how to write a sentence with hypothesis

How to Write a Hypothesis [31 Tips + Examples]

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

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

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

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

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

Writing a Hypothesis: The Basics

Table of Contents

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

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

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

31 Tips for Writing a Hypothesis

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

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

Tip 1: Start with a Clear Research Question

A clear research question ensures your hypothesis is targeted.

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

Tip 2: Do Background Research

Research helps you understand current knowledge and any existing gaps.

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

Tip 3: Identify Independent and Dependent Variables

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

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

Tip 4: Make Your Hypothesis Testable

A hypothesis must be measurable and falsifiable.

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

Tip 5: Be Specific and Concise

Keep your hypothesis straightforward and to the point.

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

Tip 6: Choose Simple Language

Use simple, understandable language to ensure clarity.

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

Tip 7: Formulate a Null Hypothesis

A null hypothesis assumes no relationship between variables.

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

Tip 8: State Alternative Hypotheses

Provide alternative hypotheses to explore other plausible relationships.

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

Tip 9: Use “If…Then” Statements

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

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

Tip 10: Avoid Assumptions

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

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

Tip 11: Review Existing Literature

Previous research offers insights into forming a hypothesis.

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

Tip 12: Consider Multiple Variables

Hypotheses with multiple variables can offer deeper insights.

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

Tip 13: Review Ethical Considerations

Ethics are essential for trustworthy research.

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

Tip 14: Test with Pilot Studies

Small-scale pilot studies test feasibility and refine hypotheses.

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

Tip 15: Build Hypotheses on Existing Theories

Existing theories provide strong foundations.

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

Tip 16: Address Real-World Problems

Solve real-world problems through practical hypotheses.

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

Tip 17: Aim for Clear, Measurable Outcomes

The results should be easy to measure and interpret.

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

Tip 18: Stay Open to Unexpected Results

Not all hypotheses yield expected results.

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

Tip 19: Keep Hypotheses Relevant

Ensure your hypothesis aligns with the purpose of your research.

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

Tip 20: Collaborate with Peers

Collaboration strengthens hypothesis development.

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

Tip 21: Re-evaluate Hypotheses Periodically

Revising hypotheses ensures relevance.

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

Tip 22: Develop Compelling Visuals

Illustrating hypotheses can help communicate relationships effectively.

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

Tip 23: Refine Your Data Collection Plan

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

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

Tip 24: Focus on Logical Progression

Ensure your hypothesis logically follows your research question.

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

Tip 25: Test Against Diverse Samples

Testing across diverse samples ensures broader applicability.

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

Tip 26: Use Control Groups

Control groups provide a baseline for comparison.

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

Tip 27: Consider Practical Constraints

Work within realistic constraints for your resources and timeline.

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

Tip 28: Recognize Bias Risks

Biases can skew hypothesis formation.

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

Tip 29: Prepare for Peer Review

Peer review ensures your hypothesis holds up to scrutiny.

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

Tip 30: Create a Research Proposal

A proposal outlines your hypothesis, methodology, and significance.

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

Tip 31: Document Your Findings

Recording findings helps validate or challenge your hypothesis.

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

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

Hypothesis Examples for Different Situations

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

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

Professional Contacts

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

Super Personal

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

Final Thoughts: How to Write a Hypothesis

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

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

Stay curious and let your research journey unfold.

Read This Next:

• How to Write a Topic Sentence (30+ Tips & Examples)
• How to Describe a Graph in Writing [+ 22 Examples]
• How to Write an Address (21+ Examples)
• How to Write an Email (Ultimate Guide + 60 Examples)
• How to Write a Recommendation Letter (Examples & Templates)

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

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

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

Prevent plagiarism, run a free check.

Step 1: ask a question.

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

Step 2: Do some preliminary research

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

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

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 26 May 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

Is this article helpful?

Shona McCombes

Other students also liked, operationalisation | a guide with examples, pros & cons, what is a conceptual framework | tips & examples, a quick guide to experimental design | 5 steps & examples.

• Affiliate Program

• UNITED STATES
• 台灣 (TAIWAN)
• TÜRKIYE (TURKEY)
• Academic Editing Services
• - Research Paper
• - Journal Manuscript
• - Dissertation
• - College & University Assignments
• Admissions Editing Services
• - Application Essay
• - Personal Statement
• - Recommendation Letter
• - Cover Letter
• - CV/Resume
• Business Editing Services
• - Business Documents
• - Report & Brochure
• - Website & Blog
• Writer Editing Services
• - Script & Screenplay
• Our Editors
• Client Reviews
• Editing & Proofreading Prices
• Wordvice Points
• Partner Discount
• Plagiarism Checker
• APA Citation Generator
• MLA Citation Generator
• Chicago Citation Generator
• Vancouver Citation Generator
• - APA Style
• - MLA Style
• - Chicago Style
• - Vancouver Style
• Writing & Editing Guide
• Academic Resources
• Admissions Resources

How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

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

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

What is the difference between a hypothesis and a prediction?

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

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

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

Types of Research Hypotheses

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

Alternative Hypothesis

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

Null Hypothesis

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

Directional Hypothesis

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

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

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

Your null hypothesis would then be that

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

Nondirectional Hypothesis

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

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

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

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

How to Write a Hypothesis for a Research Paper

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

Writing a Hypothesis Step:1

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

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

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

Writing a Hypothesis Step 2:

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

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

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

Writing a Hypothesis Step 3:

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

Examples of a Good and Bad Hypothesis

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

Good Hypothesis Examples

Bad hypothesis examples, tips for writing a research hypothesis.

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

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

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

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

Perfect Your Manuscript With Professional Editing

Now that you know how to write a strong research hypothesis for your research paper, you might be interested in our free AI proofreader , Wordvice AI, which finds and fixes errors in grammar, punctuation, and word choice in academic texts. Or if you are interested in human proofreading , check out our English editing services , including research paper editing and manuscript editing .

On the Wordvice academic resources website , you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

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!”

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

Related Articles

Research Methodology

Qualitative Data Coding

What Is a Focus Group?

Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

How to Write a Hypothesis

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

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

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

Hypothesis Definition

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

Get Help With Writing a Hypothesis Now!

Head on over to EssayPro. We can help you with editing and polishing up any of the work you speedwrite.

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

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

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

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

Characteristics and Sources of a Hypothesis

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

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

The main sources of a hypothesis are:

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

Types of Hypothesis

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

• Alternative Hypothesis

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

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

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

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

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

• Null Hypothesis

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

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

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

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

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

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

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

For example: Eating more vegetables leads to better immunity.

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

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

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

Be sure to read how to write a DBQ - this article will expand your understanding.

Add a secret ingredient to your hypothesis

Help of a professional writer.

Hypothesis vs Prediction

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

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

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

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

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

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

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

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

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

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

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

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

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

1. Define Your Research Question

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

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

2. Conduct Your Basic Initial Research

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

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

3. Formulate a Hypothesis

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

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

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

4. Refine Your Hypothesis

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

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

In case you need some help with your essay, leave us a notice ' pay someone to write my essay ' and we'll help asap. We also provide nursing writing services .

Hypothesis Examples

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

Ask Pros to Make a Perfect Hypothesis for You!

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

is an expert in nursing and healthcare, with a strong background in history, law, and literature. Holding advanced degrees in nursing and public health, his analytical approach and comprehensive knowledge help students navigate complex topics. On EssayPro blog, Adam provides insightful articles on everything from historical analysis to the intricacies of healthcare policies. In his downtime, he enjoys historical documentaries and volunteering at local clinics.

Related Articles

• 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."

• EXPLORE Random Article

How to Write a Hypothesis for an Essay

Last Updated: September 16, 2021

wikiHow is a “wiki,” similar to Wikipedia, which means that many of our articles are co-written by multiple authors. To create this article, volunteer authors worked to edit and improve it over time. This article has been viewed 22,067 times.

A hypothesis is an educated guess as to what will happen, given a certain set of circumstances. [1] X Research source Hypotheses are often used in science. Scientists look at a given set of circumstances or parameters and make an educated guess about how those circumstances affect something else. Then, they test that guess. Essays are often used to write up the results of these experiments. But before you ever write this type of essay, you have to choose and write out a hypothesis to test.

Narrowing Down Your Topic

• It’s easy to use the library database at your local library. Once you locate the library’s databases, you should pick out ones that are focused on science articles.
• Most libraries have some form of EBSCOhost, and if you use advanced search, you can select the databases you want, such as Science and Technology or Science Reference Center.
• Once you’ve decided on databases, you can use search terms to find what you need. Ask your librarian can help you if you’re having trouble.

• As a high school student, you’ll want to stick to more basic stuff; you can find databases geared towards your level, and your librarian should be able to point you in the right direction.
• If you’re a college student, you should be able to use most of what you find in the academic databases. You can also use your textbook to help you decide what you want to study, as well as whose theories you will base your own experiment on.
• For instance, maybe you want to study Gregor Mendel’s techniques with genetics and plants.

• It’s best to keep all of the bibliographical information together so you can find it again. Just make sure you jot down the name of the author when you begin taking notes from a source, so you know what bibliographic entry it came from.
• You should also note where you found the article or book, as well, so you can go back to it if you need to do further research.

• It is possible to be too narrow, but it is easier to expand it a bit if you need to rather than to condense it after you’ve tried to tackle too much research. If you are a younger student, such as a high school student, you may just want to repeat Mendel’s experiments to see how they work.
• If you are an older student, such as a graduate student, your work will need to be more original. You will need to put your own spin on plants and genetics. Maybe you want to study how splicing together two plants changes the genes of the plant over time.

Composing the Hypothesis

• That is, with a paper on hybrids, you might want to make one category on Mendel’s research, one for newer studies that are similar, one for splicing, and one for the type of cucumber you are using.

• To conduct the experiment, you will splice plants that manifest certain characteristics to see which produces the desired results. In this case, you are manipulating genes by picking plants for certain characteristics.
• According to Explorable, the point of an experiment is to change one variable while controlling other ones and watching for changes. [3] X Research source That is, with the cucumbers, you would need a control, such as splicing one set of cucumber plants at random, noting its characteristics, instead of choosing for a particular characteristic. Then you compare the fruit each type of plants produce.

• Also include how you plan to carry out the experiment and what you expect to happen. Because a hypothesis is a guess about what will happen, you have to spell out for your reader what you're thinking.
• Start putting it together into a formal sentence. Basically, your hypothesis is how you tell your reader in one concise sentence what you are going to do. You are boiling it down as much as possible.

• For instance, for this experiment, you could write something like, “This experiment will test the hypothesis that selecting Armenian cucumbers (scientific name Cucumis melo var. flexuosus) for crispiness and splicing those plants together will, over time, produce a crisper cucumber, and this hypothesis will be tested by selecting cucumbers for crispiness to splice with cucumbers with similar traits, along with a control group for comparing results.”
• This hypothesis is specific, it tells what you want to do, and it gives an idea of how you are going to do it.

Expert Q&A

• Essentially, to write a hypothesis, you need to pick a field and narrow down to an experiment you want to conduct. Make an educated guess about that experiment, and write it up formally for your paper. Thanks Helpful 0 Not Helpful 0
• For instance, a kid doing science project might guess that a plant will grow better if it is fed tea rather than water. It is an educated guess because the kid knows that tea has more nutrients than water, so it might help it grow faster. The kid will then test the hypothesis by performing a set of experiments over time, comparing a plant growing with just water to one growing with tea, to prove whether her hypothesis was correct or not. Thanks Helpful 0 Not Helpful 0
• Remember that you will not necessarily prove your hypothesis is correct. The point of the experiment is to see if you are right, but you may not be. The outcome of the experiment should not affect the quality of the essay one way or the other. Thanks Helpful 0 Not Helpful 0

You Might Also Like

• ↑ http://dictionary.reference.com/browse/hypothesis
• ↑ http://writingcenter.waldenu.edu/314.htm
• ↑ https://explorable.com/experimental-research

About this article

Did this article help you.

• About wikiHow
• Terms of Use
• Privacy Policy
• Do Not Sell or Share My Info
• Not Selling Info

• Manuscript Preparation

What is and How to Write a Good Hypothesis in Research?

• 4 minute read
• 317.8K views

Table of Contents

One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

Language Editing Plus

Elsevier’s Language Editing Plus service can help ensure that your research hypothesis is well-designed, and articulates your research and conclusions. Our most comprehensive editing package, you can count on a thorough language review by native-English speakers who are PhDs or PhD candidates. We’ll check for effective logic and flow of your manuscript, as well as document formatting for your chosen journal, reference checks, and much more.

• Research Process

Systematic Literature Review or Literature Review?

What is a Problem Statement? [with examples]

You may also like.

Make Hook, Line, and Sinker: The Art of Crafting Engaging Introductions

Can Describing Study Limitations Improve the Quality of Your Paper?

A Guide to Crafting Shorter, Impactful Sentences in Academic Writing

6 Steps to Write an Excellent Discussion in Your Manuscript

How to Write Clear and Crisp Civil Engineering Papers? Here are 5 Key Tips to Consider

The Clear Path to An Impactful Paper: ②

The Essentials of Writing to Communicate Research in Medicine

Changing Lines: Sentence Patterns in Academic Writing

Input your search keywords and press Enter.

• Researching
• 7. Hypothesis

How to write a hypothesis

Once you have created your three topic sentences , you are ready to create your hypothesis.

What is a 'hypothesis'?

A hypothesis is a single sentence answer to the Key Inquiry Question  that clearly states what your entire essay is going to argue.

It contains both the argument and the main reasons in support of your argument. Each hypothesis should clearly state the ‘answer’ to the question, followed by a ‘why’.

For Example:  

The Indigenous people of Australia were treated as second-class citizens until the 1960’s (answer) by the denial of basic political rights by State and Federal governments (why) .

How do you create a hypothesis?

Back in Step 3 of the research process, you split your Key Inquiry Question into three sub-questions .

Then at Step 6 you used the quotes from your Source Research to create answers to each of the sub-questions. These answers became your three Topic Sentences .

To create your hypothesis, you need to combine the three Topic Sentences into a single sentence answer.

By combining your three answers to the sub-questions , you are ultimately providing a complete answer to the original Key Inquiry Question .

For example:

What's next?

Need a digital Research Journal?

Additional resources

What do you need help with, download ready-to-use digital learning resources.

Copyright © History Skills 2014-2024.

Contact  via email

Statistics Made Easy

How to Write Hypothesis Test Conclusions (With Examples)

A   hypothesis test is used to test whether or not some hypothesis about a population parameter is true.

To perform a hypothesis test in the real world, researchers obtain a random sample from the population and perform a hypothesis test on the sample data, using a null and alternative hypothesis:

• Null Hypothesis (H 0 ): The sample data occurs purely from chance.
• Alternative Hypothesis (H A ): The sample data is influenced by some non-random cause.

If the p-value of the hypothesis test is less than some significance level (e.g. α = .05), then we reject the null hypothesis .

Otherwise, if the p-value is not less than some significance level then we fail to reject the null hypothesis .

When writing the conclusion of a hypothesis test, we typically include:

• Whether we reject or fail to reject the null hypothesis.
• The significance level.
• A short explanation in the context of the hypothesis test.

For example, we would write:

We reject the null hypothesis at the 5% significance level.   There is sufficient evidence to support the claim that…

Or, we would write:

We fail to reject the null hypothesis at the 5% significance level.   There is not sufficient evidence to support the claim that…

The following examples show how to write a hypothesis test conclusion in both scenarios.

Example 1: Reject the Null Hypothesis Conclusion

Suppose a biologist believes that a certain fertilizer will cause plants to grow more during a one-month period than they normally do, which is currently 20 inches. To test this, she applies the fertilizer to each of the plants in her laboratory for one month.

She then performs a hypothesis test at a 5% significance level using the following hypotheses:

• H 0 : μ = 20 inches (the fertilizer will have no effect on the mean plant growth)
• H A : μ > 20 inches (the fertilizer will cause mean plant growth to increase)

Suppose the p-value of the test turns out to be 0.002.

Here is how she would report the results of the hypothesis test:

We reject the null hypothesis at the 5% significance level.   There is sufficient evidence to support the claim that this particular fertilizer causes plants to grow more during a one-month period than they normally do.

Example 2: Fail to Reject the Null Hypothesis Conclusion

Suppose the manager of a manufacturing plant wants to test whether or not some new method changes the number of defective widgets produced per month, which is currently 250. To test this, he measures the mean number of defective widgets produced before and after using the new method for one month.

He performs a hypothesis test at a 10% significance level using the following hypotheses:

• H 0 : μ after = μ before (the mean number of defective widgets is the same before and after using the new method)
• H A : μ after ≠ μ before (the mean number of defective widgets produced is different before and after using the new method)

Suppose the p-value of the test turns out to be 0.27.

Here is how he would report the results of the hypothesis test:

We fail to reject the null hypothesis at the 10% significance level.   There is not sufficient evidence to support the claim that the new method leads to a change in the number of defective widgets produced per month.

Additional Resources

The following tutorials provide additional information about hypothesis testing:

Introduction to Hypothesis Testing 4 Examples of Hypothesis Testing in Real Life How to Write a Null Hypothesis

Featured Posts

Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Join the Statology Community

Sign up to receive Statology's exclusive study resource: 100 practice problems with step-by-step solutions. Plus, get our latest insights, tutorials, and data analysis tips straight to your inbox!

By subscribing you accept Statology's Privacy Policy.

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., =).

26 Types of Punctuation Marks & Typographical Symbols

• What Is Punctuation?
• What Is A Typographical Symbol?
• Punctuation Vs. Typographical Symbols
• Types Of Punctuation And Symbols
• Try Grammar Coach

We use words in writing. Shocking, I know! Do you know what else we use in writing? Here is a hint: they have already appeared in this paragraph. In addition to words, we use many different symbols and characters to organize our thoughts and make text easier to read. All of these symbols come in two major categories: punctuation marks and typographical symbols . These symbols have many different uses and include everything from the humble period ( . ) to the rarely used caret symbol ( ^ ). There may even be a few symbols out there that you’ve never even heard of before that leave you scratching your head when you see them on your keyboard!

What is punctuation ?

Punctuation is the act or system of using specific marks or symbols in writing to separate different elements from each other or to make writing more clear. Punctuation is used in English and the other languages that use the Latin alphabet. Many other writing systems also use punctuation, too. Thanks to punctuation, we don’t have to suffer through a block of text that looks like this:

• My favorite color is red do you like red red is great my sister likes green she always says green is the color of champions regardless of which color is better we both agree that no one likes salmon which is a fish and not a color seriously

Punctuation examples

The following sentences give examples of the many different punctuation marks that we use:

• My dog , Bark Scruffalo , was featured in a superhero movie . 
• If there ’ s something strange in your neighborhood , who are you going to call ?
• A wise man once said , “ Within the body of every person lies a skeleton .”
• Hooray ! I found everything on the map : the lake , the mountain , and the forest . 
• I told Ashley ( if that was her real name ) that I needed the copy lickety-split .

What is a typographical symbol ?

The term typographical symbol , or any other number of phrases, refers to a character or symbol that isn’t considered to be a punctuation mark but may still be used in writing for various purposes. Typographical symbols are generally avoided in formal writing under most circumstances. However, you may see typographic symbols used quite a bit in informal writing.

Typographical symbol examples

The following examples show some ways that a writer might use typographical symbols. Keep in mind that some of these sentences may not be considered appropriate in formal writing.

• The frustrated actor said she was tired of her co-star’s “annoying bull **** .”
• For questions, email us at anascabana @ bananacabanas.fake!
• The band had five # 1 singles on the American music charts during the 1990s.
• My internet provider is AT & T.

⚡️ Punctuation vs. typographical symbols

Punctuation marks are considered part of grammar and often have well-established rules for how to use them properly. For example, the rules of proper grammar state that a letter after a period should be capitalized and that a comma must be used before a coordinating conjunction.

Typographical symbols, on the other hand, may not have widely accepted rules for how, or even when, they should be used. Generally speaking, most grammar resources will only allow the use of typographical symbols under very specific circumstances and will otherwise advise a writer to avoid using them.

Types of punctuation and symbols

There are many different types of punctuation marks and typographical symbols. We’ll briefly touch on them now, but you can learn more about these characters by checking out the links in this list and also each section below:

• Question mark
• Exclamation point
• Parentheses
• Square brackets
• Curly brackets
• Angle brackets
• Quotation marks
• Bullet point
• Pound symbol
• Caret symbol
• Pipe symbol

Period, question mark, and exclamation point

These three commonly used punctuation marks are used for the same reason: to end an independent thought.

A period is used to end a declarative sentence . A period indicates that a sentence is finished.

• Today is Friday .

Unique to them, periods are also often used in abbreviations.

• Prof . Dumbledore once again awarded a ludicrous amount of points to Gryffindor.

Question mark (?)

The question mark is used to end a question, also known as an interrogative sentence .

• Do you feel lucky ?

Exclamation point (!)

The exclamation point is used at the end of exclamations and interjections .

• Our house is haunted ! 

Comma, colon, and semicolon

Commas, colons, and semicolons can all be used to connect sentences together.

The comma is often the punctuation mark that gives writers the most problems. It has many different uses and often requires good knowledge of grammar to avoid making mistakes when using it. Some common uses of the comma include:

• Joining clauses: Mario loves Peach , and she loves him . 
• Nonrestrictive elements: My favorite team , the Fighting Mongooses , won the championship this year.
• Lists: The flag was red , white , and blue.
• Coordinate adjectives: The cute , happy puppy licked my hand.

Try out this quiz on the Oxford comma!

The colon is typically used to introduce additional information.

• The detective had three suspects : the salesman, the gardener, and the lawyer.

Like commas, colons can also connect clauses together.

• We forgot to ask the most important question : who was buying lunch?

Colons have a few other uses, too.

• The meeting starts at 8:15 p.m.
• The priest started reading from Mark 3:6 .

Semicolon (;)

Like the comma and the colon, the semicolon is used to connect sentences together. The semicolon typically indicates that the second sentence is closely related to the one before it.

• I can’t eat peanuts ; I am highly allergic to them.
• Lucy loves to eat all kinds of sweets ; lollipops are her favorite.

Hyphen and dashes (en dash and em dash)

All three of these punctuation marks are often referred to as “dashes.” However, they are all used for entirely different reasons.

The hyphen is used to form compound words.

• I went to lunch with my father-in-law .
• She was playing with a jack-in-the-box .
• He was accused of having pro-British sympathies.

En dash (–)

The en dash is used to express ranges or is sometimes used in more complex compound words.

• The homework exercises are on pages 20–27 .
• The songwriter had worked on many Tony Award–winning productions.

Em dash (—)

The em dash is used to indicate a pause or interrupted speech.

• The thief was someone nobody expected —me !
• “Those kids will— ” was all he managed to say before he was hit by a water balloon.

Test your knowledge on the different dashes here.

Parentheses, brackets, and braces

These pairs of punctuation marks look similar, but they all have different uses. In general, the parentheses are much more commonly used than the others.

Parentheses ()

Typically, parentheses are used to add additional information.

• I thought (for a very long time) if I should actually give an honest answer.
• Tomorrow is Christmas (my favorite holiday) !

Parentheses have a variety of other uses, too.

• Pollution increased significantly. (See Chart 14B)
• He was at an Alcoholics Anonymous (AA) meeting.
• Richard I of England (1157–1199) had the heart of a lion.

Square brackets []

Typically, square brackets  are used to clarify or add information to quotations.

• According to an eyewitness, the chimpanzees “climbed on the roof and juggled [bananas] .”
• The judge said that “the defense attorney [Mr. Wright] had made it clear that the case was far from closed.”

Curly brackets {}

Curly brackets , also known as braces , are rarely used punctuation marks that are used to group a set.

• I was impressed by the many different colors {red, green, yellow, blue, purple, black, white} they selected for the flag’s design.

Angle brackets <>

Angle brackets have no usage in formal writing and are rarely ever used even in informal writing. These characters have more uses in other fields, such as math or computing.

Quotation marks and apostrophe

You’ll find these punctuation marks hanging out at the top of a line of text.

Quotation marks (“”)

The most common use of quotation marks is to contain quotations.

• She said, “ Don’t let the dog out of the house. ”
• Bob Ross liked to put “ happy little trees ” in many of his paintings.

Apostrophe (‘)

The apostrophe is most often used to form possessives and contractions.

• The house ’ s back door is open.
• My cousin ’ s birthday is next week.
• It isn ’ t ready yet.
• We should ’ ve stayed outside.

Slash and ellipses

These are two punctuation marks you may not see too often, but they are still useful.

The slash has several different uses. Here are some examples:

• Relationships: The existence of boxer briefs somehow hasn’t ended the boxers/briefs debate.
• Alternatives: They accept cash and/or credit.
• Fractions: After an hour, 2/3 of the audience had already left.

Ellipses (…)

In formal writing, ellipses are used to indicate that words were removed from a quote.

• The mayor said, “The damages will be … paid for by the city … as soon as possible.”

In informal writing, ellipses are often used to indicate pauses or speech that trails off.

• He nervously stammered and said, “Look, I … You see … I wasn’t … Forget it, okay.”

Make Your Writing Shine!

• By clicking "Sign Up", you are accepting Dictionary.com Terms & Conditions and Privacy policies.
• Phone This field is for validation purposes and should be left unchanged.

Typographical symbols

Typographical symbols rarely appear in formal writing. You are much more likely to see them used for a variety of reasons in informal writing.

Asterisk (*)

In formal writing, especially academic and scientific writing, the asterisk is used to indicate a footnote.

• Chocolate is the preferred flavor of ice cream.* * According to survey data from the Ice Cream Data Center.

The asterisk may also be used to direct a reader toward a clarification or may be used to censor inappropriate words or phrases.

Ampersand (&)

The ampersand substitutes for the word and . Besides its use in the official names of things, the ampersand is typically avoided in formal writing.

•  The band gave a speech at the Rock & Roll Hall of Fame .

Bullet Point (•)

Bullet points are used to create lists. For example,

For this recipe you will need:

• baking powder

Pound symbol (#)

Informally, the pound symbol is typically used to mean number or is used in social media hashtags.

• The catchy pop song reached #1 on the charts.
• Ready 4 Halloween 2morrow!!! #spooky #TrickorTreat

Besides being used as an accent mark in Spanish and Portuguese words, the tilde is rarely used. Informally, a person may use it to mean “about” or “approximately.”

• We visited São Paulo during our vacation.
• I think my dog weighs ~20 pounds.

Backslash (\)

The backslash is primarily used in computer programming and coding. It might be used online and in texting to draw emoticons , but it has no other common uses in writing. Be careful not to mix it up with the similar forward slash (/), which is a punctuation mark.

At symbol (@)

The at symbol substitutes for the word at in informal writing. In formal writing, it is used when writing email addresses.

• His email address is [email protected] .

Caret symbol (^)

The caret symbol is used in proofreading, but may be used to indicate an exponent if a writer is unable to use superscript .

• Do you know what 3 ^ 4 (3 to the power of 4) is equal to?

Pipe symbol (|)

The pipe symbol is not used in writing. Instead, it has a variety of functions in the fields of math, physics, or computing.

How much do you know about verbs? Learn about them here.

Ways To Say

Synonym of the day

Teaching and Learning

Professors try ‘restrained ai’ approach to help teach writing, can chatgpt make human writing more efficient, or is writing an inherently time-consuming process best handled without ai tools, by jeffrey r. young     may 23, 2024.

Phonlamai Photo / Shutterstock

When ChatGPT emerged a year and half ago, many professors immediately worried that their students would use it as a substitute for doing their own written assignments — that they’d click a button on a chatbot instead of doing the thinking involved in responding to an essay prompt themselves.

But two English professors at Carnegie Mellon University had a different first reaction: They saw in this new technology a way to show students how to improve their writing skills.

To be clear, these professors — Suguru Ishizaki and David Kaufer — did also worry that generative AI tools could easily be abused by students. And it’s still a concern.

They had an idea, though, for how they could set up a unique set of guardrails that would make a new kind of teaching tool that could help students get more of their ideas into their assignments and spend less time thinking about formatting sentences.

“When everyone else was afraid that AI was going to hijack writing from students,” remembers Kaufer, “We said, ‘Well if we can restrain AI, then AI can reduce many of the remedial tasks of writing that keep students from really [looking] to see what’s going on with their writing.”

The professors call their approach “restrained generative AI,” and they’ve already built a prototype software tool to try it in classrooms — called myScribe — that is being piloted in 10 courses at the university this semester.

Kaufer and Ishizaki were uniquely positioned. They have been building tools together to help teach writing for decades. A previous system they built, DocuScope , uses algorithms to spot patterns in student writing and visually show those patterns to students.

A key feature of their new tool is called “Notes to Prose,” which can take loose bullet points or stray thoughts typed by a student and turn them into sentences or draft paragraphs, thanks to an interface to ChatGPT.

“A bottleneck of writing is sentence generation — getting ideas into sentences,” Ishizaki says. “That is a big task. That part is really costly in terms of cognitive load.”

In other words, especially for beginning writers, it’s difficult to both think of new ideas and keep in mind all the rules of crafting a sentence at the same time, just as it’s difficult for a beginning driver to keep track of both the road surroundings and the mechanics of driving.

“We thought, ‘Can we really lighten that load with generative AI?” he says.

Kaufer adds that novice writers often shift too early in the writing process into making fragments of ideas they put down into carefully crafted sentences, when they might just end up later deleting those sentences because the ideas may not fit into their final argument or essay.

“They start really polishing way too early,” Kaufer says. “And so what we’re trying to do is with AI, now you have a tool to rapidly prototype your language when you are prototyping the quality of your thinking.”

He says the concept is based on writing research from the 1980s that shows that experienced writers spend about 80 percent of their early writing time thinking about whole-text plans and organization and not about sentences.

Taming the Chatbot

Building their “notes to prose” feature took some doing, the professors say.

In their early experiments with ChatGPT, when they put in a few fragments and asked it to make sentences, “what we found is it starts to add a lot of new ideas into the text,” says Ishizaki. In other words, the tool tended to go even further in completing an essay by adding in other information from its vast stores of training data.

“So we just came up with a really lengthy set of prompts to make sure that there are no new ideas or new concepts,” Ishizaki adds.

The technique is different from other attempts to focus the use of AI for education, in that the only source the myScribe bot draws from is the student’s notes rather than a wider dataset.

Stacie Rohrbach, an associate professor and director of graduate studies in the School of Design at Carnegie Mellon, sees potential in tools like those her colleagues created.

“We’ve long encouraged students to always do a robust outline and say, ‘What are you trying to say in each sentence?” she says, and she hopes that “restrained AI” approaches could help that effort.

And she says she already sees student writers misuse ChatGPT and therefore believes some restraint is needed.

“This is the first year that I saw lots of AI-generated text,” she says. “And the ideas get lost. The sentences are framed correctly, but it ends up being gibberish.”

John Warner, an author and education consultant who is writing a book about AI and writing, says he wondered whether the myScribe tool would be able to fully prevent “hallucinations” by the AI chatbot, or instances where tools insert erroneous information.

“The folks that I talk to think that that’s probably not possible,” he says. “Hallucination is a feature of how large language models work. The large language model is absent judgment. You may not be able to get away from it making something up. Because what does it know?”

Kaufer says that their tests so far have been working. In an email follow-up interview he wrote: “It's important to note that ‘notes to prose’ operates within the confines of a paragraph unit. This means that if it were to exceed the boundaries of the notes (or 'hallucinate', as you put it), it would be readily apparent and easy to identify. The worry about AI hallucinating would expand if we were talking about larger discourse units.”

Ishizaki, though, acknowledged that it may not be possible to completely eliminate AI hallucinations in their tool. “But we are hoping that we can restrain or guide AI enough to minimize ‘hallucinations’ or inaccurate or unintended information so that writers can correct them during the review/revision process.”

He described their tool as a “vision” for how they hope the technology will develop, not just a one-off system. “We are setting the goal toward where writing technology should progress,” he says. “In other words, the concept of notes to prose is integral to our vision of the future of writing.”

Even as a vision, though, Warner says he has different dreams for the future of writing.

One tech writer, he says, recently noted that ChatGPT is like having 1,000 interns.

“On one hand, ‘Awesome,’” Warner says. “On the other hand, 1,000 interns are going to make a lot of mistakes. Interns early on cost you more time than they save, but the goal is over time that person makes less and less supervision, they learn.” But with AI, he says, “the oversight doesn’t necessarily improve the underlying product.”

In that way, he argues, AI chatbots end up being “a very powerful tool that requires enormous human oversight.”

And he argues that turning notes into text is in fact the important human process of writing that should be preserved.

“A lot of these tools want to make a process efficient that has no need to be efficient,” he says. “A huge thing happens when I go from my notes to a draft. It’s not just a translation — that these are my ideas and I want them on a page. It’s more like — these are my ideas, and my ideas take shape while I’m writing.”

Kaufer is sympathetic to that argument. “The point is, AI is here to stay and it’s not going to disappear,” he says. “There’s going to be a battle over how it’s going to be used. We’re fighting for responsible uses.”

Jeffrey R. Young ( @jryoung ) is an editor and reporter at EdSurge and host of the EdSurge Podcast . He can be reached at jeff [at] edsurge [dot] com

More from EdSurge

PK-12 School Models

Do alternatives to public school have to be political, by daniel mollenkamp.

An Innovative Journey to Scalable Computer Science Programs

By abbie misha.

EdSurge Podcast

‘college for what' high school students want answers before heading to campus, by jeffrey r. young.

Choosing Wisely: Lessons for Leaders in AI Integration

By wendy mcmahon.

Journalism that ignites your curiosity about education.

EdSurge is an editorially independent project of and

• Product Index
• Write for us
• Advertising

FOLLOW EDSURGE

© 2024 All Rights Reserved

• Share full article

When Prison and Mental Illness Amount to a Death Sentence

The downward spiral of one inmate, Markus Johnson, shows the larger failures of the nation’s prisons to care for the mentally ill.

Supported by

By Glenn Thrush

Photographs by Carlos Javier Ortiz

Glenn Thrush spent more than a year reporting this article, interviewing close to 50 people and reviewing court-obtained body-camera footage and more than 1,500 pages of documents.

• Published May 5, 2024 Updated May 7, 2024

Markus Johnson slumped naked against the wall of his cell, skin flecked with pepper spray, his face a mask of puzzlement, exhaustion and resignation. Four men in black tactical gear pinned him, his face to the concrete, to cuff his hands behind his back.

He did not resist. He couldn’t. He was so gravely dehydrated he would be dead by their next shift change.

Listen to this article with reporter commentary

“I didn’t do anything,” Mr. Johnson moaned as they pressed a shield between his shoulders.

It was 1:19 p.m. on Sept. 6, 2019, in the Danville Correctional Center, a medium-security prison a few hours south of Chicago. Mr. Johnson, 21 and serving a short sentence for gun possession, was in the throes of a mental collapse that had gone largely untreated, but hardly unwatched.

He had entered in good health, with hopes of using the time to gain work skills. But for the previous three weeks, Mr. Johnson, who suffered from bipolar disorder and schizophrenia, had refused to eat or take his medication. Most dangerous of all, he had stealthily stopped drinking water, hastening the physical collapse that often accompanies full-scale mental crises.

Mr. Johnson’s horrific downward spiral, which has not been previously reported, represents the larger failures of the nation’s prisons to care for the mentally ill. Many seriously ill people receive no treatment . For those who do, the outcome is often determined by the vigilance and commitment of individual supervisors and frontline staff, which vary greatly from system to system, prison to prison, and even shift to shift.

The country’s jails and prisons have become its largest provider of inpatient mental health treatment, with 10 times as many seriously mentally ill people now held behind bars as in hospitals. Estimating the population of incarcerated people with major psychological problems is difficult, but the number is likely 200,000 to 300,000, experts say.

Many of these institutions remain ill-equipped to handle such a task, and the burden often falls on prison staff and health care personnel who struggle with the dual roles of jailer and caregiver in a high-stress, dangerous, often dehumanizing environment.

In 2021, Joshua McLemore , a 29-year-old with schizophrenia held for weeks in an isolation cell in Jackson County, Ind., died of organ failure resulting from a “refusal to eat or drink,” according to an autopsy. In April, New York City agreed to pay $28 million to settle a lawsuit filed by the family of Nicholas Feliciano, a young man with a history of mental illness who suffered severe brain damage after attempting to hang himself on Rikers Island — as correctional officers stood by.

Mr. Johnson’s mother has filed a wrongful-death suit against the state and Wexford Health Sources, a for-profit health care contractor in Illinois prisons. The New York Times reviewed more than 1,500 pages of reports, along with depositions taken from those involved. Together, they reveal a cascade of missteps, missed opportunities, potential breaches of protocol and, at times, lapses in common sense.

Prison officials and Wexford staff took few steps to intervene even after it became clear that Mr. Johnson, who had been hospitalized repeatedly for similar episodes and recovered, had refused to take medication. Most notably, they did not transfer him to a state prison facility that provides more intensive mental health treatment than is available at regular prisons, records show.

The quality of medical care was also questionable, said Mr. Johnson’s lawyers, Sarah Grady and Howard Kaplan, a married legal team in Chicago. Mr. Johnson lost 50 to 60 pounds during three weeks in solitary confinement, but officials did not initiate interventions like intravenous feedings or transfer him to a non-prison hospital.

And they did not take the most basic step — dialing 911 — until it was too late.

There have been many attempts to improve the quality of mental health treatment in jails and prisons by putting care on par with punishment — including a major effort in Chicago . But improvements have proved difficult to enact and harder to sustain, hampered by funding and staffing shortages.

Lawyers representing the state corrections department, Wexford and staff members who worked at Danville declined to comment on Mr. Johnson’s death, citing the unresolved litigation. In their interviews with state police investigators, and in depositions, employees defended their professionalism and adherence to procedure, while citing problems with high staff turnover, difficult work conditions, limited resources and shortcomings of co-workers.

But some expressed a sense of resignation about the fate of Mr. Johnson and others like him.

Prisoners have “much better chances in a hospital, but that’s not their situation,” said a senior member of Wexford’s health care team in a deposition.

“I didn’t put them in prison,” he added. “They are in there for a reason.”

Markus Mison Johnson was born on March 1, 1998, to a mother who believed she was not capable of caring for him.

Days after his birth, he was taken in by Lisa Barker Johnson, a foster mother in her 30s who lived in Zion, Ill., a working-class city halfway between Chicago and Milwaukee. Markus eventually became one of four children she adopted from different families.

The Johnson house is a lively split level, with nieces, nephews, grandchildren and neighbors’ children, family keepsakes, video screens and juice boxes. Ms. Johnson sits at its center on a kitchen chair, chin resting on her hand as children wander over to share their thoughts, or to tug on her T-shirt to ask her to be their bathroom buddy.

From the start, her bond with Markus was particularly powerful, in part because the two looked so much alike, with distinctive dimpled smiles. Many neighbors assumed he was her biological son. The middle name she chose for him was intended to convey that message.

“Mison is short for ‘my son,’” she said standing over his modest footstone grave last summer.

He was happy at home. School was different. His grades were good, but he was intensely shy and was diagnosed with attention deficit hyperactivity disorder in elementary school.

That was around the time the bullying began. His sisters were fierce defenders, but they could only do so much. He did the best he could, developing a quick, taunting tongue.

These experiences filled him with a powerful yearning to fit in.

It was not to be.

When he was around 15, he called 911 in a panic, telling the dispatcher he saw two men standing near the small park next to his house threatening to abduct children playing there. The officers who responded found nothing out of the ordinary, and rang the Johnsons’ doorbell.

He later told his mother he had heard a voice telling him to “protect the kids.”

He was hospitalized for the first time at 16, and given medications that stabilized him for stretches of time. But the crises would strike every six months or so, often triggered by his decision to stop taking his medication.

His family became adept at reading signs he was “getting sick.” He would put on his tan Timberlands and a heavy winter coat, no matter the season, and perch on the edge of his bed as if bracing for battle. Sometimes, he would cook his own food, paranoid that someone might poison him.

He graduated six months early, on the dean’s list, but was rudderless, and hanging out with younger boys, often paying their way.

His mother pointed out the perils of buying friendship.

“I don’t care,” he said. “At least I’ll be popular for a minute.”

Zion’s inviting green grid of Bible-named streets belies the reality that it is a rough, unforgiving place to grow up. Family members say Markus wanted desperately to prove he was tough, and emulated his younger, reckless group of friends.

Like many of them, he obtained a pistol. He used it to hold up a convenience store clerk for $425 in January 2017, according to police records. He cut a plea deal for two years of probation, and never explained to his family what had made him do it.

But he kept getting into violent confrontations. In late July 2018, he was arrested in a neighbor’s garage with a handgun he later admitted was his. He was still on probation for the robbery, and his public defender negotiated a plea deal that would send him to state prison until January 2020.

An inpatient mental health system

Around 40 percent of the about 1.8 million people in local, state and federal jails and prison suffer from at least one mental illness, and many of these people have concurrent issues with substance abuse, according to recent Justice Department estimates.

Psychological problems, often exacerbated by drug use, often lead to significant medical problems resulting from a lack of hygiene or access to good health care.

“When you suffer depression in the outside world, it’s hard to concentrate, you have reduced energy, your sleep is disrupted, you have a very gloomy outlook, so you stop taking care of yourself,” said Robert L. Trestman , a Virginia Tech medical school professor who has worked on state prison mental health reforms.

The paradox is that prison is often the only place where sick people have access to even minimal care.

But the harsh work environment, remote location of many prisons, and low pay have led to severe shortages of corrections staff and the unwillingness of doctors, nurses and counselors to work with the incarcerated mentally ill.

In the early 2000s, prisoners’ rights lawyers filed a class-action lawsuit against Illinois claiming “deliberate indifference” to the plight of about 5,000 mentally ill prisoners locked in segregated units and denied treatment and medication.

In 2014, the parties reached a settlement that included minimum staffing mandates, revamped screening protocols, restrictions on the use of solitary confinement and the allocation of about $100 million to double capacity in the system’s specialized mental health units.

Yet within six months of the deal, Pablo Stewart, an independent monitor chosen to oversee its enforcement, declared the system to be in a state of emergency.

Over the years, some significant improvements have been made. But Dr. Stewart’s final report , drafted in 2022, gave the system failing marks for its medication and staffing policies and reliance on solitary confinement “crisis watch” cells.

Ms. Grady, one of Mr. Johnson’s lawyers, cited an additional problem: a lack of coordination between corrections staff and Wexford’s professionals, beyond dutifully filling out dozens of mandated status reports.

“Markus Johnson was basically documented to death,” she said.

‘I’m just trying to keep my head up’

Mr. Johnson was not exactly looking forward to prison. But he saw it as an opportunity to learn a trade so he could start a family when he got out.

On Dec. 18, 2018, he arrived at a processing center in Joliet, where he sat for an intake interview. He was coherent and cooperative, well-groomed and maintained eye contact. He was taking his medication, not suicidal and had a hearty appetite. He was listed as 5 feet 6 inches tall and 256 pounds.

Mr. Johnson described his mood as “go with the flow.”

A few days later, after arriving in Danville, he offered a less settled assessment during a telehealth visit with a Wexford psychiatrist, Dr. Nitin Thapar. Mr. Johnson admitted to being plagued by feelings of worthlessness, hopelessness and “constant uncontrollable worrying” that affected his sleep.

He told Dr. Thapar he had heard voices in the past — but not now — telling him he was a failure, and warning that people were out to get him.

At the time he was incarcerated, the basic options for mentally ill people in Illinois prisons included placement in the general population or transfer to a special residential treatment program at the Dixon Correctional Center, west of Chicago. Mr. Johnson seemed out of immediate danger, so he was assigned to a standard two-man cell in the prison’s general population, with regular mental health counseling and medication.

Things started off well enough. “I’m just trying to keep my head up,” he wrote to his mother. “Every day I learn to be stronger & stronger.”

But his daily phone calls back home hinted at friction with other inmates. And there was not much for him to do after being turned down for a janitorial training program.

Then, in the spring of 2019, his grandmother died, sending him into a deep hole.

Dr. Thapar prescribed a new drug used to treat major depressive disorders. Its most common side effect is weight gain. Mr. Johnson stopped taking it.

On July 4, he told Dr. Thapar matter-of-factly during a telehealth check-in that he was no longer taking any of his medications. “I’ve been feeling normal, I guess,” he said. “I feel like I don’t need the medication anymore.”

Dr. Thapar said he thought that was a mistake, but accepted the decision and removed Mr. Johnson from his regular mental health caseload — instructing him to “reach out” if he needed help, records show.

The pace of calls back home slackened. Mr. Johnson spent more time in bed, and became more surly. At a group-therapy session, he sat stone silent, after showing up late.

By early August, he was telling guards he had stopped eating.

At some point, no one knows when, he had intermittently stopped drinking fluids.

‘I’m having a breakdown’

Then came the crash.

On Aug. 12, Mr. Johnson got into a fight with his older cellmate.

He was taken to a one-man disciplinary cell. A few hours later, Wexford’s on-site mental health counselor, Melanie Easton, was shocked by his disoriented condition. Mr. Johnson stared blankly, then burst into tears when asked if he had “suffered a loss in the previous six months.”

He was so unresponsive to her questions she could not finish the evaluation.

Ms. Easton ordered that he be moved to a 9-foot by 8-foot crisis cell — solitary confinement with enhanced monitoring. At this moment, a supervisor could have ticked the box for “residential treatment” on a form to transfer him to Dixon. That did not happen, according to records and depositions.

Around this time, he asked to be placed back on his medication but nothing seems to have come of it, records show.

By mid-August, he said he was visualizing “people that were not there,” according to case notes. At first, he was acting more aggressively, once flicking water at a guard through a hole in his cell door. But his energy ebbed, and he gradually migrated downward — from standing to bunk to floor.

“I’m having a breakdown,” he confided to a Wexford employee.

At the time, inmates in Illinois were required to declare an official hunger strike before prison officials would initiate protocols, including blood testing or forced feedings. But when a guard asked Mr. Johnson why he would not eat, he said he was “fasting,” as opposed to starving himself, and no action seems to have been taken.

‘Tell me this is OK!’

Lt. Matthew Morrison, one of the few people at Danville to take a personal interest in Mr. Johnson, reported seeing a white rind around his mouth in early September. He told other staff members the cell gave off “a death smell,” according to a deposition.

On Sept. 5, they moved Mr. Johnson to one of six cells adjacent to the prison’s small, bare-bones infirmary. Prison officials finally placed him on the official hunger strike protocol without his consent.

Mr. Morrison, in his deposition, said he was troubled by the inaction of the Wexford staff, and the lack of urgency exhibited by the medical director, Dr. Justin Young.

On Sept. 5, Mr. Morrison approached Dr. Young to express his concerns, and the doctor agreed to order blood and urine tests. But Dr. Young lived in Chicago, and was on site at the prison about four times a week, according to Mr. Kaplan. Friday, Sept. 6, 2019, was not one of those days.

Mr. Morrison arrived at work that morning, expecting to find Mr. Johnson’s testing underway. A Wexford nurse told him Dr. Young believed the tests could wait.

Mr. Morrison, stunned, asked her to call Dr. Young.

“He’s good till Monday,” Dr. Young responded, according to Mr. Morrison.

“Come on, come on, look at this guy! You tell me this is OK!” the officer responded.

Eventually, Justin Duprey, a licensed nurse practitioner and the most senior Wexford employee on duty that day, authorized the test himself.

Mr. Morrison, thinking he had averted a disaster, entered the cell and implored Mr. Johnson into taking the tests. He refused.

So prison officials obtained approval to remove him forcibly from his cell.

‘Oh, my God’

What happened next is documented in video taken from cameras held by officers on the extraction team and obtained by The Times through a court order.

Mr. Johnson is scarcely recognizable as the neatly groomed 21-year-old captured in a cellphone picture a few months earlier. His skin is ashen, eyes fixed on the middle distance. He might be 40. Or 60.

At first, he places his hands forward through the hole in his cell door to be cuffed. This is against procedure, the officers shout. His hands must be in back.

He will not, or cannot, comply. He wanders to the rear of his cell and falls hard. Two blasts of pepper spray barely elicit a reaction. The leader of the tactical team later said he found it unusual and unnerving.

The next video is in the medical unit. A shield is pressed to his chest. He is in agony, begging for them to stop, as two nurses attempt to insert a catheter.

Then they move him, half-conscious and limp, onto a wheelchair for the blood draw.

For the next 20 minutes, the Wexford nurse performing the procedure, Angelica Wachtor, jabs hands and arms to find a vessel that will hold shape. She winces with each puncture, tries to comfort him, and grows increasingly rattled.

“Oh, my God,” she mutters, and asks why help is not on the way.

She did not request assistance or discuss calling 911, records indicate.

“Can you please stop — it’s burning real bad,” Mr. Johnson said.

Soon after, a member of the tactical team reminds Ms. Wachtor to take Mr. Johnson’s vitals before taking him back to his cell. She would later tell Dr. Young she had been unable to able to obtain his blood pressure.

“You good?” one of the team members asks as they are preparing to leave.

“Yeah, I’ll have to be,” she replies in the recording.

Officers lifted him back onto his bunk, leaving him unconscious and naked except for a covering draped over his groin. His expressionless face is visible through the window on the cell door as it closes.

‘Cardiac arrest.’

Mr. Duprey, the nurse practitioner, had been sitting inside his office after corrections staff ordered him to shelter for his own protection, he said. When he emerged, he found Ms. Wachtor sobbing, and after a delay, he was let into the cell. Finding no pulse, Mr. Duprey asked a prison employee to call 911 so Mr. Johnson could be taken to a local emergency room.

The Wexford staff initiated CPR. It did not work.

At 3:38 p.m., the paramedics declared Markus Mison Johnson dead.

Afterward, a senior official at Danville called the Johnson family to say he had died of “cardiac arrest.”

Lisa Johnson pressed for more information, but none was initially forthcoming. She would soon receive a box hastily crammed with his possessions: uneaten snacks, notebooks, an inspirational memoir by a man who had served 20 years at Leavenworth.

Later, Shiping Bao, the coroner who examined his body, determined Mr. Johnson had died of severe dehydration. He told the state police it “was one of the driest bodies he had ever seen.”

For a long time, Ms. Johnson blamed herself. She says that her biggest mistake was assuming that the state, with all its resources, would provide a level of care comparable to what she had been able to provide her son.

She had stopped accepting foster care children while she was raising Markus and his siblings. But as the months dragged on, she decided her once-boisterous house had become oppressively still, and let local agencies know she was available again.

“It is good to have children around,” she said. “It was too quiet around here.”

Read by Glenn Thrush

Audio produced by Jack D’Isidoro .

Glenn Thrush covers the Department of Justice. He joined The Times in 2017 after working for Politico, Newsday, Bloomberg News, The New York Daily News, The Birmingham Post-Herald and City Limits. More about Glenn Thrush

Advertisement