenhefer of Duke University.

Mailing Address

Pomona College 333 N. College Way Claremont , CA 91711

Get in touch

Give back to pomona.

Part of   The Claremont Colleges

User Preferences

Content preview.

Arcu felis bibendum ut tristique et egestas quis:

• Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris
• Duis aute irure dolor in reprehenderit in voluptate
• Excepteur sint occaecat cupidatat non proident

Keyboard Shortcuts

5.2 - writing hypotheses.

The first step in conducting a hypothesis test is to write the hypothesis statements that are going to be tested. For each test you will have a null hypothesis (\(H_0\)) and an alternative hypothesis (\(H_a\)).

When writing hypotheses there are three things that we need to know: (1) the parameter that we are testing (2) the direction of the test (non-directional, right-tailed or left-tailed), and (3) the value of the hypothesized parameter.

• At this point we can write hypotheses for a single mean (\(\mu\)), paired means(\(\mu_d\)), a single proportion (\(p\)), the difference between two independent means (\(\mu_1-\mu_2\)), the difference between two proportions (\(p_1-p_2\)), a simple linear regression slope (\(\beta\)), and a correlation (\(\rho\)). 
• The research question will give us the information necessary to determine if the test is two-tailed (e.g., "different from," "not equal to"), right-tailed (e.g., "greater than," "more than"), or left-tailed (e.g., "less than," "fewer than").
• The research question will also give us the hypothesized parameter value. This is the number that goes in the hypothesis statements (i.e., \(\mu_0\) and \(p_0\)). For the difference between two groups, regression, and correlation, this value is typically 0.

Hypotheses are always written in terms of population parameters (e.g., \(p\) and \(\mu\)).  The tables below display all of the possible hypotheses for the parameters that we have learned thus far. Note that the null hypothesis always includes the equality (i.e., =).

1.3 How Economists Use Theories and Models to Understand Economic Issues

Learning objectives.

By the end of this section, you will be able to:

• Interpret a circular flow diagram
• Explain the importance of economic theories and models
• Describe goods and services markets and labor markets

John Maynard Keynes (1883–1946), one of the greatest economists of the twentieth century, pointed out that economics is not just a subject area but also a way of thinking. Keynes ( Figure 1.6 ) famously wrote in the introduction to a fellow economist’s book: “[Economics] is a method rather than a doctrine, an apparatus of the mind, a technique of thinking, which helps its possessor to draw correct conclusions.” In other words, economics teaches you how to think, not what to think.

Watch this video about John Maynard Keynes and his influence on economics.

Economists see the world through a different lens than anthropologists, biologists, classicists, or practitioners of any other discipline. They analyze issues and problems using economic theories that are based on particular assumptions about human behavior. These assumptions tend to be different than the assumptions an anthropologist or psychologist might use. A theory is a simplified representation of how two or more variables interact with each other. The purpose of a theory is to take a complex, real-world issue and simplify it down to its essentials. If done well, this enables the analyst to understand the issue and any problems around it. A good theory is simple enough to understand, while complex enough to capture the key features of the object or situation you are studying.

Sometimes economists use the term model instead of theory. Strictly speaking, a theory is a more abstract representation, while a model is a more applied or empirical representation. We use models to test theories, but for this course we will use the terms interchangeably.

For example, an architect who is planning a major office building will often build a physical model that sits on a tabletop to show how the entire city block will look after the new building is constructed. Companies often build models of their new products, which are more rough and unfinished than the final product, but can still demonstrate how the new product will work.

A good model to start with in economics is the circular flow diagram ( Figure 1.7 ). It pictures the economy as consisting of two groups—households and firms—that interact in two markets: the goods and services market in which firms sell and households buy and the labor market in which households sell labor to business firms or other employees.

Firms produce and sell goods and services to households in the market for goods and services (or product market). Arrow “A” indicates this. Households pay for goods and services, which becomes the revenues to firms. Arrow “B” indicates this. Arrows A and B represent the two sides of the product market. Where do households obtain the income to buy goods and services? They provide the labor and other resources (e.g., land, capital, raw materials) firms need to produce goods and services in the market for inputs (or factors of production). Arrow “C” indicates this. In return, firms pay for the inputs (or resources) they use in the form of wages and other factor payments. Arrow “D” indicates this. Arrows “C” and “D” represent the two sides of the factor market.

Of course, in the real world, there are many different markets for goods and services and markets for many different types of labor. The circular flow diagram simplifies this to make the picture easier to grasp. In the diagram, firms produce goods and services, which they sell to households in return for revenues. The outer circle shows this, and represents the two sides of the product market (for example, the market for goods and services) in which households demand and firms supply. Households sell their labor as workers to firms in return for wages, salaries, and benefits. The inner circle shows this and represents the two sides of the labor market in which households supply and firms demand.

This version of the circular flow model is stripped down to the essentials, but it has enough features to explain how the product and labor markets work in the economy. We could easily add details to this basic model if we wanted to introduce more real-world elements, like financial markets, governments, and interactions with the rest of the globe (imports and exports).

Economists carry a set of theories in their heads like a carpenter carries around a toolkit. When they see an economic issue or problem, they go through the theories they know to see if they can find one that fits. Then they use the theory to derive insights about the issue or problem. Economists express theories as diagrams, graphs, or even as mathematical equations. (Do not worry. In this course, we will mostly use graphs.) Economists do not figure out the answer to the problem first and then draw the graph to illustrate. Rather, they use the graph of the theory to help them figure out the answer. Although at the introductory level, you can sometimes figure out the right answer without applying a model, if you keep studying economics, before too long you will run into issues and problems that you will need to graph to solve. We explain both micro and macroeconomics in terms of theories and models. The most well-known theories are probably those of supply and demand, but you will learn a number of others.

As an Amazon Associate we earn from qualifying purchases.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Access for free at https://openstax.org/books/principles-economics-3e/pages/1-introduction

• Authors: Steven A. Greenlaw, David Shapiro, Daniel MacDonald
• Publisher/website: OpenStax
• Book title: Principles of Economics 3e
• Publication date: Dec 14, 2022
• Location: Houston, Texas
• Book URL: https://openstax.org/books/principles-economics-3e/pages/1-introduction
• Section URL: https://openstax.org/books/principles-economics-3e/pages/1-3-how-economists-use-theories-and-models-to-understand-economic-issues

© Jan 23, 2024 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

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.

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)

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

Introduction

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

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

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

What is the simple definition of a hypothesis?

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

What is the hypothesis for in research?

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

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

What is an example of a hypothesis?

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

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

What makes a good hypothesis?

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

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

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

Null hypothesis

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

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

Alternative hypothesis

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

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

Simple hypothesis

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

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

Complex hypothesis

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

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

Directional hypothesis

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

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

Statistical hypothesis

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

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

Empirical hypothesis

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

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

Causal hypothesis

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

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

Associative hypothesis

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

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

Relational hypothesis

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

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

Logical hypothesis

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

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

Let ATLAS.ti take you from research question to key insights

Get started with a free trial and see how ATLAS.ti can make the most of your data.

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

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

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

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

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

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

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

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

Literature review

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

Research methods

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

Preliminary research

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

Data analysis

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

Turn data into evidence for insights with ATLAS.ti

Powerful analysis for your research paper or presentation is at your fingertips starting with a free trial.

• How it works

How to Write a Hypothesis – Steps & Tips

Published by Alaxendra Bets at August 14th, 2021 , Revised On October 26, 2023

What is a Research Hypothesis?

You can test a research statement with the help of experimental or theoretical research, known as a hypothesis.

If you want to find out the similarities, differences, and relationships between variables, you must write a testable hypothesis before compiling the data, performing analysis, and generating results to complete.

The data analysis and findings will help you test the hypothesis and see whether it is true or false. Here is all you need to know about how to write a hypothesis for a  dissertation .

Research Hypothesis Definition

Not sure what the meaning of the research hypothesis is?

A research hypothesis predicts an answer to the research question  based on existing theoretical knowledge or experimental data.

Some studies may have multiple hypothesis statements depending on the research question(s).  A research hypothesis must be based on formulas, facts, and theories. It should be testable by data analysis, observations, experiments, or other scientific methodologies that can refute or support the statement.

Variables in Hypothesis

Developing a hypothesis is easy. Most research studies have two or more variables in the hypothesis, particularly studies involving correlational and experimental research. The researcher can control or change the independent variable(s) while measuring and observing the independent variable(s).

“How long a student sleeps affects test scores.”

In the above statement, the dependent variable is the test score, while the independent variable is the length of time spent in sleep. Developing a hypothesis will be easy if you know your research’s dependent and independent variables.

Once you have developed a thesis statement, questions such as how to write a hypothesis for the dissertation and how to test a research hypothesis become pretty straightforward.

Looking for dissertation help?

Researchprospect to the rescue then.

We have expert writers on our team who are skilled at helping students with quantitative dissertations across a variety of STEM disciplines. Guaranteeing 100% satisfaction!

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

Here are the steps involved in how to write a hypothesis for a dissertation.

Step 1: Start with a Research Question

• Begin by asking a specific question about a topic of interest.
• This question should be clear, concise, and researchable.

Example: Does exposure to sunlight affect plant growth?

Step 2: Do Preliminary Research

• Before formulating a hypothesis, conduct background research to understand existing knowledge on the topic.
• Familiarise yourself with prior studies, theories, or observations related to the research question.

Step 3: Define Variables

• Independent Variable (IV): The factor that you change or manipulate in an experiment.
• Dependent Variable (DV): The factor that you measure.

Example: IV: Amount of sunlight exposure (e.g., 2 hours/day, 4 hours/day, 8 hours/day) DV: Plant growth (e.g., height in centimetres)

Step 4: Formulate the Hypothesis

• A hypothesis is a statement that predicts the relationship between variables.
• It is often written as an “if-then” statement.

Example: If plants receive more sunlight, then they will grow taller.

Step 5: Ensure it is Testable

A good hypothesis is empirically testable. This means you should be able to design an experiment or observation to test its validity.

Example: You can set up an experiment where plants are exposed to varying amounts of sunlight and then measure their growth over a period of time.

Step 6: Consider Potential Confounding Variables

• Confounding variables are factors other than the independent variable that might affect the outcome.
• It is important to identify these to ensure that they do not skew your results.

Example: Soil quality, water frequency, or type of plant can all affect growth. Consider keeping these constant in your experiment.

Step 7: Write the Null Hypothesis

• The null hypothesis is a statement that there is no effect or no relationship between the variables.
• It is what you aim to disprove or reject through your research.

Example: There is no difference in plant growth regardless of the amount of sunlight exposure.

Step 8: Test your Hypothesis

Design an experiment or conduct observations to test your hypothesis.

Example: Grow three sets of plants: one set exposed to 2 hours of sunlight daily, another exposed to 4 hours, and a third exposed to 8 hours. Measure and compare their growth after a set period.

Step 9: Analyse the Results

After testing, review your data to determine if it supports your hypothesis.

Step 10: Draw Conclusions

• Based on your findings, determine whether you can accept or reject the hypothesis.
• Remember, even if you reject your hypothesis, it’s a valuable result. It can guide future research and refine questions.

Three Ways to Phrase a Hypothesis

Try to use “if”… and “then”… to identify the variables. The independent variable should be present in the first part of the hypothesis, while the dependent variable will form the second part of the statement. Consider understanding the below research hypothesis example to create a specific, clear, and concise research hypothesis;

If an obese lady starts attending Zomba fitness classes, her health will improve.

In academic research, you can write the predicted variable relationship directly because most research studies correlate terms.

The number of Zomba fitness classes attended by the obese lady has a positive effect on health.

If your research compares two groups, then you can develop a hypothesis statement on their differences.

An obese lady who attended most Zumba fitness classes will have better health than those who attended a few.

How to Write a Null Hypothesis

If a statistical analysis is involved in your research, then you must create a null hypothesis. If you find any relationship between the variables, then the null hypothesis will be the default position that there is no relationship between them. H0 is the symbol for the null hypothesis, while the hypothesis is represented as H1. The null hypothesis will also answer your question, “How to test the research hypothesis in the dissertation.”

H0: The number of Zumba fitness classes attended by the obese lady does not affect her health.

H1: The number of Zumba fitness classes attended by obese lady positively affects health.

Also see:  Your Dissertation in Education

Hypothesis Examples

Research Question: Does the amount of sunlight a plant receives affect its growth? Hypothesis: Plants that receive more sunlight will grow taller than plants that receive less sunlight.

Research Question: Do students who eat breakfast perform better in school exams than those who don’t? Hypothesis: Students who eat a morning breakfast will score higher on school exams compared to students who skip breakfast.

Research Question: Does listening to music while studying impact a student’s ability to retain information? Hypothesis 1 (Directional): Students who listen to music while studying will retain less information than those who study in silence. Hypothesis 2 (Non-directional): There will be a difference in information retention between students who listen to music while studying and those who study in silence.

How can ResearchProspect Help?

If you are unsure about how to rest a research hypothesis in a dissertation or simply unsure about how to develop a hypothesis for your research, then you can take advantage of our dissertation services which cover every tiny aspect of a dissertation project you might need help with including but not limited to setting up a hypothesis and research questions,  help with individual chapters ,  full dissertation writing ,  statistical analysis , and much more.

Frequently Asked Questions

What are the 5 rules for writing a good hypothesis.

• Clear Statement: State a clear relationship between variables.
• Testable: Ensure it can be investigated and measured.
• Specific: Avoid vague terms, be precise in predictions.
• Falsifiable: Design to allow potential disproof.
• Relevant: Address research question and align with existing knowledge.

What is a hypothesis in simple words?

A hypothesis is an educated guess or prediction about something that can be tested. It is a statement that suggests a possible explanation for an event or phenomenon based on prior knowledge or observation. Scientists use hypotheses as a starting point for experiments to discover if they are true or false.

What is the hypothesis and examples?

A hypothesis is a testable prediction or explanation for an observation or phenomenon. For example, if plants are given sunlight, then they will grow. In this case, the hypothesis suggests that sunlight has a positive effect on plant growth. It can be tested by experimenting with plants in varying light conditions.

What is the hypothesis in research definition?

A hypothesis in research is a clear, testable statement predicting the possible outcome of a study based on prior knowledge and observation. It serves as the foundation for conducting experiments or investigations. Researchers test the validity of the hypothesis to draw conclusions and advance knowledge in a particular field.

Why is it called a hypothesis?

The term “hypothesis” originates from the Greek word “hypothesis,” which means “base” or “foundation.” It’s used to describe a foundational statement or proposition that can be tested. In scientific contexts, it denotes a tentative explanation for a phenomenon, serving as a starting point for investigation or experimentation.

You May Also Like

This article is a step-by-step guide to how to write statement of a problem in research. The research problem will be half-solved by defining it correctly.

Penning your dissertation proposal can be a rather daunting task. Here are comprehensive guidelines on how to write a dissertation proposal.

Repository of ten perfect research question examples will provide you a better perspective about how to create research questions.

USEFUL LINKS

LEARNING RESOURCES

COMPANY DETAILS

• How It Works

Choose Your Test

Sat / act prep online guides and tips, what is a hypothesis and how do i write one.

General Education

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

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

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

So let’s get started!

What Is a Hypothesis?

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

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

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

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

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

Independent and Dependent Variables 

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

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

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

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

Elements of a Good Hypothesis

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

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

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

Writing Your Hypothesis

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

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

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

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

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

The Two Types of Hypotheses

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

#1: If-Then Hypotheses

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

If ____ happens, then ____ will happen.

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

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

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

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

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

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

#2: Null Hypotheses

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

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

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

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

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

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

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

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

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

4 Tips to Write the Best Hypothesis

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

#1: Plausibility

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

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

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

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

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

#2: Defined Concepts

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

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

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

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

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

Good hypotheses ensure that you can observe the results. 

#3: Observability

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

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

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

#4: Generalizability 

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

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

Hypothesis Testing Examples

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

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

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

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

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

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

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

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

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

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

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

Here’s what your hypotheses might look like: 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Key Takeaways: Hypothesis Writing

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

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

What’s Next?

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

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

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

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

Ask a Question Below

Have any questions about this article or other topics? Ask below and we'll reply!

Improve With Our Famous Guides

• For All Students

The 5 Strategies You Must Be Using to Improve 160+ SAT Points

How to Get a Perfect 1600, by a Perfect Scorer

Series: How to Get 800 on Each SAT Section:

Score 800 on SAT Math

Score 800 on SAT Reading

Score 800 on SAT Writing

Series: How to Get to 600 on Each SAT Section:

Score 600 on SAT Math

Score 600 on SAT Reading

Score 600 on SAT Writing

Free Complete Official SAT Practice Tests

What SAT Target Score Should You Be Aiming For?

15 Strategies to Improve Your SAT Essay

The 5 Strategies You Must Be Using to Improve 4+ ACT Points

How to Get a Perfect 36 ACT, by a Perfect Scorer

Series: How to Get 36 on Each ACT Section:

36 on ACT English

36 on ACT Math

36 on ACT Reading

36 on ACT Science

Series: How to Get to 24 on Each ACT Section:

24 on ACT English

24 on ACT Math

24 on ACT Reading

24 on ACT Science

What ACT target score should you be aiming for?

ACT Vocabulary You Must Know

ACT Writing: 15 Tips to Raise Your Essay Score

How to Get Into Harvard and the Ivy League

How to Get a Perfect 4.0 GPA

How to Write an Amazing College Essay

What Exactly Are Colleges Looking For?

Is the ACT easier than the SAT? A Comprehensive Guide

Should you retake your SAT or ACT?

When should you take the SAT or ACT?

Stay Informed

Get the latest articles and test prep tips!

Looking for Graduate School Test Prep?

Check out our top-rated graduate blogs here:

GRE Online Prep Blog

GMAT Online Prep Blog

TOEFL Online Prep Blog

Holly R. "I am absolutely overjoyed and cannot thank you enough for helping me!”

• Foundations
• Write Paper

Search form

• Experiments
• Anthropology
• Self-Esteem
• Social Anxiety

• Research Paper >

How to Write a Hypothesis

Often, one of the trickiest parts of designing and writing up any research paper is writing the hypothesis.

This article is a part of the guide:

• Outline Examples
• Example of a Paper
• Introduction
• Example of a Paper 2

Browse Full Outline

• 1 Write a Research Paper
• 2 Writing a Paper
• 3.1 Write an Outline
• 3.2 Outline Examples
• 4.1 Thesis Statement
• 4.2 Write a Hypothesis
• 5.2 Abstract
• 5.3 Introduction
• 5.4 Methods
• 5.5 Results
• 5.6 Discussion
• 5.7 Conclusion
• 5.8 Bibliography
• 6.1 Table of Contents
• 6.2 Acknowledgements
• 6.3 Appendix
• 7.1 In Text Citations
• 7.2 Footnotes
• 7.3.1 Floating Blocks
• 7.4 Example of a Paper
• 7.5 Example of a Paper 2
• 7.6.1 Citations
• 7.7.1 Writing Style
• 7.7.2 Citations
• 8.1.1 Sham Peer Review
• 8.1.2 Advantages
• 8.1.3 Disadvantages
• 8.2 Publication Bias
• 8.3.1 Journal Rejection
• 9.1 Article Writing
• 9.2 Ideas for Topics

The entire experiment revolves around the research hypothesis (H 1 ) and the null hypothesis (H 0 ), so making a mistake here could ruin the whole design .

Needless to say, it can all be a little intimidating, and many students find this to be the most difficult stage of the scientific method .

In fact, it is not as difficult as it looks, and if you have followed the steps of the scientific process and found an area of research and potential research problem , then you may already have a few ideas.

It is just about making sure that you are asking the right questions and wording your hypothesis statements correctly.

Once you have nailed down a promising hypothesis, the rest of the process will flow a lot more easily.

The Three-Step Process

It can quite difficult to isolate a testable hypothesis after all of the research and study. The best way is to adopt a three-step hypothesis; this will help you to narrow things down, and is the most foolproof guide to how to write a hypothesis.

Step one is to think of a general hypothesis, including everything that you have observed and reviewed during the information gathering stage of any research design . This stage is often called developing the research problem .

An Example of How to Write a Hypothesis

A worker on a fish-farm notices that his trout seem to have more fish lice in the summer, when the water levels are low, and wants to find out why. His research leads him to believe that the amount of oxygen is the reason - fish that are oxygen stressed tend to be more susceptible to disease and parasites.

He proposes a general hypothesis.

“Water levels affect the amount of lice suffered by rainbow trout.”

This is a good general hypothesis, but it gives no guide to how to design the research or experiment . The hypothesis must be refined to give a little direction.

“Rainbow trout suffer more lice when water levels are low.”

Now there is some directionality, but the hypothesis is not really testable , so the final stage is to design an experiment around which research can be designed, i.e. a testable hypothesis.

“Rainbow trout suffer more lice in low water conditions because there is less oxygen in the water.”

This is a testable hypothesis - he has established variables , and by measuring the amount of oxygen in the water, eliminating other controlled variables , such as temperature, he can see if there is a correlation against the number of lice on the fish.

This is an example of how a gradual focusing of research helps to define how to write a hypothesis .

The Next Stage - What to Do with the Hypothesis

Once you have your hypothesis , the next stage is to design the experiment , allowing a statistical analysis of data, and allowing you to test your hypothesis .

The statistical analysis will allow you to reject either the null or the alternative hypothesis. If the alternative is rejected, then you need to go back and refine the initial hypothesis or design a completely new research program.

This is part of the scientific process, striving for greater accuracy and developing ever more refined hypotheses.

• Psychology 101
• Flags and Countries
• Capitals and Countries

Martyn Shuttleworth (Aug 1, 2009). How to Write a Hypothesis. Retrieved May 07, 2024 from Explorable.com: https://explorable.com/how-to-write-a-hypothesis

You Are Allowed To Copy The Text

The text in this article is licensed under the Creative Commons-License Attribution 4.0 International (CC BY 4.0) .

This means you're free to copy, share and adapt any parts (or all) of the text in the article, as long as you give appropriate credit and provide a link/reference to this page.

That is it. You don't need our permission to copy the article; just include a link/reference back to this page. You can use it freely (with some kind of link), and we're also okay with people reprinting in publications like books, blogs, newsletters, course-material, papers, wikipedia and presentations (with clear attribution).

Want to stay up to date? Follow us!

Check out the official book.

Learn how to construct, style and format an Academic paper and take your skills to the next level.

(also available as ebook )

Save this course for later

Don't have time for it all now? No problem, save it as a course and come back to it later.

Footer bottom

• Privacy Policy

• Subscribe to our RSS Feed
• Like us on Facebook
• Follow us on Twitter

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

• Search Search Please fill out this field.

What Is Hypothesis Testing?

Step 1: define the hypothesis, step 2: set the criteria, step 3: calculate the statistic, step 4: reach a conclusion, types of errors, the bottom line.

• Trading Skills
• Trading Basic Education

Hypothesis Testing in Finance: Concept and Examples

Charlene Rhinehart is a CPA , CFE, chair of an Illinois CPA Society committee, and has a degree in accounting and finance from DePaul University.

Your investment advisor proposes you a monthly income investment plan that promises a variable return each month. You will invest in it only if you are assured of an average $180 monthly income. Your advisor also tells you that for the past 300 months, the scheme had investment returns with an average value of $190 and a standard deviation of $75. Should you invest in this scheme? Hypothesis testing comes to the aid for such decision-making.

Key Takeaways

• Hypothesis testing is a mathematical tool for confirming a financial or business claim or idea.
• Hypothesis testing is useful for investors trying to decide what to invest in and whether the instrument is likely to provide a satisfactory return.
• Despite the existence of different methodologies of hypothesis testing, the same four steps are used: define the hypothesis, set the criteria, calculate the statistic, and reach a conclusion.
• This mathematical model, like most statistical tools and models, has limitations and is prone to certain errors, necessitating investors also considering other models in conjunction with this one

Hypothesis or significance testing is a mathematical model for testing a claim, idea or hypothesis about a parameter of interest in a given population set, using data measured in a sample set. Calculations are performed on selected samples to gather more decisive information about the characteristics of the entire population, which enables a systematic way to test claims or ideas about the entire dataset.

Here is a simple example: A school principal reports that students in their school score an average of 7 out of 10 in exams. To test this “hypothesis,” we record marks of say 30 students (sample) from the entire student population of the school (say 300) and calculate the mean of that sample. We can then compare the (calculated) sample mean to the (reported) population mean and attempt to confirm the hypothesis.

To take another example, the annual return of a particular mutual fund is 8%. Assume that mutual fund has been in existence for 20 years. We take a random sample of annual returns of the mutual fund for, say, five years (sample) and calculate its mean. We then compare the (calculated) sample mean to the (claimed) population mean to verify the hypothesis.

This article assumes readers' familiarity with concepts of a normal distribution table, formula, p-value and related basics of statistics.

Different methodologies exist for hypothesis testing, but the same four basic steps are involved:

Usually, the reported value (or the claim statistics) is stated as the hypothesis and presumed to be true. For the above examples, the hypothesis will be:

• Example A: Students in the school score an average of 7 out of 10 in exams.
• Example B: The annual return of the mutual fund is 8% per annum.

This stated description constitutes the “ Null Hypothesis (H 0 ) ” and is  assumed  to be true – the way a defendant in a jury trial is presumed innocent until proven guilty by the evidence presented in court. Similarly, hypothesis testing starts by stating and assuming a “ null hypothesis ,” and then the process determines whether the assumption is likely to be true or false.

The important point to note is that we are testing the null hypothesis because there is an element of doubt about its validity. Whatever information that is against the stated null hypothesis is captured in the  Alternative Hypothesis (H 1 ).  For the above examples, the alternative hypothesis will be:

• Students score an average that is not equal to 7.
• The annual return of the mutual fund is not equal to 8% per annum.

In other words, the alternative hypothesis is a direct contradiction of the null hypothesis.

As in a trial, the jury assumes the defendant's innocence (null hypothesis). The prosecutor has to prove otherwise (alternative hypothesis). Similarly, the researcher has to prove that the null hypothesis is either true or false. If the prosecutor fails to prove the alternative hypothesis, the jury has to let the defendant go (basing the decision on the null hypothesis). Similarly, if the researcher fails to prove an alternative hypothesis (or simply does nothing), then the null hypothesis is assumed to be true.

The decision-making criteria have to be based on certain parameters of datasets.

The decision-making criteria have to be based on certain parameters of datasets and this is where the connection to normal distribution comes into the picture.

As per the standard statistics postulate  about sampling distribution , “For any sample size n, the sampling distribution of X̅ is normal if the population X from which the sample is drawn is normally distributed.” Hence, the probabilities of all other possible sample mean that one could select are normally distributed.

For e.g., determine if the average daily return, of any stock listed on XYZ stock market , around New Year's Day is greater than 2%.

H 0 : Null Hypothesis: mean = 2%

H 1 : Alternative Hypothesis: mean > 2% (this is what we want to prove)

Take the sample (say of 50 stocks out of total 500) and compute the mean of the sample.

For a normal distribution, 95% of the values lie within two standard deviations of the population mean. Hence, this normal distribution and central limit assumption for the sample dataset allows us to establish 5% as a significance level. It makes sense as, under this assumption, there is less than a 5% probability (100-95) of getting outliers that are beyond two standard deviations from the population mean. Depending upon the nature of datasets, other significance levels can be taken at 1%, 5% or 10%. For financial calculations (including behavioral finance), 5% is the generally accepted limit. If we find any calculations that go beyond the usual two standard deviations, then we have a strong case of outliers to reject the null hypothesis.  

Graphically, it is represented as follows:

In the above example, if the mean of the sample is much larger than 2% (say 3.5%), then we reject the null hypothesis. The alternative hypothesis (mean >2%) is accepted, which confirms that the average daily return of the stocks is indeed above 2%.

However, if the mean of the sample is not likely to be significantly greater than 2% (and remains at, say, around 2.2%), then we CANNOT reject the null hypothesis. The challenge comes on how to decide on such close range cases. To make a conclusion from selected samples and results, a level of significance is to be determined, which enables a conclusion to be made about the null hypothesis. The alternative hypothesis enables establishing the level of significance or the "critical value” concept for deciding on such close range cases.

According to the textbook standard definition , “A critical value is a cutoff value that defines the boundaries beyond which less than 5% of sample means can be obtained if the null hypothesis is true. Sample means obtained beyond a critical value will result in a decision to reject the null hypothesis."   In the above example, if we have defined the critical value as 2.1%, and the calculated mean comes to 2.2%, then we reject the null hypothesis. A critical value establishes a clear demarcation about acceptance or rejection.

This step involves calculating the required figure(s), known as test statistics (like mean, z-score , p-value , etc.), for the selected sample. (We'll get to these in a later section.)

With the computed value(s), decide on the null hypothesis. If the probability of getting a sample mean is less than 5%, then the conclusion is to reject the null hypothesis. Otherwise, accept and retain the null hypothesis.

There can be four possible outcomes in sample-based decision-making, with regard to the correct applicability to the entire population:

The “Correct” cases are the ones where the decisions taken on the samples are truly applicable to the entire population. The cases of errors arise when one decides to retain (or reject) the null hypothesis based on the sample calculations, but that decision does not really apply for the entire population. These cases constitute Type 1 ( alpha ) and Type 2 ( beta ) errors, as indicated in the table above.

Selecting the correct critical value allows eliminating the type-1 alpha errors or limiting them to an acceptable range.

Alpha denotes the error on the level of significance and is determined by the researcher. To maintain the standard 5% significance or confidence level for probability calculations, this is retained at 5%.

According to the applicable decision-making benchmarks and definitions:

• “This (alpha) criterion is usually set at 0.05 (a = 0.05), and we compare the alpha level to the p-value. When the probability of a Type I error is less than 5% (p < 0.05), we decide to reject the null hypothesis; otherwise, we retain the null hypothesis.”  
• The technical term used for this probability is the p-value . It is defined as “the probability of obtaining a sample outcome, given that the value stated in the null hypothesis is true. The p-value for obtaining a sample outcome is compared to the level of significance."  
• A Type II error, or beta error, is defined as the probability of incorrectly retaining the null hypothesis, when in fact it is not applicable to the entire population.  

A few more examples will demonstrate this and other calculations.

A monthly income investment scheme exists that promises variable monthly returns. An investor will invest in it only if they are assured of an average $180 monthly income. The investor has a sample of 300 months’ returns which has a mean of $190 and a standard deviation of $75. Should they invest in this scheme?

Let’s set up the problem. The investor will invest in the scheme if they are assured of the investor's desired $180 average return.

H 0 : Null Hypothesis: mean = 180

H 1 : Alternative Hypothesis: mean > 180

Method 1: Critical Value Approach

Identify a critical value X L for the sample mean, which is large enough to reject the null hypothesis – i.e. reject the null hypothesis if the sample mean >= critical value X L

P (identify a Type I alpha error) = P(reject H 0  given that H 0  is true),

This would be achieved when the sample mean exceeds the critical limits.

= P (given that H 0  is true) = alpha

Graphically, it appears as follows:

Taking alpha = 0.05 (i.e. 5% significance level), Z 0.05  = 1.645 (from the Z-table or normal distribution table)

           = > X L  = 180 +1.645*(75/sqrt(300)) = 187.12

Since the sample mean (190) is greater than the critical value (187.12), the null hypothesis is rejected, and the conclusion is that the average monthly return is indeed greater than $180, so the investor can consider investing in this scheme.

Method 2: Using Standardized Test Statistics

One can also use standardized value z.

Test Statistic, Z = (sample mean – population mean) / (std-dev / sqrt (no. of samples).

Then, the rejection region becomes the following:

Z= (190 – 180) / (75 / sqrt (300)) = 2.309

Our rejection region at 5% significance level is Z> Z 0.05  = 1.645.

Since Z= 2.309 is greater than 1.645, the null hypothesis can be rejected with a similar conclusion mentioned above.

Method 3: P-value Calculation

We aim to identify P (sample mean >= 190, when mean = 180).

= P (Z >= (190- 180) / (75 / sqrt (300))

= P (Z >= 2.309) = 0.0084 = 0.84%

The following table to infer p-value calculations concludes that there is confirmed evidence of average monthly returns being higher than 180:

A new stockbroker (XYZ) claims that their brokerage fees are lower than that of your current stock broker's (ABC). Data available from an independent research firm indicates that the mean and std-dev of all ABC broker clients are $18 and $6, respectively.

A sample of 100 clients of ABC is taken and brokerage charges are calculated with the new rates of XYZ broker. If the mean of the sample is $18.75 and std-dev is the same ($6), can any inference be made about the difference in the average brokerage bill between ABC and XYZ broker?

H 0 : Null Hypothesis: mean = 18

H 1 : Alternative Hypothesis: mean <> 18 (This is what we want to prove.)

Rejection region: Z <= - Z 2.5  and Z>=Z 2.5  (assuming 5% significance level, split 2.5 each on either side).

Z = (sample mean – mean) / (std-dev / sqrt (no. of samples))

= (18.75 – 18) / (6/(sqrt(100)) = 1.25

This calculated Z value falls between the two limits defined by:

- Z 2.5  = -1.96 and Z 2.5  = 1.96.

This concludes that there is insufficient evidence to infer that there is any difference between the rates of your existing broker and the new broker.

Alternatively, The p-value = P(Z< -1.25)+P(Z >1.25)

= 2 * 0.1056 = 0.2112 = 21.12% which is greater than 0.05 or 5%, leading to the same conclusion.

Graphically, it is represented by the following:

Criticism Points for the Hypothetical Testing Method:

• A statistical method based on assumptions
• Error-prone as detailed in terms of alpha and beta errors
• Interpretation of p-value can be ambiguous, leading to confusing results

Hypothesis testing allows a mathematical model to validate a claim or idea with a certain confidence level. However, like the majority of statistical tools and models, it is bound by a few limitations. The use of this model for making financial decisions should be considered with a critical eye, keeping all dependencies in mind. Alternate methods like  Bayesian Inference are also worth exploring for similar analysis.

Sage Publications. " Introduction to Hypothesis Testing ," Page 13.

Sage Publications. " Introduction to Hypothesis Testing ," Page 11.

Sage Publications. " Introduction to Hypothesis Testing ," Page 7.

Sage Publications. " Introduction to Hypothesis Testing ," Pages 10-11.

• Terms of Service
• Editorial Policy
• Privacy Policy
• Your Privacy Choices

The Plagiarism Checker Online For Your Academic Work

Start Plagiarism Check

Editing & Proofreading for Your Research Paper

Get it proofread now

Online Printing & Binding with Free Express Delivery

Configure binding now

• Academic essay overview
• The writing process
• Structuring academic essays
• Types of academic essays
• Academic writing overview
• Sentence structure
• Academic writing process
• Improving your academic writing
• Titles and headings
• APA style overview
• APA citation & referencing
• APA structure & sections
• Citation & referencing
• Structure and sections
• APA examples overview
• Commonly used citations
• Other examples
• British English vs. American English
• Chicago style overview
• Chicago citation & referencing
• Chicago structure & sections
• Chicago style examples
• Citing sources overview
• Citation format
• Citation examples
• College essay overview
• Application
• How to write a college essay
• Types of college essays
• Commonly confused words
• Definitions
• Dissertation overview
• Dissertation structure & sections
• Dissertation writing process
• Graduate school overview
• Application & admission
• Study abroad
• Master degree
• Harvard referencing overview
• Language rules overview
• Grammatical rules & structures
• Parts of speech
• Punctuation
• Methodology overview
• Analyzing data
• Experiments
• Observations
• Inductive vs. Deductive
• Qualitative vs. Quantitative
• Types of validity
• Types of reliability
• Sampling methods
• Theories & Concepts
• Types of research studies
• Types of variables
• MLA style overview
• MLA examples
• MLA citation & referencing
• MLA structure & sections
• Plagiarism overview
• Plagiarism checker
• Types of plagiarism
• Printing production overview
• Research bias overview
• Types of research bias
• Example sections
• Types of research papers
• Research process overview
• Problem statement
• Research proposal
• Research topic
• Statistics overview
• Levels of measurment
• Frequency distribution
• Measures of central tendency
• Measures of variability
• Hypothesis testing
• Parameters & test statistics
• Types of distributions
• Correlation
• Effect size
• Hypothesis testing assumptions
• Types of ANOVAs
• Types of chi-square
• Statistical data
• Statistical models
• Spelling mistakes
• Tips overview
• Academic writing tips
• Dissertation tips
• Sources tips
• Working with sources overview
• Evaluating sources
• Finding sources
• Including sources
• Types of sources

Your Step to Success

Plagiarism Check within 10min

Printing & Binding with 3D Live Preview

Hypothesis – Definition, Development & Examples

How do you like this article cancel reply.

Save my name, email, and website in this browser for the next time I comment.

A hypothesis is a fundamental element in academic writing and research papers , offering a tentative explanation or prediction about relationships between variables. It is crafted based on preliminary observations or existing knowledge, guiding the direction of research. The development of a hypothesis involves observation, research, and the formulation of a testable statement, critical for structuring and advancing your academic inquiry.

Inhaltsverzeichnis

• 1 Hypothesis in a nutshell
• 2 Definition: Hypothesis
• 3 Developing a hypothesis
• 4 Hypothesis examples
• 5 Null hypothesis and alternative hypothesis
• 6 Statistical hypothesis testing
• 7 Scientific hypothesis
• 8 Hypotheses as concepts & measurements

Hypothesis in a nutshell

A hypothesis is like a smart guess about what might happen or why something happens, based on what you already know. It’s a prediction you can test to see whether it’s true or not.

Definition: Hypothesis

A hypothesis , plural hypotheses, is a tentative explanation or prediction about a phenomenon or the relationship between variables that can be tested through observation and experimentation. According to Oxford Languages, the term hypothesis is “a supposition or proposed explanation made based on limited evidence as a starting point for further investigation” or “a proposition made as a basis for reasoning, without any assumption of its truth.” It’s an informed conjecture that is formulated based on existing knowledge, observations, and available evidence. Well-constructed hypotheses are clear, specific, and testable, stating an expected outcome in a way that can be supported or refuted through empirical research. It often takes the form of a statement predicting a relationship between variables, such as cause and effect, and serves as a foundational element in the scientific method, guiding the direction of research and experimentation. Hypotheses are found across different subject areas, like:

• Astronomical hypotheses
• Authorship debates
• Biological hypotheses
• Documentary hypothesis
• Hypothetical documents
• Hypothetical impact events
• Hypothetical laws
• Linguistic theories and hypotheses
• Meteorological hypotheses
• Hypothetical objects
• Origin hypotheses of ethnic groups
• Hypothetical processes
• Hypothetical spacecraft

Statistical hypothesis testing

• Hypothetical technology

If you notice that plants in your garden grow better in the sunlight than in the shade, you might come up with a hypothesis like: “Plants that get more sunlight grow faster than plants that get less sunlight.” This is a prediction you can test by observing and measuring the growth of plants in different lighting conditions.

There are typically two main types of variables: Independent vs. dependent variables .

• Independent variable: This is the variable that you change or control in an experiment to see how it affects the dependent variable. It’s the cause you’re investigating.
• Dependent variable: This is the variable you measure in the experiment and expect to change as a result of alterations to the independent variable. It’s the effect or outcome you’re observing.

Directionality is also part of a hypothesis; however, it is only an optional component. It specifies if the effect increases or decreases, showing the expected relationship between the variables.

• Hypothesis: If fertilizer is added to plants, then they will grow taller.
• Independent variable: Presence of fertilizer (what you control).
• Dependent variable: Height of the plants (the outcome you’re measuring).

Conceptual framework

A conceptual framework plays a crucial role in the development and contextualization of hypotheses within research. It acts as a scaffold that outlines the theoretical underpinnings and assumptions guiding the research, providing a structured approach to understanding the relationships between variables. The conceptual framework helps to:

• Identify and define key concepts
• Establish relationships
• Guide the research design
• Interpret findings

The word comes from the Greek term “hupóthesis,” which means “foundation” or “supposition.” Let’s break it down into its separate parts:

• “hupo-“ means “under”
• “-thesis” means “placing” or “setting”

The etymology reflects the idea of setting down a foundation or a preliminary assumption that can be tested and built upon through research and observation.

Developing a hypothesis

Developing a hypothesis is an integral part of academic writing because it guides the direction of the study, helping to formulate questions, determine methodology , and analyze results. By stating what the writer expects to find, hypotheses lay the groundwork for a structured investigation, allowing for a systematic approach to gathering and interpreting data.

Preparation

Experimentation, null hypothesis.

In this step, you’ll determine the research question to which you want the answer. The question should be concise, specific, and researchable based on observations or existing knowledge.

Observing that some children seem to get sick less often than others, you might wonder, “Do children who visit the doctor yearly get sick less often?”

The results you expect to get should be supported by the information already known about the topic. This includes reviewing existing literature, studies, and data. Use previous research to make an educated guess as to what your research will find. Journals and reports are also good places to find information. This will help you figure out your variables and what direction your research will take, which is the overriding principle of how to write a hypothesis. In this step, you may create a conceptual framework.

Researching how regular health check-ups might influence the frequency of illness in children, including immunity, early detection of issues, etc.

Based on your research, you propose a tentative explanation or prediction that addresses your question. This should be specific and testable. Now that you have a bit of research under your belt, you can write the answer you expect to find when you conduct your research. Like the question you pose in your hypothesis, this statement should be clear, concise, and to the point.

Children who have yearly doctor’s visits have fewer illnesses compared to children who do not have yearly doctor’s visits.

In this step, you’ll outline why your question is researchable. Design and experiment to test whether the data supports your hypothesis. This often involves data collection through observations or experiments , discussing your variables, who or what you’ll be studying, and what you expect to happen with experimentation or analysis.

Design a study comparing the health records of two groups of children over a year – those who have yearly doctor visits and those who do not.

Make sure the wording of your hypothesis aligns with your purpose. This could be an “if-then” statement. It could also use a correlation between the variables. It might be what differences you expect to find. After the experiment, analyze the collected data to see if it supports your hypothesis. This step may involve statistical analysis to determine the significance of your findings.

Analyzing the frequency of illness in both groups to see if there is a statistically significant difference in health outcomes, leads to a conclusion about the impact of yearly doctor’s visits on children’s health.

The last step involves formulating a null hypothesis (H₀) , which is a statement that there is no effect or no difference, and it serves as a default or neutral position that researchers aim to test against the alternative hypothesis (H₁) .

The null hypothesis is designed to be tested and possibly rejected in favor of the alternative hypothesis. It’s a critical component because it allows for a precise and statistical examination of the expected relationship between variables.

• Null hypothesis (H₁): Children who visit the doctor yearly get sick less often than those who don’t.
• Alternative hypothesis (H₀): Yearly visits to the doctor do not affect the frequency of illnesses in children; children who visit the doctor yearly get sick just as often as those who do not.

Hypothesis examples

There are many ways to address how to write a hypothesis, but the process isn’t always easy. Sometimes seeing a research example is a good way to figure out what direction you want to take. Below are some examples that can help guide you.

Null hypothesis and alternative hypothesis

The null and alternative hypotheses are fundamental concepts in hypothesis testing , a core aspect of statistical analysis used to infer the relationship between variables.

Alternative hypothesis

The alternative hypothesis (H₁) proposes that there is a significant difference or relationship between the variables. The researcher aims to support it, suggesting that the observed effects are not due to chance but are real and can be attributed to the specified conditions or treatments. Below, you will find examples that illustrate using an alternative hypothesis.

• Eating breakfast improves students’ academic performance.
• Regular physical activity reduces the risk of chronic diseases.
• Increased screen time is associated with decreased sleep quality in adults.

The null hypothesis (H₀) asserts that there is no significant difference or relationship between the variables being studied. It represents the default position that any observed effects are due to chance. The purpose of hypothesis testing is often to reject or fail to reject the null hypothesis.

• Eating breakfast does not affect students’ academic performance.
• Regular physical activity does not reduce the risk of chronic diseases.
• Increased screen time is not associated with decreased sleep quality in adults.

Statistical hypothesis testing is a scientific method used in statistics to decide whether there is enough evidence in a sample of data to infer that a certain condition is true for the entire population. It starts with formulating two hypotheses: the null hypothesis (H₀), which suggests no effect or no difference, and the alternative hypothesis (H₁ or Ha), which suggests a significant effect or difference. Researchers then use statistical tests to calculate a p-value , which indicates the probability of observing the data if the null hypothesis is true. If the p-value is less than a predefined significance level (commonly 0.05), the null hypothesis is rejected in favor of the alternative hypothesis, suggesting the observed effect is statistically significant. This process helps researchers make informed conclusions based on sample data , though it’s important to consider, it only indicates the null hypothesis is unlikely given the data.

Scientific hypothesis

A scientific hypothesis is a specific, testable prediction about what will happen under certain conditions, based on a combination of existing knowledge, observations, and an understanding of scientific principles. It suggests a possible explanation for a phenomenon or a predicted relationship between variables that researchers aim to explore through empirical investigation.

When formulating a scientific hypothesis, researchers should consider the following:

• Empirical testability: Allows it to be tested through observation or experimentation.
• Clarity and specificity: Should specify the expected relationship between variables.
• Consistency: Must be consistent with existing scientific theories and knowledge.
• Simplicity: If two hypotheses explain the data equally well, the simpler one is preferred.
• Falsifiability:  Evidence can refute a hypothesis, proving it wrong.

Hypotheses as concepts & measurements

In the realm of scientific research, hypotheses serve as bridges between abstract concepts and tangible measurements, enabling researchers to rigorously test theories and explore the laws of nature. At the core of this process lies the differentiation between primitive and derivative hypotheses, each playing a vital role in the systematic inquiry into natural phenomena.

Primitive hypotheses are foundational assumptions or propositions taken as given within a particular research context. These hypotheses are often broader in scope, serving as the bedrock upon which more specific, testable propositions are built. They usually stem from established theories or widespread observations, acting as the initial building blocks in the formulation of a conceptual framework.

Derivative Hypotheses , on the other hand, are those that are directly formulated from primitive hypotheses. They are more specific, testable statements that predict outcomes based on the foundational assumptions of the primitive hypotheses. Derivative hypotheses are crucial for the empirical testing of theories, as they translate broad theoretical concepts into specific, measurable predictions.

Levels for testing hypotheses reflect the complexity and hierarchical structuring of research questions, ranging from broad, theoretical propositions to specific, empirical inquiries. This gradation ensures that hypotheses can be systematically tested across different layers of abstraction, from verifying fundamental laws of nature to assessing the applicability of theories in specific contexts. Levels for testing hypotheses often range from very broad, theoretical inquiries to more specific, empirical investigations.

The testing of hypotheses is a methodical process that involves comparing empirical data against predictions made by derivative hypotheses . This critical step allows researchers to assess the validity of their hypotheses, refine theoretical models, and contribute to the body of scientific knowledge. Successful hypothesis testing typically relies on sophisticated statistical analysis, providing a quantifiable measure of the extent to which the data supports or refutes the proposed hypotheses.

Ultimately, the interplay between hypotheses as concepts and their empirical measurements is fundamental to advancing our understanding of the law of nature . Through the meticulous testing of hypotheses, science progresses from abstract theories to concrete understanding, uncovering the principles that govern our world. This iterative process of formulation, testing, and refinement is essential for the continuous evolution of scientific knowledge, driving forward the quest for truths of the law of nature.

What is a hypothesis?

Your hypothesis is a statement that lets the reader of your paper know what your research question is and what you expect the answer to be.

How to write a hypothesis?

A well-written hypothesis should include:

• The question you want to answer with your research
• Information about what you already know about the topic
• A clear sentence that says what you expect your results to show
• The variables involved in the research
• The group that is part of the research
• The outcome you think you’ll see

What is an example of a hypothesis?

Here’s an example:

If people sleep less than seven hours a night, then they will feel more tired during the day compared to those who sleep more than seven hours.

What components does a hypothesis have?

The components are:

• Independent variable
• Dependent variable
• Directionality

We use cookies on our website. Some of them are essential, while others help us to improve this website and your experience.

• External Media

Individual Privacy Preferences

Cookie Details Privacy Policy Imprint

Here you will find an overview of all cookies used. You can give your consent to whole categories or display further information and select certain cookies.

Accept all Save

Essential cookies enable basic functions and are necessary for the proper function of the website.

Show Cookie Information Hide Cookie Information

Statistics cookies collect information anonymously. This information helps us to understand how our visitors use our website.

Content from video platforms and social media platforms is blocked by default. If External Media cookies are accepted, access to those contents no longer requires manual consent.

Privacy Policy Imprint

• Business Essentials
• Leadership & Management
• Credential of Leadership, Impact, and Management in Business (CLIMB)
• Entrepreneurship & Innovation
• Digital Transformation
• Finance & Accounting
• Business in Society
• For Organizations
• Support Portal
• Media Coverage
• Founding Donors
• Leadership Team

• Harvard Business School →
• HBS Online →
• Business Insights →

Business Insights

Harvard Business School Online's Business Insights Blog provides the career insights you need to achieve your goals and gain confidence in your business skills.

• Career Development
• Communication
• Decision-Making
• Earning Your MBA
• Negotiation
• News & Events
• Productivity
• Staff Spotlight
• Student Profiles
• Work-Life Balance
• AI Essentials for Business
• Alternative Investments
• Business Analytics
• Business Strategy
• Business and Climate Change
• Design Thinking and Innovation
• Digital Marketing Strategy
• Disruptive Strategy
• Economics for Managers
• Entrepreneurship Essentials
• Financial Accounting
• Global Business
• Launching Tech Ventures
• Leadership Principles
• Leadership, Ethics, and Corporate Accountability
• Leading with Finance
• Management Essentials
• Negotiation Mastery
• Organizational Leadership
• Power and Influence for Positive Impact
• Strategy Execution
• Sustainable Business Strategy
• Sustainable Investing
• Winning with Digital Platforms

A Beginner’s Guide to Hypothesis Testing in Business

• 30 Mar 2021

Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.

If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.

Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.

Access your free e-book today.

What Is Hypothesis Testing?

To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.

A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”

Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.

Hypothesis Testing in Business

When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.

The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.

As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.

In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.

Related: 9 Fundamental Data Science Skills for Business Professionals

Key Considerations for Hypothesis Testing

1. alternative hypothesis and null hypothesis.

In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.

For example, consider a company’s leadership team that historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.

In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”

The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.

Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.

2. Significance Level and P-Value

Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.

With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.

In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.

3. One-Sided vs. Two-Sided Testing

When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.

Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.

4. Sampling

To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.

A survey involves asking a series of questions to a random population sample and recording self-reported responses.

Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.

Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.

Learn How to Perform Hypothesis Testing

Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.

If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.

Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .

About the Author

• How To Write A Hypothesis

How to Write a Hypothesis: Types and Tips to Remember

Many people might not know what a hypothesis is, the purpose of a hypothesis or where a hypothesis is needed. A hypothesis is a statement that explains the research’s predictions and the reasons behind the research. It is an “educated guess” of the final result of the research problem and is written for an academic research paper. A good hypothesis is carefully stated as a key aspect of the scientific method, yet even the simplest ones can be difficult to explain.

If you are unaware of the process of writing a hypothesis, we are here to help you with all your queries. Read the article and learn how to write a hypothesis for your academic paper/thesis.

Table of Contents

What is a hypothesis.

• Simple Hypothesis
• Complex Hypothesis
• Null Hypothesis
• Alternative Hypothesis
• Logical Hypothesis
• Empirical Hypothesis
• Statistical Hypothesis

Writing a Good Hypothesis – Points to Remember

• How to Write a Hypothesis

Frequently Asked Questions on How to Write a Hypothesis

A hypothesis is prepared in the early stages of a research project. Based on the preliminary research observations, a hypothesis is framed. It is the prediction of the end result of the research problem. For example, suppose you have observed that the plants grow up better with regular watering. In that case, your hypothesis can be “Plants grow better with regular watering”. Once you have got your hypothesis, you can begin the experiments required to support and prove it.

A hypothesis must include variables. It can be some events, objects or concepts which are to be observed and tested for your research experiments. There are two kinds of variables – dependent variables and independent variables. The independent variables are the ones which can be modified in the experiment, and the dependent variables are the ones which can only be observed.

Hypotheses are a crucial part of the research paper since they influence the direction and arrangement of the research methods. The readers will want to know if the hypothesis was proven right or wrong, and therefore it must be mentioned clearly in the introduction or the abstract of the paper.

Types of Hypotheses

Depending on the nature of the research and the findings, the hypothesis can be categorised into one or more of the seven major categories.

1. Simple Hypothesis

A simple hypothesis states the relationship between the two variables (dependent and independent variables).

2. Complex Hypothesis

A complex hypothesis entails the existence of a relationship between two or more variables. It can be two dependent variables and one independent variable or vice versa.

3. Null Hypothesis

A null hypothesis is a statement that states that the variables have no relationship.

4. Alternative Hypothesis

The null hypothesis is the polar opposite of the alternative hypothesis. It states that the two variables under study have a link (one variable has an effect on the other).

5. Logical Hypothesis

In the absence of verifiable proof, a logical hypothesis indicates a relationship between variables. Assertions are based on inference or logic rather than evidence.

6. Empirical Hypothesis

An empirical hypothesis, often known as a “working hypothesis,” is one that is being researched right now. Empirical hypotheses, unlike logical assumptions, are supported by evidence.

7. Statistical Hypothesis

When you test a sample of a population and then use the collected statistical evidence to draw conclusions about the full population, you’ve generated a statistical hypothesis. You test a section of it and then make an educated guess about the rest based on the results.

A good hypothesis is written following the same format and guidelines. To write a good hypothesis, the below-mentioned information has to be added.

Causes and Effects: A hypothesis always includes a cause-and-effect relationship where one variable causes another to change or not change, depending on the type of hypothesis.

Measurable Prediction: Other than logical hypotheses, most hypotheses are designed to be tested. Before you commit to any hypothesis, make sure that it is experimented. Select a testable hypothesis involving an independent variable over which you can have complete control.

Dependent and Independent Variables: You can define the type of variables of your research for the readers.

Language used in a Hypothesis: Make sure to write the hypothesis in simple and clear language.

Adhere to Ethics: Before conducting your research, keep an eye on what you are experimenting with. Those hypotheses which are objectionable, questionable or taboo can be avoided unless they are absolutely necessary.

How to Write a Hypothesis?

A good hypothesis can be written in the following six steps.

Asking a Question

Arousing curiosity in the minds of the readers can be a good way to start a hypothesis. It would make the readers think about the topic critically.

Conducting a Preliminary Research

Before writing the hypothesis, it is essential to get background information regarding the topic. The preliminary research can be done through various web searches, reading books, etc.

Defining the Variables

After you have decided on your hypothesis, you can now decide on your variables. Keep in mind that the independent variables are the ones over which you have complete control and accordingly decide the limits of your hypothesis.

Writing the Hypothesis in the “if-then” Statement

While writing a hypothesis, keep in mind that it must be written in an “if-then” format statement which is a reliable method of expressing the causes and effects. A simple example would be, “If we water the plants daily, then they might grow really well.”

Collection of Adequate Data to Back the Hypothesis

A hypothesis is written to reach the conclusion of the research. After writing the hypothesis, the experiments can be conducted. See to it that you collect the adequate data needed to support the hypothesis.

Writing with Confidence

After you have collected enough data, you can start writing the hypothesis. Make sure you write confidently, without any errors. It would be good to get your writing counter-checked by an expert if you are not confident about it.

What is a hypothesis?

A hypothesis is a statement that explains the research’s predictions and the reasons behind the research. It is written based on various observations.

Why is a hypothesis important?

A hypothesis is important in an academic paper because it explains the result of the research problem. It will help the researcher, as well as the audience, to stay focused and not deviate from the main idea.

• Share Share

Register with BYJU'S & Download Free PDFs

Register with byju's & watch live videos.