hypothesis reasoning example

How to Write a Hypothesis [31 Tips + Examples]

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

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

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

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

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

Writing a Hypothesis: The Basics

Table of Contents

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

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

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

31 Tips for Writing a Hypothesis

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

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

Tip 1: Start with a Clear Research Question

A clear research question ensures your hypothesis is targeted.

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

Tip 2: Do Background Research

Research helps you understand current knowledge and any existing gaps.

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

Tip 3: Identify Independent and Dependent Variables

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

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

Tip 4: Make Your Hypothesis Testable

A hypothesis must be measurable and falsifiable.

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

Tip 5: Be Specific and Concise

Keep your hypothesis straightforward and to the point.

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

Tip 6: Choose Simple Language

Use simple, understandable language to ensure clarity.

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

Tip 7: Formulate a Null Hypothesis

A null hypothesis assumes no relationship between variables.

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

Tip 8: State Alternative Hypotheses

Provide alternative hypotheses to explore other plausible relationships.

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

Tip 9: Use “If…Then” Statements

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

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

Tip 10: Avoid Assumptions

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

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

Tip 11: Review Existing Literature

Previous research offers insights into forming a hypothesis.

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

Tip 12: Consider Multiple Variables

Hypotheses with multiple variables can offer deeper insights.

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

Tip 13: Review Ethical Considerations

Ethics are essential for trustworthy research.

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

Tip 14: Test with Pilot Studies

Small-scale pilot studies test feasibility and refine hypotheses.

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

Tip 15: Build Hypotheses on Existing Theories

Existing theories provide strong foundations.

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

Tip 16: Address Real-World Problems

Solve real-world problems through practical hypotheses.

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

Tip 17: Aim for Clear, Measurable Outcomes

The results should be easy to measure and interpret.

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

Tip 18: Stay Open to Unexpected Results

Not all hypotheses yield expected results.

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

Tip 19: Keep Hypotheses Relevant

Ensure your hypothesis aligns with the purpose of your research.

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

Tip 20: Collaborate with Peers

Collaboration strengthens hypothesis development.

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

Tip 21: Re-evaluate Hypotheses Periodically

Revising hypotheses ensures relevance.

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

Tip 22: Develop Compelling Visuals

Illustrating hypotheses can help communicate relationships effectively.

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

Tip 23: Refine Your Data Collection Plan

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

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

Tip 24: Focus on Logical Progression

Ensure your hypothesis logically follows your research question.

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

Tip 25: Test Against Diverse Samples

Testing across diverse samples ensures broader applicability.

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

Tip 26: Use Control Groups

Control groups provide a baseline for comparison.

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

Tip 27: Consider Practical Constraints

Work within realistic constraints for your resources and timeline.

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

Tip 28: Recognize Bias Risks

Biases can skew hypothesis formation.

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

Tip 29: Prepare for Peer Review

Peer review ensures your hypothesis holds up to scrutiny.

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

Tip 30: Create a Research Proposal

A proposal outlines your hypothesis, methodology, and significance.

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

Tip 31: Document Your Findings

Recording findings helps validate or challenge your hypothesis.

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

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

Hypothesis Examples for Different Situations

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

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

Professional Contacts

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

Super Personal

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

Final Thoughts: How to Write a Hypothesis

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

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

Stay curious and let your research journey unfold.

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Educational resources and simple solutions for your research journey

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

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

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

Table of Contents

What is a hypothesis ?  

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

What is a research hypothesis ?  

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

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

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

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

How to create an effective research hypothesis  

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

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

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

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

How to write a research hypothesis  

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

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

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

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

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

Research hypothesis checklist  

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

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

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

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

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

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

2. Alternative hypothesis:

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

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

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

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

4. Non-directional hypothesis:

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

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

5. Simple hypothesis :

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

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

6 . Complex hypothesis :

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

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

7. Associative hypothesis:  

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

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

8 . Causal hypothesis:

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

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

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

Research hypothesis examples  

Here are some good research hypothesis examples :  

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

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

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

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

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

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

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

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

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

Importance of testable hypothesis  

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

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

Frequently Asked Questions (FAQs) on research hypothesis  

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

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

2. When to reject null hypothesis ?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

9. Can research hypotheses be used in qualitative research?

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

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

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The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

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What Is a Hypothesis and How Do I Write One?

General Education

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

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

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

So let’s get started!

What Is a Hypothesis?

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

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

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

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

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

Independent and Dependent Variables 

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

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

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

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

Elements of a Good Hypothesis

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

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

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

Writing Your Hypothesis

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

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

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

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

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

The Two Types of Hypotheses

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

#1: If-Then Hypotheses

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

If ____ happens, then ____ will happen.

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

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

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

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

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

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

#2: Null Hypotheses

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

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

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

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

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

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

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

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

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

4 Tips to Write the Best Hypothesis

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

#1: Plausibility

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

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

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

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

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

#2: Defined Concepts

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

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

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

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

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

Good hypotheses ensure that you can observe the results. 

#3: Observability

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

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

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

#4: Generalizability 

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

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

Hypothesis Testing Examples

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

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

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

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

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

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

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

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

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

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

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

Here’s what your hypotheses might look like: 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Key Takeaways: Hypothesis Writing

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

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

What’s Next?

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

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

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

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15 Hypothesis Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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

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

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

Hypothesis Examples

1. “inadequate sleep decreases memory retention”.

Field: Psychology

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

How to Test:

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

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

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

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

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

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

Field: Physics

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

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

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

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

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

Field: Psychology/Linguistics

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

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

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

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

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

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

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

Field: Medicine/Nutrition

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

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

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

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

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

5. “Gravity Influences the Directional Growth of Plants”

Field: Agronomy / Botany

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

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

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

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

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

Field: Education

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

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

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

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

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

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

7. “Mathematics Anxiety Negatively Affects Performance”

Field: Educational Psychology

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

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

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

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

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

Field: Organizational Psychology

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

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

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

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

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

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

Field: Health Psychology

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

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

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

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

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

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

10. “Regular Meditation Enhances Emotional Stability”

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

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

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

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

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

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

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

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

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

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

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

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

Field: Environmental Science

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

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

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

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

13. “Reduced Screen Time Improves Sleep Quality”

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

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

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

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

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

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

Field: Gerontology

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

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

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

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

15. Farming Practices Influence Soil Erosion Rates

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Number Games for Kids (Free and Easy)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Word Games for Kids (Free and Easy)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Outdoor Games for Kids
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 50 Incentives to Give to Students

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scientific hypothesis

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• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - Open Science - On the scope of scientific hypotheses

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

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

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

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

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

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What Is Hypothesis Testing?

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Hypothesis Testing: 4 Steps and Example

Hypothesis testing, sometimes called significance testing, is an act in statistics whereby an analyst tests an assumption regarding a population parameter. The methodology employed by the analyst depends on the nature of the data used and the reason for the analysis.

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data. Such data may come from a larger population or a data-generating process. The word "population" will be used for both of these cases in the following descriptions.

Key Takeaways

• Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data.
• The test provides evidence concerning the plausibility of the hypothesis, given the data.
• Statistical analysts test a hypothesis by measuring and examining a random sample of the population being analyzed.
• The four steps of hypothesis testing include stating the hypotheses, formulating an analysis plan, analyzing the sample data, and analyzing the result.

How Hypothesis Testing Works

In hypothesis testing, an  analyst  tests a statistical sample, intending to provide evidence on the plausibility of the null hypothesis. Statistical analysts measure and examine a random sample of the population being analyzed. All analysts use a random population sample to test two different hypotheses: the null hypothesis and the alternative hypothesis.

The null hypothesis is usually a hypothesis of equality between population parameters; e.g., a null hypothesis may state that the population mean return is equal to zero. The alternative hypothesis is effectively the opposite of a null hypothesis. Thus, they are mutually exclusive , and only one can be true. However, one of the two hypotheses will always be true.

The null hypothesis is a statement about a population parameter, such as the population mean, that is assumed to be true.

• State the hypotheses.
• Formulate an analysis plan, which outlines how the data will be evaluated.
• Carry out the plan and analyze the sample data.
• Analyze the results and either reject the null hypothesis, or state that the null hypothesis is plausible, given the data.

Example of Hypothesis Testing

If an individual wants to test that a penny has exactly a 50% chance of landing on heads, the null hypothesis would be that 50% is correct, and the alternative hypothesis would be that 50% is not correct. Mathematically, the null hypothesis is represented as Ho: P = 0.5. The alternative hypothesis is shown as "Ha" and is identical to the null hypothesis, except with the equal sign struck-through, meaning that it does not equal 50%.

A random sample of 100 coin flips is taken, and the null hypothesis is tested. If it is found that the 100 coin flips were distributed as 40 heads and 60 tails, the analyst would assume that a penny does not have a 50% chance of landing on heads and would reject the null hypothesis and accept the alternative hypothesis.

If there were 48 heads and 52 tails, then it is plausible that the coin could be fair and still produce such a result. In cases such as this where the null hypothesis is "accepted," the analyst states that the difference between the expected results (50 heads and 50 tails) and the observed results (48 heads and 52 tails) is "explainable by chance alone."

When Did Hypothesis Testing Begin?

Some statisticians attribute the first hypothesis tests to satirical writer John Arbuthnot in 1710, who studied male and female births in England after observing that in nearly every year, male births exceeded female births by a slight proportion. Arbuthnot calculated that the probability of this happening by chance was small, and therefore it was due to “divine providence.”

What are the Benefits of Hypothesis Testing?

Hypothesis testing helps assess the accuracy of new ideas or theories by testing them against data. This allows researchers to determine whether the evidence supports their hypothesis, helping to avoid false claims and conclusions. Hypothesis testing also provides a framework for decision-making based on data rather than personal opinions or biases. By relying on statistical analysis, hypothesis testing helps to reduce the effects of chance and confounding variables, providing a robust framework for making informed conclusions.

What are the Limitations of Hypothesis Testing?

Hypothesis testing relies exclusively on data and doesn’t provide a comprehensive understanding of the subject being studied. Additionally, the accuracy of the results depends on the quality of the available data and the statistical methods used. Inaccurate data or inappropriate hypothesis formulation may lead to incorrect conclusions or failed tests. Hypothesis testing can also lead to errors, such as analysts either accepting or rejecting a null hypothesis when they shouldn’t have. These errors may result in false conclusions or missed opportunities to identify significant patterns or relationships in the data.

Hypothesis testing refers to a statistical process that helps researchers determine the reliability of a study. By using a well-formulated hypothesis and set of statistical tests, individuals or businesses can make inferences about the population that they are studying and draw conclusions based on the data presented. All hypothesis testing methods have the same four-step process, which includes stating the hypotheses, formulating an analysis plan, analyzing the sample data, and analyzing the result.

Sage. " Introduction to Hypothesis Testing ," Page 4.

Elder Research. " Who Invented the Null Hypothesis? "

Formplus. " Hypothesis Testing: Definition, Uses, Limitations and Examples ."

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Hypothesis For Kids

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Crafting a hypothesis isn’t just for scientists in white lab coats; even young budding researchers can join in the fun! When kids learn to frame their curious wonders as hypothesis statements, they pave the way for exciting discoveries. Our guide breaks down the world of hypothesis writing into kid-friendly chunks, complete with relatable thesis statement examples and easy-to-follow tips. Dive in to spark a love for inquiry and nurture young scientific minds!

What is an example of a Hypothesis for Kids?

Question: Do plants grow taller when they are watered with coffee instead of water?

Hypothesis: If I water a plant with coffee instead of water, then the plant will not grow as tall because coffee might have substances that aren’t good for plants.

This hypothesis is based on a simple observation or question a child might have, and it predicts a specific outcome (the plant not growing as tall) due to a specific condition (being watered with coffee). It’s presented in simple language suitable for kids.

100 Kids Hypothesis Statement Examples

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Children’s innate curiosity lays the foundation for numerous questions about the world around them. Framing these questions as good hypothesis statements can transform them into exciting learning experiments. Presented below are relatable and straightforward examples crafted especially for young minds, offering them a structured way to articulate their wonders and predictions.

• Sunlight & Plant Growth : If a plant gets more sunlight, then it will grow taller.
• Sugary Drinks & Tooth Decay : Drinking sugary drinks daily will lead to faster tooth decay.
• Chocolates & Energy : Eating chocolate will make me feel more energetic.
• Moon Phases & Sleep : I’ll sleep more during a full moon night.
• Homework & Weekend Moods : If I finish my homework on Friday, I’ll be happier over the weekend.
• Pets & Happiness : Owning a pet will make a child happier.
• Rain & Worms : Worms come out more after it rains.
• Shadows & Time of Day : Shadows are longer in the evening than at noon.
• Snow & School Holidays : More snow means there’s a better chance of school being canceled.
• Ice Cream & Brain Freeze : Eating ice cream too fast will give me a brain freeze.
• Video Games & Dreams : Playing video games before bed might make my dreams more vivid.
• Green Vegetables & Strength : Eating more green vegetables will make me stronger.
• Bicycles & Balance : The more I practice, the better I’ll get at riding my bike without training wheels.
• Stars & Wishes : If I wish on the first star I see at night, my wish might come true.
• Cartoons & Laughing : Watching my favorite cartoon will always make me laugh.
• Soda & Bone Health : Drinking soda every day will make my bones weaker.
• Beach Visits & Sunburn : If I don’t wear sunscreen at the beach, I’ll get sunburned.
• Loud Noises & Pet Behavior : My cat hides when she hears loud noises.
• Bedtime & Morning Energy : Going to bed early will make me feel more energetic in the morning.
• Healthy Snacks & Hunger : Eating a healthy snack will keep me full for longer. …
• Toys & Sharing : The more toys I have, the more I want to share with my friends.
• Homemade Cookies & Taste : Homemade cookies always taste better than store-bought ones.
• Books & Imagination : The more books I read, the more adventures I can imagine.
• Jumping & Height : The more I practice, the higher I can jump.
• Singing & Mood : Singing my favorite song always makes me happy.
• Snowmen & Temperature : If the temperature rises, my snowman will melt faster.
• Costumes & Play : Wearing a costume will make playtime more fun.
• Gardening & Patience : Waiting for my plants to grow teaches me patience.
• Night Lights & Sleep : Having a night light makes it easier for me to sleep.
• Handwriting & Practice : The more I practice, the better my handwriting will become.
• Painting & Creativity : Using more colors in my painting lets me express my creativity better.
• Puzzles & Problem Solving : The more puzzles I solve, the better I become at problem-solving.
• Dancing & Coordination : The more I dance, the more coordinated I will become.
• Stargazing & Constellations : If I stargaze every night, I’ll recognize more constellations.
• Bird Watching & Species Knowledge : The more I watch birds, the more species I can identify.
• Cooking & Skill : If I help in the kitchen often, I’ll become a better cook.
• Swimming & Confidence : The more I swim, the more confident I become in the water.
• Trees & Birds’ Nests : The taller the tree, the more likely it is to have birds’ nests.
• Roller Skating & Balance : If I roller skate every weekend, I’ll improve my balance.
• Drawing & Observation : The more I draw, the better I become at observing details.
• Sandcastles & Water : If I use wet sand, I can build a stronger sandcastle.
• Hiking & Endurance : The more I hike, the farther I can walk without getting tired.
• Camping & Outdoor Skills : If I go camping often, I’ll learn more about surviving outdoors.
• Magic Tricks & Practice : The more I practice a magic trick, the better I’ll get at performing it.
• Stickers & Collection : If I collect stickers, my album will become more colorful.
• Board Games & Strategy : The more board games I play, the better strategist I’ll become.
• Pets & Responsibility : The more I take care of my pet, the more responsible I become.
• Music & Concentration : Listening to calm music while studying will help me concentrate better.
• Photographs & Memories : The more photos I take, the more memories I can preserve.
• Rainbows & Rain : If it rains while the sun is out, I might see a rainbow.
• Museums & Knowledge : Every time I visit a museum, I learn something new.
• Fruits & Health : Eating more fruits will keep me healthier.
• Stories & Vocabulary : The more stories I listen to, the more new words I learn.
• Trees & Fresh Air : The more trees there are in a park, the fresher the air will be.
• Diary & Feelings : Writing in my diary helps me understand my feelings better.
• Planets & Telescopes : If I look through a telescope, I’ll see more planets clearly.
• Crafting & Creativity : The more crafts I make, the more creative I become.
• Snowflakes & Patterns : Every snowflake has a unique pattern.
• Jokes & Laughter : The funnier the joke, the louder I’ll laugh.
• Riddles & Thinking : Solving riddles makes me think harder.
• Nature Walks & Observations : The quieter I am on a nature walk, the more animals I’ll spot.
• Building Blocks & Structures : The more blocks I use, the taller my tower will be.
• Kites & Wind : If there’s more wind, my kite will fly higher.
• Popcorn & Movie Nights : Watching a movie with popcorn makes it more enjoyable.
• Stars & Wishes : If I see a shooting star, I should make a wish.
• Diets & Energy : Eating a balanced diet gives me more energy for playtime.
• Clay & Sculptures : The more I play with clay, the better my sculptures will be.
• Insects & Magnifying Glass : Using a magnifying glass will let me see more details of tiny insects.
• Aquarium Visits & Marine Knowledge : Every time I visit the aquarium, I discover a new marine creature.
• Yoga & Flexibility : If I practice yoga daily, I’ll become more flexible.
• Toothpaste & Bubbles : The more toothpaste I use, the more bubbles I’ll get while brushing.
• Journals & Memories : Writing in my journal every day helps me remember special moments.
• Piggy Banks & Savings : The more coins I save, the heavier my piggy bank will get.
• Baking & Measurements : If I measure ingredients accurately, my cake will turn out better.
• Coloring Books & Art Skills : The more I color, the better I get at staying inside the lines.
• Picnics & Outdoor Fun : Having a picnic makes a sunny day even more enjoyable.
• Recycling & Environment : The more I recycle, the cleaner my environment will be.
• Treasure Hunts & Discoveries : Every treasure hunt has a new discovery waiting.
• Milk & Bone Health : Drinking milk daily will make my bones stronger.
• Puppet Shows & Stories : The more puppet shows I watch, the more stories I learn.
• Field Trips & Learning : Every field trip to a new place teaches me something different.
• Chores & Responsibility : The more chores I do, the more responsible I feel.
• Fishing & Patience : Fishing teaches me to be patient while waiting for a catch.
• Fairy Tales & Imagination : Listening to fairy tales expands my imagination.
• Homemade Pizza & Toppings : The more toppings I add, the tastier my homemade pizza will be.
• Gardens & Butterflies : If I plant more flowers, I’ll see more butterflies in my garden.
• Raincoats & Puddles : Wearing a raincoat lets me jump in puddles without getting wet.
• Gymnastics & Balance : The more I practice gymnastics, the better my balance will be.
• Origami & Craft Skills : The more origami I fold, the better my craft skills become.
• Basketball & Shooting Skills : The more I practice, the better I get at shooting baskets.
• Fireflies & Night Beauty : Catching fireflies makes summer nights magical.
• Books & Knowledge : The more books I read, the smarter I become.
• Pillows & Forts : With more pillows, I can build a bigger fort.
• Lemonade & Summers : Drinking lemonade makes hot summer days refreshing.
• Bicycles & Balance : The more I practice, the better I get at riding my bike without training wheels.
• Pencils & Drawings : If I have colored pencils, my drawings will be more colorful.
• Ice Cream & Happiness : Eating ice cream always makes me happy.
• Beach Visits & Shell Collections : Every time I visit the beach, I find new shells for my collection.
• Jump Ropes & Fitness : The more I jump rope, the fitter I become.
• Tea Parties & Imagination : Hosting tea parties lets my imagination run wild.

Simple Hypothesis Statement Examples for Kids

Simple hypothesis are straightforward predictions that can be tested easily. They help children understand the relationship between two variables. Here are some examples tailored just for kids.

• Plants & Sunlight : Plants placed near the window will grow taller than those in the dark.
• Chocolates & Happiness : Eating chocolates can make kids feel happier.
• Rain & Puddles : The more it rains, the bigger the puddles become.
• Homework & Learning : Doing homework helps kids understand lessons better.
• Toys & Sharing : Sharing toys with friends makes playtime more fun.
• Pets & Care : Taking care of a pet fish helps it live longer.
• Storytime & Sleep : Listening to a bedtime story helps kids sleep faster.
• Brushing & Cavity : Brushing teeth daily prevents cavities.
• Games & Skill : Playing a new game every day improves problem-solving skills.
• Baking & Patience : Waiting for cookies to bake teaches patience.

Hypothesis Statement Examples for Kids Psychology

Child psychology hypothesis delves into how kids think, behave, and process emotions. These hypotheses help understand the psychological aspects of children’s behaviors.

• Emotions & Colors : Kids might feel calm when surrounded by blue and energetic with red.
• Friendship & Self-esteem : Making friends can boost a child’s self-confidence.
• Learning Styles & Memory : Some kids remember better by seeing, while others by doing.
• Play & Development : Pretend play is crucial for cognitive development.
• Rewards & Motivation : Giving small rewards can motivate kids to finish tasks.
• Music & Mood : Listening to soft music can calm a child’s anxiety.
• Sibling Bonds & Sharing : Having siblings can influence a child’s willingness to share.
• Feedback & Performance : Positive feedback can improve a kid’s academic performance.
• Outdoor Play & Attention Span : Playing outside can help kids concentrate better in class.
• Dreams & Reality : Kids sometimes can’t differentiate between dreams and reality.

Hypothesis Examples in Kid Friendly Words

Phrasing hypothesis in simple words makes it relatable and easier for kids to grasp. Here are examples with kid-friendly language.

• Socks & Warmth : Wearing socks will keep my toes toasty.
• Jumping & Energy : The more I jump, the more energy I feel.
• Sandcastles & Water : A little water makes my sandcastle stand tall.
• Stickers & Smiles : Getting a sticker makes my day shine brighter.
• Rainbows & Rain : After the rain, I might see a rainbow.
• Slides & Speed : The taller the slide, the faster I go.
• Hugs & Love : Giving hugs makes me and my friends feel loved.
• Stars & Counting : The darker it is, the more stars I can count.
• Paint & Mess : The more paint I use, the messier it gets.
• Bubbles & Wind : If I blow my bubble wand, the wind will carry them high.

Hypothesis Statement Examples for Kids in Research

Even in a research setting, research hypothesis should be age-appropriate for kids. These examples focus on concepts children might encounter in structured studies.

• Reading & Vocabulary : Kids who read daily might have a richer vocabulary.
• Games & Math Skills : Playing number games can improve math skills.
• Experiments & Curiosity : Conducting science experiments can make kids more curious.
• Doodles & Creativity : Drawing daily might enhance a child’s creativity.
• Learning Methods & Retention : Kids who learn with visuals might remember lessons better.
• Discussions & Understanding : Talking about a topic can deepen understanding.
• Observation & Knowledge : Observing nature can increase a kid’s knowledge about the environment.
• Puzzles & Cognitive Skills : Solving puzzles regularly might enhance logical thinking.
• Music & Rhythmic Abilities : Kids who practice music might develop better rhythm skills.
• Teamwork & Social Skills : Group projects can boost a child’s social skills.

Hypothesis Statement Examples for Kids Science Fair

Science fairs are a chance for kids to delve into the world of experiments and observations. Here are hypotheses suitable for these events.

• Magnet & Metals : Certain metals will be attracted to a magnet.
• Plants & Colored Light : Plants might grow differently under blue and red lights.
• Eggs & Vinegar : An egg in vinegar might become bouncy.
• Solar Panels & Sunlight : Solar panels will generate more power on sunny days.
• Volcanoes & Eruptions : Mixing baking soda and vinegar will make a mini eruption.
• Mirrors & Reflection : Shiny surfaces can reflect light better than dull ones.
• Battery & Energy : Fresh batteries will make a toy run faster.
• Density & Floating : Objects with lower density will float in water.
• Shadows & Light Source : Moving the light source will change the shadow’s direction.
• Freezing & States : Water turns solid when kept in the freezer.

Hypothesis Statement Examples for Science Experiments

Experiments let kids test out their predictions in real-time. Here are hypotheses crafted for various scientific tests.

• Salt & Boiling Point : Adding salt will make water boil at a higher temperature.
• Plants & Music : Playing music might affect a plant’s growth rate.
• Rust & Moisture : Metals kept in a moist environment will rust faster.
• Candles & Oxygen : A candle will burn out faster in an enclosed jar.
• Fruits & Browning : Lemon juice can prevent cut fruits from browning.
• Yeast & Sugar : Adding sugar will make yeast activate more vigorously.
• Density & Layers : Different liquids will form layers based on their density.
• Acids & Bases : Red cabbage juice will change color in acids and bases.
• Soil Types & Water : Sandy soil will drain water faster than clay.
• Thermometers & Temperatures : Thermometers will show higher readings in the sun.

Hypothesis Statement Examples for Kids At Home

These hypotheses are crafted for experiments and observations kids can easily make at home, using everyday items.

• Chores & Time : Setting a timer will make me finish my chores faster.
• Pets & Behavior : My cat sleeps more during the day than at night.
• Recycling & Environment : Recycling more can reduce the trash in my home.
• Cooking & Tastes : Adding spices will change the taste of my food.
• Family Time & Bonding : Playing board games strengthens our family bond.
• Cleaning & Organization : Organizing my toys daily will keep my room tidier.
• Watering & Plant Health : Watering my plant regularly will keep its leaves green.
• Decor & Mood : Changing the room decor can influence my mood.
• Journals & Memories : Writing in my journal daily will help me remember fun events.
• Photos & Growth : Taking monthly photos will show how much I’ve grown.

How do you write a hypothesis for kids? – A Step by Step Guide

Step 1: Start with Curiosity Begin with a question that your child is curious about. This could be something simple, like “Why is the sky blue?” or “Do plants need sunlight to grow?”

Step 2: Observe and Research Before formulating the hypothesis, encourage your child to observe the world around them. If possible, read or watch videos about the topic to gather information. The idea is to get a general understanding of the subject.

Step 3: Keep it Simple For kids, it’s essential to keep the hypothesis straightforward and concise. Use language that is easy to understand and relatable to their age.

Step 4: Make a Predictable Statement Help your child frame their hypothesis as an “If… then…” statement. For example, “If I water a plant every day, then it will grow taller.”

Step 5: Ensure Testability Ensure that the hypothesis can be tested using simple experiments or observations. It should be something they can prove or disprove through hands-on activities.

Step 6: Avoid Certainty Teach kids that a hypothesis is not a definitive statement of fact but rather a best guess based on what they know. It’s okay if the hypothesis turns out to be wrong; the learning process is more important.

Step 7: Review and Refine After forming the initial hypothesis, review it with your child. Discuss if it can be made simpler or clearer. Refinement aids in better understanding and testing.

Step 8: Test the Hypothesis This is the fun part! Plan an experiment or set of observations to test the hypothesis. Whether the hypothesis is proven correct or not, the experience provides a learning opportunity.

Tips for Writing Hypothesis for Kids

• Encourage Curiosity : Always encourage your child to ask questions about the world around them. It’s the first step to formulating a hypothesis.
• Use Familiar Language : Use words that the child understands and can relate to. Avoid jargon or technical terms.
• Make it Fun : Turn the process of forming a hypothesis into a game or a storytelling session. This will keep kids engaged.
• Use Visual Aids : Kids often respond well to visuals. Drawing or using props can help in understanding and formulating the hypothesis.
• Stay Open-minded : It’s essential to teach kids that it’s okay if their hypothesis is wrong. The process of discovery and learning is what’s crucial.
• Practice Regularly : The more often kids practice forming hypotheses, the better they get at it. Use everyday situations as opportunities.
• Link to Real-life Scenarios : Relate the hypothesis to real-life situations or personal experiences. For instance, if discussing plants, you can relate it to a plant you have at home.
• Collaborate : Sometimes, two heads are better than one. Encourage group activities where kids can discuss and come up with hypotheses together.
• Encourage Documentation : Keeping a journal or notebook where they document their hypotheses and results can be a great learning tool.
• Celebrate Efforts : Regardless of whether the hypothesis was correct, celebrate the effort and the learning journey. This reinforces the idea that the process is more important than the outcome.

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Methodology

Inductive Reasoning | Types, Examples, Explanation

Published on January 12, 2022 by Pritha Bhandari . Revised on June 22, 2023.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning , where you go from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Note Inductive reasoning is often confused with deductive reasoning. However, in deductive reasoning, you make inferences by going from general premises to specific conclusions.

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What is inductive reasoning, inductive reasoning in research, types of inductive reasoning, inductive generalization, statistical generalization, causal reasoning, sign reasoning, analogical reasoning, inductive vs. deductive reasoning, other interesting articles, frequently asked questions about inductive reasoning.

Inductive reasoning is a logical approach to making inferences, or conclusions. People often use inductive reasoning informally in everyday situations.

You may have come across inductive logic examples that come in a set of three statements. These start with one specific observation, add a general pattern, and end with a conclusion.

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In inductive research, you start by making observations or gathering data. Then , you take a broad view of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

You distribute a survey to pet owners. You ask about the type of animal they have and any behavioral changes they’ve noticed in their pets since they started working from home. These data make up your observations.

To analyze your data, you create a procedure to categorize the survey responses so you can pick up on repeated themes. You notice a pattern : most pets became more needy and clingy or agitated and aggressive.

Inductive reasoning is commonly linked to qualitative research , but both quantitative and qualitative research use a mix of different types of reasoning.

There are many different types of inductive reasoning that people use formally or informally, so we’ll cover just a few in this article:

Inductive reasoning generalizations can vary from weak to strong, depending on the number and quality of observations and arguments used.

Inductive generalizations use observations about a sample to come to a conclusion about the population it came from.

Inductive generalizations are also called induction by enumeration.

• The flamingos here are all pink.
• All flamingos I’ve ever seen are pink.
• All flamingos must be pink.

Inductive generalizations are evaluated using several criteria:

• Large sample: Your sample should be large for a solid set of observations.
• Random sampling: Probability sampling methods let you generalize your findings.
• Variety: Your observations should be externally valid .
• Counterevidence: Any observations that refute yours falsify your generalization.

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Statistical generalizations use specific numbers to make statements about populations, while non-statistical generalizations aren’t as specific.

These generalizations are a subtype of inductive generalizations, and they’re also called statistical syllogisms.

Here’s an example of a statistical generalization contrasted with a non-statistical generalization.

Causal reasoning means making cause-and-effect links between different things.

A causal reasoning statement often follows a standard setup:

• You start with a premise about a correlation (two events that co-occur).
• You put forward the specific direction of causality or refute any other direction.
• You conclude with a causal statement about the relationship between two things.
• All of my white clothes turn pink when I put a red cloth in the washing machine with them.
• My white clothes don’t turn pink when I wash them on their own.
• Putting colorful clothes with light colors causes the colors to run and stain the light-colored clothes.

Good causal inferences meet a couple of criteria:

• Direction: The direction of causality should be clear and unambiguous based on your observations.
• Strength: There’s ideally a strong relationship between the cause and the effect.

Sign reasoning involves making correlational connections between different things.

Using inductive reasoning, you infer a purely correlational relationship where nothing causes the other thing to occur. Instead, one event may act as a “sign” that another event will occur or is currently occurring.

• Every time Punxsutawney Phil casts a shadow on Groundhog Day, winter lasts six more weeks.
• Punxsutawney Phil doesn’t cause winter to be extended six more weeks.
• His shadow is a sign that we’ll have six more weeks of wintery weather.

It’s best to be careful when making correlational links between variables . Build your argument on strong evidence, and eliminate any confounding variables , or you may be on shaky ground.

Analogical reasoning means drawing conclusions about something based on its similarities to another thing. You first link two things together and then conclude that some attribute of one thing must also hold true for the other thing.

Analogical reasoning can be literal (closely similar) or figurative (abstract), but you’ll have a much stronger case when you use a literal comparison.

Analogical reasoning is also called comparison reasoning.

• Humans and laboratory rats are extremely similar biologically, sharing over 90% of their DNA.
• Lab rats show promising results when treated with a new drug for managing Parkinson’s disease.
• Therefore, humans will also show promising results when treated with the drug.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

In deductive reasoning, you make inferences by going from general premises to specific conclusions. You start with a theory, and you might develop a hypothesis that you test empirically. You collect data from many observations and use a statistical test to come to a conclusion about your hypothesis.

Inductive research is usually exploratory in nature, because your generalizations help you develop theories. In contrast, deductive research is generally confirmatory.

Sometimes, both inductive and deductive approaches are combined within a single research study.

Inductive reasoning approach

You begin by using qualitative methods to explore the research topic, taking an inductive reasoning approach. You collect observations by interviewing workers on the subject and analyze the data to spot any patterns. Then, you develop a theory to test in a follow-up study.

Deductive reasoning approach

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

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MIT researchers advance automated interpretability in AI models

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As artificial intelligence models become increasingly prevalent and are integrated into diverse sectors like health care, finance, education, transportation, and entertainment, understanding how they work under the hood is critical. Interpreting the mechanisms underlying AI models enables us to audit them for safety and biases, with the potential to deepen our understanding of the science behind intelligence itself.

Imagine if we could directly investigate the human brain by manipulating each of its individual neurons to examine their roles in perceiving a particular object. While such an experiment would be prohibitively invasive in the human brain, it is more feasible in another type of neural network: one that is artificial. However, somewhat similar to the human brain, artificial models containing millions of neurons are too large and complex to study by hand, making interpretability at scale a very challenging task. 

To address this, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers decided to take an automated approach to interpreting artificial vision models that evaluate different properties of images. They developed “MAIA” (Multimodal Automated Interpretability Agent), a system that automates a variety of neural network interpretability tasks using a vision-language model backbone equipped with tools for experimenting on other AI systems.

“Our goal is to create an AI researcher that can conduct interpretability experiments autonomously. Existing automated interpretability methods merely label or visualize data in a one-shot process. On the other hand, MAIA can generate hypotheses, design experiments to test them, and refine its understanding through iterative analysis,” says Tamar Rott Shaham, an MIT electrical engineering and computer science (EECS) postdoc at CSAIL and co-author on a new paper about the research . “By combining a pre-trained vision-language model with a library of interpretability tools, our multimodal method can respond to user queries by composing and running targeted experiments on specific models, continuously refining its approach until it can provide a comprehensive answer.”

The automated agent is demonstrated to tackle three key tasks: It labels individual components inside vision models and describes the visual concepts that activate them, it cleans up image classifiers by removing irrelevant features to make them more robust to new situations, and it hunts for hidden biases in AI systems to help uncover potential fairness issues in their outputs. “But a key advantage of a system like MAIA is its flexibility,” says Sarah Schwettmann PhD ’21, a research scientist at CSAIL and co-lead of the research. “We demonstrated MAIA’s usefulness on a few specific tasks, but given that the system is built from a foundation model with broad reasoning capabilities, it can answer many different types of interpretability queries from users, and design experiments on the fly to investigate them.” 

Neuron by neuron

In one example task, a human user asks MAIA to describe the concepts that a particular neuron inside a vision model is responsible for detecting. To investigate this question, MAIA first uses a tool that retrieves “dataset exemplars” from the ImageNet dataset, which maximally activate the neuron. For this example neuron, those images show people in formal attire, and closeups of their chins and necks. MAIA makes various hypotheses for what drives the neuron’s activity: facial expressions, chins, or neckties. MAIA then uses its tools to design experiments to test each hypothesis individually by generating and editing synthetic images — in one experiment, adding a bow tie to an image of a human face increases the neuron’s response. “This approach allows us to determine the specific cause of the neuron’s activity, much like a real scientific experiment,” says Rott Shaham.

MAIA’s explanations of neuron behaviors are evaluated in two key ways. First, synthetic systems with known ground-truth behaviors are used to assess the accuracy of MAIA’s interpretations. Second, for “real” neurons inside trained AI systems with no ground-truth descriptions, the authors design a new automated evaluation protocol that measures how well MAIA’s descriptions predict neuron behavior on unseen data.

The CSAIL-led method outperformed baseline methods describing individual neurons in a variety of vision models such as ResNet, CLIP, and the vision transformer DINO. MAIA also performed well on the new dataset of synthetic neurons with known ground-truth descriptions. For both the real and synthetic systems, the descriptions were often on par with descriptions written by human experts.

How are descriptions of AI system components, like individual neurons, useful? “Understanding and localizing behaviors inside large AI systems is a key part of auditing these systems for safety before they’re deployed — in some of our experiments, we show how MAIA can be used to find neurons with unwanted behaviors and remove these behaviors from a model,” says Schwettmann. “We’re building toward a more resilient AI ecosystem where tools for understanding and monitoring AI systems keep pace with system scaling, enabling us to investigate and hopefully understand unforeseen challenges introduced by new models.” Peeking inside neural networks

The nascent field of interpretability is maturing into a distinct research area alongside the rise of “black box” machine learning models. How can researchers crack open these models and understand how they work? Current methods for peeking inside tend to be limited either in scale or in the precision of the explanations they can produce. Moreover, existing methods tend to fit a particular model and a specific task. This caused the researchers to ask: How can we build a generic system to help users answer interpretability questions about AI models while combining the flexibility of human experimentation with the scalability of automated techniques?

One critical area they wanted this system to address was bias. To determine whether image classifiers displayed bias against particular subcategories of images, the team looked at the final layer of the classification stream (in a system designed to sort or label items, much like a machine that identifies whether a photo is of a dog, cat, or bird) and the probability scores of input images (confidence levels that the machine assigns to its guesses). To understand potential biases in image classification, MAIA was asked to find a subset of images in specific classes (for example “labrador retriever”) that were likely to be incorrectly labeled by the system. In this example, MAIA found that images of black labradors were likely to be misclassified, suggesting a bias in the model toward yellow-furred retrievers.

Since MAIA relies on external tools to design experiments, its performance is limited by the quality of those tools. But, as the quality of tools like image synthesis models improve, so will MAIA. MAIA also shows confirmation bias at times, where it sometimes incorrectly confirms its initial hypothesis. To mitigate this, the researchers built an image-to-text tool, which uses a different instance of the language model to summarize experimental results. Another failure mode is overfitting to a particular experiment, where the model sometimes makes premature conclusions based on minimal evidence.

“I think a natural next step for our lab is to move beyond artificial systems and apply similar experiments to human perception,” says Rott Shaham. “Testing this has traditionally required manually designing and testing stimuli, which is labor-intensive. With our agent, we can scale up this process, designing and testing numerous stimuli simultaneously. This might also allow us to compare human visual perception with artificial systems.”

“Understanding neural networks is difficult for humans because they have hundreds of thousands of neurons, each with complex behavior patterns. MAIA helps to bridge this by developing AI agents that can automatically analyze these neurons and report distilled findings back to humans in a digestible way,” says Jacob Steinhardt, assistant professor at the University of California at Berkeley, who wasn’t involved in the research. “Scaling these methods up could be one of the most important routes to understanding and safely overseeing AI systems.”

Rott Shaham and Schwettmann are joined by five fellow CSAIL affiliates on the paper: undergraduate student Franklin Wang; incoming MIT student Achyuta Rajaram; EECS PhD student Evan Hernandez SM ’22; and EECS professors Jacob Andreas and Antonio Torralba. Their work was supported, in part, by the MIT-IBM Watson AI Lab, Open Philanthropy, Hyundai Motor Co., the Army Research Laboratory, Intel, the National Science Foundation, the Zuckerman STEM Leadership Program, and the Viterbi Fellowship. The researchers’ findings will be presented at the International Conference on Machine Learning this week.

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The belief that putting on a happy face makes one feel happier is an example of the. A) facial feedback hypothesis. B) Duchenne hypothesis.

The belief that putting on a happy face makes one feel happier is an example of the facial feedback hypothesis. The correct answer is option a.

Facial feedback hypothesis is the idea that the facial expressions we make can influence our emotions . This concept suggests that facial expressions and emotions are linked in a cause-and-effect manner. According to this hypothesis, when we experience a stimulus that would typically evoke an emotional response, our facial expressions and our emotions are connected in a feedback loop.

For example, when people smile, it causes them to feel happier because the action of smiling triggers the brain's emotional processing center to release neurotransmitters such as endorphins, which create a positive feeling. Similarly, when people frown, it can make them feel sadder because it triggers negative emotions.

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Related Questions

When attempting to reach a health diagnosis, the health care provider commonly applies four primary steps. Place the steps for reaching a diagnosis in order. Use all the options. 1. Obtain clinical history, 2. Conduct a physical examination, 3. Perform diagnostic testing, 4. Determine the most likely cause of the client's presentation

In order to reach a diagnosis , a healthcare provider should first obtain the patient's clinical history, then conduct a physical examination, followed by any necessary diagnostic testing, and finally determine the most likely cause of the client's presentation.

When a healthcare provider is attempting to reach a diagnosis, they generally follow a four-step process.

Step 1: Obtain clinical history - The healthcare provider will ask the patient about their symptoms, medical history , and any other relevant information that could help in determining the cause of the symptoms.

Step 2: Conduct a physical examination - The healthcare provider will perform a physical examination of the patient to look for any visible signs of illness, such as swelling, rashes, or other physical changes.

Step 3: Perform diagnostic testing - Based on the information gathered from the clinical history and physical examination, the healthcare provider may order diagnostic tests to help confirm or rule out potential causes of the patient's symptoms.

Step 4: Determine the most likely cause of the client's presentation - After analyzing the information gathered from the clinical history, physical examination, and diagnostic testing, the healthcare provider will use their medical expertise to determine the most likely cause of the patient's symptoms and make a diagnosis.

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Fresh Zone is a supermarket based in New York that has branches in two other states, Ohio and Illinois. It uses _____, in which ads are placed in the local media or territorial editions of the national media. A. international advertising B. local advertising C. regional advertising D. national advertising E. global advertising

Fresh Zone is a supermarket based in New York that has branches in two other states, Ohio and Illinois. It uses regional advertising , in which ads are placed in the local media or territorial editions of the national media.

The answer to this question is C. Regional advertising.

Regional advertising is a kind of advertising that is used by firms and businesses to attract customers from a certain geographical area.

Regional advertisements are shown in magazines , radio stations, and newspapers that are read or viewed only in the region. This type of advertising aims to increase brand awareness and familiarity in a specific area, and it is usually cost-effective for small businesses that cannot afford national campaigns.

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This cycle continues, resulting in action potentials traveling down the axon. This propagation of action potentials is all called _____ conduction

This cycle continues, resulting in action potentials traveling down the axon. This propagation of action potentials is all called " saltatory " conduction.

Saltatory conduction is a kind of propagation of action potentials that happens along myelinated axons. Action potentials travel from node to node in myelinated axons in saltatory conduction. The axonal membrane is covered by a myelin sheath that isolates the action potential from the extracellular fluid in this kind of conduction. The saltatory conduction mechanism allows the action potential to travel faster along the axon than in the case of unmyelinated axons.

hence, This cycle continues, resulting in action potentials traveling down the axon . This propagation of action potentials is all called "saltatory" conduction.

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besides china, which country consumes the most rice in the world?

India is the country that consumes the most rice in the world besides China. Rice is a staple food in India, and it is consumed in large quantities across the country.

According to the Food and Agriculture Organization (FAO), India produced over 100 million metric tons of rice in 2020, which is more than any other country. The high consumption of rice in India can be attributed to its affordability , availability, and versatility . Rice is consumed in a variety of dishes, including biryani, pulao, and idli, and is often served with vegetables, lentils , and curries, making it a popular choice for many meals.

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when a person feels pressure from society to achieve or obtain socially acceptable goals, but they have no legitimate means to do so, they may feel disconnected from society and may lean toward criminality. this feeling is known as .

The feeling you are describing, when a person feels pressure from society to achieve or obtain socially acceptable goals without legitimate means and may lean toward criminality, is known as anomie .

Anomie is a social situation in that is characterized by the uprooting or dissolution of any moral principles , norms, or directives for people to follow. Conflicting belief systems are thought to be a possible source of anomie, which weakens social ties between a person and the society. One such instance is the progression of alienation in a person into a dysfunctional inability to integrate within normal social contexts, such as landing a job, succeeding in relationships, etc.

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which cultural setting would foster the least amount of resentment from older siblings when younger siblings receive preferential treatment

A Mexican American family would foster the least amount of resentment from older siblings when younger siblings receive preferential treatment.

Mexican families are typically very traditional, with the father exercising ultimate control over family decisions. The mother is well-liked, but she is frequently regarded as second-in-command to her husband. Mexico, in addition to being a family-oriented society, is also a hierarchical society.

In the United States, eight out of ten children a re growing up with a sibling rather than a father. However, researchers in family studies have paid far less attention to sibling relationships than to other close relationships.

Susan McHale, Ph.D., a Penn State University professor of human development and family studies, stated that it is a mistake to underestimate the contribution that brothers and sisters make to children's development and well-being.

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When people born and raised at sea level travel up to high altitude their ____ increases. As they acclimatize, this decreases as their ___ begins to increase. O respiratory rate; hemoglobin production O hemoglobin production; heart rate O hemoglobin production; melanin production O melanin production; respiratory rate

When people born and raised at sea level travel up to high altitude, their respiratory rate increases. As they acclimatize, this decreases as their hemoglobin production begins to increase. Therefore, the correct answer is Option A: Respiratory rate; hemoglobin production.

High altitude is characterized as elevations greater than 1500 meters above sea level, and at such elevations, the atmospheric pressure and the availability of oxygen are both lower than at sea level, which can affect the body's physiology. Hemoglobin production increases as an acclimation to altitude, allowing for more oxygen to be transported to body tissues, particularly the brain and muscles, which require a lot of oxygen during physical activity.

At high altitude, the respiratory rate increases in order to maintain sufficient oxygenation. The body tries to maintain oxygen delivery to cells and tissues by various mechanisms, including increasing the number of red blood cells , which contain hemoglobin, a protein that carries oxygen, and increasing the heart rate to pump more oxygen-rich blood to the cells. This is how the body adjusts to altitude by acclimating to the new conditions.

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what does an appraiser look for when appraising a house

An appraiser looks for several factors when appraising a house. Some key aspects they consider are: 1. Property size : The appraiser will measure the square footage of the house and the lot size, as larger properties typically have a higher value. 2. Location : The property's location plays a significant role in its value. Factors such as proximity to schools, shopping centers, public transportation, and other amenities can affect the appraisal value. 3. Age and condition : The age and condition of the house are also important factors. A newer home in good condition will generally be worth more than an older home in need of repairs. 4. Comparable properties : Appraisers will look at the sale prices of similar homes in the area (comps) to help determine the market value of the property. 5. Improvements and upgrades : Any upgrades or improvements made to the home, such as kitchen renovations, bathroom remodels, or additions, will also be considered in the appraisal. 6. Market trends : Appraisers consider current market trends and the overall state of the local real estate market when determining the value of a home. 7. Design and functionality : The design, layout, and functionality of the home can also impact its value. A well-designed house with a functional layout is likely to be worth more than a poorly designed one. In summary, when appraising a house, an appraiser looks for factors such as property size, location, age, condition, comparable properties, improvements and upgrades, market trends, and design and functionality. They use this information to determine the home's fair market value.

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do people better remember what they learned if they are in the same physical space where they first learned it? researchers asked people to learn a set of 20 unrelated objects. two days later, these people were asked to recall the objects learned on the first day. some of the people were asked to recall the objects in the same room where they originally learned the objects. the others were asked to recall the objects in a different room. people were assigned at random to one of these two recall conditions. the authors found that the data on the number of objects recalled supported the claim that recall is better when people return to the original learning context. is the inference made one that involves estimation or one that involves hypothesis testing?

Yes, the inference made in this case involves hypothesis testing .

The authors found that the data on the number of objects recalled supported the claim that recall is better when people return to the original learning context. The question is asking whether the inference made involves estimation or hypothesis testing. Inference can be defined as the act of drawing a conclusion based on evidence or reasoning. In this case, the authors drew a conclusion based on the data collected in their study.

They concluded that recall is better when people return to the original learning context. This conclusion is based on the results of their study and the data collected. The authors used statistical analysis to analyze the data and draw their conclusion. This process is known as hypothesis testing.

Hypothesis testing involves the formulation of a hypothesis, the collection of data, and the use of statistical analysis to determine whether the hypothesis is supported by the data. In this case, the authors formulated a hypothesis that recall is better when people return to the original learning context. They then collected data by asking people to recall a set of 20 unrelated objects in either the same room or a different room.

Finally, they used statistical analysis to determine whether the data supported their hypothesis. They found that the data did support their hypothesis, which led them to conclude that recall is better when people return to the original learning context.

In conclusion, the inference made in this case involves hypothesis testing. The authors formulated a hypothesis, collected data, and used statistical analysis to determine whether the hypothesis was supported by the data. They found that the data did support their hypothesis, which led them to conclude that recall is better when people return to the original learning context.

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Some symptoms of dementia are progressive decreases in memory, thinking, and behavior. What are the major causes of dementia in order adults?

Some major causes of dementia in older adults include the following:

Neurodegenerative diseases

Brain injuries and strokes

InfectionsChronic drug or alcohol use

A person with dementia experiences a gradual decrease in their cognitive functions, which include thinking, memory , and behavior. It's a degenerative disorder that can be caused by various factors. It is critical to keep in mind that not all memory loss is caused by dementia, and not all dementias are the same.

Types of dementia include Alzheimer's disease, Lewy body dementia, and vascular dementia. The diagnosis of dementia is made through a careful medical examination that includes medical history, physical examination, and laboratory tests.

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True/False: in buddhist tantric images of sexual union, the male represents the active techniques of compassion and bliss, while the female represents perfect wisdom.

In Buddhist tantric images of sexual union, the male represents the active techniques of compassion and bliss, while the female represents perfect wisdom. The given statement is true.

Tantric Buddhism is a type of Buddhism that combines tantra , yoga, and other esoteric practices. Tantric Buddhism is a subset of Mahayana Buddhism, which is one of the two main branches of Buddhism. It is thought that the idea of using sexual intercourse as a means of spiritual practice in Tantric Buddhism began in India around the fifth or sixth century AD.

Tantric Buddhism has long been associated with sex, but it is not merely about sex.

Tantric Buddhism's fundamental beliefs are that all beings are inherently pure and that enlightenment can be achieved in one's current life. Tantra means liberation from the constraining material body and its forces by methods that directly transform and purify one's inner consciousness. The tantric path is about transforming one's impure perception of the world into pure wisdom.

Tantric Buddhism practices include asana (yoga postures), meditation, mantra chanting, visualization, and ritual. The purpose of these methods is to cultivate a higher level of consciousness and to help practitioners gain the knowledge and insights required to reach enlightenment. In general, Tantric Buddhism teaches that there is no difference between the sacred and the mundane, and that everything is an expression of the divine, including sexuality.

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fill in the blank. use a(n)___when you want to convey your message point by point or use logical steps to build up to a conclusion. group of answer choices convergent presentation model linear presentation model recursive presentation model nonlinear presentation model indirect presentation model

You should use a(n) linear presentation model when you want to convey your message point by point or use logical steps to build up to a conclusion. Therefore, the correct option is 2.

The presentation model that is used to convey your message point by point or use logical steps to build up to a conclusion is a linear presentation model. Linear presentation model is a type of presentation model that is used when you want to convey your message point by point or use logical steps to build up to a conclusion. This model helps to present information in a clear, sequential order , allowing the audience to follow along and understand the message more easily.

The linear presentation model is a type of presentation that is usually done in a specific order, starting from the first point and working through to the final one, which is a bit different from a recursive presentation model, which involves moving back and forth through the information to draw connections and make points.

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What is superego also known as?

The superego is also known as the " conscience " or "moral compass" of an individual's psyche.

According to Sigmund Freud's psychoanalytic theory of personality, the superego is one of the three components of the human psyche, alongside the id and the ego. The superego is responsible for enforcing moral and ethical standards and can generate feelings of guilt or shame when an individual's behavior deviates from these standards. The superego develops during childhood as a result of internalizing societal values, cultural norms , and parental teachings. It is an important aspect of personality development and influences an individual's thoughts, behaviors, and interactions with others.

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chapter 9 according to your text, how do the majority of couples communicate expectations of monogamy?

According to Chapter 9 of the text, the majority of couples communicate expectations of monogamy through open and honest discussions about their relationship boundaries and expectations.

The majority of couples communicate expectations of monogamy through implicit understandings or assumptions. This means that they may assume that their partner shares the same expectations without having explicit conversations about it.

In some cases, couples may have a conversation about monogamy and make their expectations explicit.

However, this is not the norm for the majority of couples. Instead, many couples rely on unspoken assumptions about what it means to be in a monogamous relationship.

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imagine a syncline has been eroded to a flat surface. how would the rock age change as you walked across that flat surface?

As you walk across the flat surface of an eroded syncline , the rock age changes from older to younger and then back to older. 1. A syncline is a fold in a rock layer that has a concave-upward shape, with younger rocks in the center and older rocks on the sides. 2. When the syncline is eroded to a flat surface, the younger rocks in the center will be exposed at the surface. 3. As you start walking across the flat surface, you will initially be walking over the younger rocks in the center of the syncline. 4. As you continue walking, the rock age will gradually become older as you move towards the sides of the syncline. 5. Once you reach the edge of the syncline and start moving across the other side, the rock age would begin to decrease again as you moved back towards the younger rocks in the center. 6. When you reach the center of the syncline on the opposite side, the rock age will once again be the youngest. 7. Continuing further, the rock age would again increase as you walked toward the outer edge of the syncline. So, as you walk across the eroded syncline's flat surface , the rock age would change from younger to older and then back to younger as you move from one side to the other.

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GDP includes the cost of buying pollution-control equipment, but does not address which indicates that GDP has limitations when it comes to measuring the well-being of society in terms of the environment. Select the two correct answers below. Select all that apply: A. the scientific debate on climate change B. whether the air is actually cleaner or dirtier C. whether the water is actually cleaner or dirtier D. the negative externalities of pollution

The correct answers that indicate GDP has limitations when it comes to measuring the well-being of society in terms of the environment are: the negative externalities of pollution and whether the water is actually cleaner or dirtier. The correct option is B and D.

Gross Domestic Product (GDP) measures the economic activity of a country by adding up the value of all goods and services produced. However, it does not address the negative externalities of pollution or other factors that may affect the well-being of society.

For example, GDP includes the cost of buying pollution-control equipment, but it does not address whether the air or water is actually cleaner or dirtier. This indicates that GDP has limitations when it comes to measuring the well-being of society in terms of the environment.

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Which of the following statistics reflect the change in the number of women working outside the home? - About one-third of married women earn more than their husbands. - Women make up roughly forty-seven percent of the labor force. - Nearly sixty percent of marriages have two earners.

The statistic that reflects the change in the number of women working outside the home is: Women make up roughly forty-seven percent of the labor force . This statistic directly relates to the number of women participating in the workforce , indicating an increase in women working outside the home. The other statistics refer to earnings within marriages and do not specifically address the change in women's workforce participation.

The fact that women now comprise approximately 47% of the labor force shows the significant progress made towards gender equality in the workplace and the growing trend of women working outside the home.

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Identify the groups of people who were most commonly Anti-Federalists Small farmers and state politicians

The groups of people who were most commonly Anti-Federalists were small farmers and state politicians.

This group of people who were opposed to the ratification of the Constitution and opposed the new national government were known as Anti-Federalists. They were apprehensive of the government's power and were concerned that it would suppress the states' autonomy and individual freedoms. They also believed that the Constitution gave too much power to the wealthy and well-connected individuals.Anti-Federalists were a diverse group of people that included small farmers, state politicians, and some wealthy merchants.

They were concentrated in areas with less access to transportation and communication , such as the South and the frontier regions. They opposed the Constitution's ratification, primarily because they were concerned that the new federal government would erode their rights and liberties, leading to the eventual establishment of a monarchy or aristocracy .

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Nakyla is a young girl who is often anxious and unhappy. At times she has very low self-esteem and is very fearful of new social situations. Which of the following parenting styles is Nakyla most likely a product of? A. authoritative parenting B. authoritarian parenting C. permissive parenting D. disengaged parenting

The highest levels of moral reasoning, called ______ morality, are based on internal principles that transcend society. postconventional.

The highest levels of moral reasoning , called postconventional morality, are based on internal principles that transcend society.

Moral reasoning is a mental procedure that people use to figure out whether an idea or action is morally right or wrong. It includes ethical decisions that may appear contradictory but are justified based on moral criteria that are socially acceptable.

Postconventional morality is a level of moral development that goes beyond conventional morality, which is defined by society's norms and standards, and focuses on individual freedom and basic human rights .

In comparison to conventional morality, postconventional morality is more difficult to achieve, but it is also more comprehensive and holistic since it takes into account various facets of ethical issues, such as cultural, religious, social, and individual beliefs.

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you want your dog to stop barking unnecessarily so you take him to a dog trainer. the trainer, who is well versed in associative learning theory, tells you that the key to good training is figuring out how to increase the desired behavior. according to the trainer, which of the following is the best approach? a. positive reinforcement when the dog is barking b. negative reinforcement when the dog is barking c. positive punishment when the dog is quiet d. negative reinforcement when the dog is quiet

Option A). A dog trainer who is well versed in associative learning theory tells you that the secret to good training is to figure out how to increase the desired behaviour. " Positive reinforcement is the best approach."

Positive reinforcement is the best approach to take when your dog is barking unnecessarily, according to the trainer. Giving your dog a treat when they behave correctly is an excellent method to teach them how to do the behaviour again. The trainer will use positive reinforcement when the dog is barking.

When you give them the appropriate reward, the dog will associate the barking with the reward, and it will become good behavior. This will eventually lead to the dog barking less often or only when necessary. Negative reinforcement when the dog is barking or when the dog is quiet and positive punishment when the dog is quiet are both counterproductive methods of training the dog.

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Because of the prevalence of social media , it is now appropriate to use it for personal reasons while at work. a. True b. False

The statement ''because of the prevalence of social media , it is now appropriate to use it for personal reasons while at work'' is false as social media has become a ubiquitous part of modern life, it is generally not appropriate to use it for personal reasons while at work.

Most employers have policies in place that prohibit excessive personal use of social media during work hours, as it can be a significant distraction and reduce productivity. Furthermore, using social media at work for personal reasons can reflect poorly on an employee's professionalism and commitment to their job .

In some cases, it may also raise security concerns if personal social media accounts are accessed using company devices or networks. Therefore, it is important for employees to be mindful of their company's policies regarding social media use and limit personal usage to outside of work hours.

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If you are the victim of identity theft or fraud, you should contact the following places except _______. a. Your local elected official like the mayor b. Your financial insitution c. Credit card companies d. Credit bureaus

If you are the victim of identity theft or fraud, you should contact the following places except your local elected official like the mayor. The correct answer is option a .

Identity theft is the act of stealing someone's identity by using their personal information without their knowledge. It's a crime that can harm your reputation and your financial health, leaving you with lasting harm. There are a few things you can do if you've been the victim of identity theft or fraud. Following are the places you should contact if you are the victim of identity theft or fraud:Your financial institution, Credit card companies and Credit bureaus. The only place you should not contact is your local elected official like the mayor.

The Federal Trade Commission (FTC) recommends that you take the following steps if you believe you've been a victim of identity theft: Place a fraud alert with one of the three big credit bureaus. Keep track of your credit reports. Contact the creditors of any accounts that have been tampered with or opened without your permission. Contact the businesses where any new accounts were opened with your name and tell them that they were fraudulent. Notify the FTC of the identity theft that occurred. Report the fraud to your local law enforcement agency.

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what only serves to reject the individual by permanently labeling him/her a criminal and preventing them from becoming fully functioning and productive societal members?

The practice of permanent labeling and stigmatizing individuals as criminals can serve to reject them and prevent them from becoming fully functioning and productive members of society.

This is often seen in the way that criminal records are used to deny individuals access to employment, housing, education, and other opportunities. Once a person has been labeled as a criminal, this label can follow them for life, hindering their ability to reintegrate into society and leading to a cycle of poverty, social exclusion, and further criminal behavior. This labeling and stigmatization can also have a negative impact on individuals' mental health and well-being, exacerbating the harms caused by the criminal justice system and perpetuating systemic inequalities . To promote a more just and equitable society, it is essential to address the root causes of criminal behavior and provide opportunities for rehabilitation, reintegration, and restoration.

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Anthropological research reveals that most ethnic groups and nations are recent historical creations, our connection to people within these groups is relatively new, and our shared traditions are recently invented. In addition, most members will never meet each other. This allows us to observe that most nations today are what?

Anthropological research reveals that most ethnic groups and nations are recent historical creations, with relatively new connections and recently invented shared traditions. In addition, most members will never meet each other. Based on this information, we can observe that most nations today are " imagined communities. "

In his book Imagined Communities from 1983, Benedict Anderson introduced the idea of an imagined community as a way to examine nationalism. According to Anderson, a country is a socially constructed community that its citizens who identify as belonging to a group conceive. Anderson focuses on the ways in which media constructs imagined communities, particularly the influence of print media on a person's social psyche. Anderson examines the written word, which is a tool utilised by authors, churches , and media outlets, including publishers of books, newspapers, and magazines, as well as by institutions of government like the census, the museum, and the map.

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overall scores on tests such as the gesell and the bayley scales in infancy do not correlate highly with iq scores obtained later in childhood. what is the most likely explanation for this?

The most likely explanation for overall scores on tests such as the Gesell and the Bayley scales in infancy not correlating highly with IQ scores obtained later in childhood is that these tests measure different abilities and developmental domains.

What are the Gesell and the Bayley scales? The Gesell and the Bayley scales are two standardized tests used to assess developmental delays and to measure infant and toddler development. The Gesell Developmental Schedule, which was created by Arnold Gesell in 1921, measures an infant's development across several areas, including gross motor skills, fine motor skills, social skills, and language development. The Bayley Scales of Infant Development, which was created by Nancy Bayley in 1969, measure infant and toddler development across several areas, including cognitive development, motor development, and language development.What is an IQ score?An IQ score is a measure of cognitive ability and intelligence that is derived from standardized tests.

An IQ score is a numerical representation of a person's intelligence quotient, which is a measure of a person's intellectual abilities relative to the average abilities of people of the same age. An IQ score of 100 is considered average, while scores below 100 indicate below-average intelligence, and scores above 100 indicate above-average intelligence.

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according to aztec history, how did the aztecs know where to build tenochtitlán? why did they think this was a good location?

According to Aztec history, the Aztecs knew where to build Tenochtitlán because they saw an eagle perched on a cactus while on a pilgrimage. They believed that this was a good location because it fulfilled the prophecy of their god, Huitzilopochtli, who told them to build their city on a site where they saw an eagle perched on a cactus growing out of a rock. They believed this location to be the center of the world, a place where they could fulfill their destiny.

Tenochtitlán was the capital city of the Aztec Empire. The Aztecs saw an eagle perched on a cactus while on a pilgrimage, and this was how they knew where to build Tenochtitlán. They believed that this location was the center of the world and that it fulfilled the prophecy of their god, Huitzilopochtli, who told them to build their city on a site where they saw an eagle perched on a cactus growing out of a rock. They believed this location to be the perfect spot to fulfill their destiny.

The Aztecs' migration took place in the thirteenth century, and they eventually arrived in the Valley of Mexico in the fifteenth century. According to legend, their god Huitzilopochtli told them to build their capital city on the site where they saw an eagle perched on a cactus growing out of a rock. They eventually found this site, which was an island in the middle of Lake Texcoco. They believed this location to be the center of the world, and they built their city there.

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Anger and grief are emotional by-products in the lives of children who have been sexually abused. Venting of these feelings should be encouraged, particularly when a. adults are uncomfortable with such venting and attempt to repress such feelings when children exhibit them. b. such venting provides legal evidence and data for court action against perpetrators. c. play therapy, relaxation techniques, and stress reduction measures fail to produce positive results. d. children's developmental immaturity impedes the therapeutic process.

Anger and grief are emotional by-products in the lives of children who have been sexually abused . Venting of these feelings should be encouraged, particularly when adults are uncomfortable with such venting and attempt to repress such feelings when children exhibit them.  Thus, option a. is correct.

It is suggested that sexual abuse produces a number of different emotional reactions in children, including anger , grief, shame, and guilt. It is critical to acknowledge that the child was wronged, and that the grief and outrage that they are feeling are natural responses to this wrong. It's also important to realize that these feelings can be frightening to both the child and the people around them.

When a child who has been sexually abused reacts in a way that others consider "abnormal," it can be confusing and frightening. Some adults may want to stop the child from expressing their emotions or speaking about the abuse, but this should be avoided because it might cause more damage to the child. Therefore, option a. is the correct answer.

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Strict scrutiny is the level of judicial review that federal judges give to all cases that involve ______ classifications.

Strict scrutiny is the level of judicial review that federal judges give to all cases that involve suspect classifications.

Strict scrutiny is the most stringent standard of judicial review applied by the courts to the legislature's actions. Strict scrutiny is the standard that is applied when the court assesses whether a law regulating a fundamental right or involving a suspect classification is constitutional or not. The government must show that it has a compelling interest, and the rule must be narrowly tailored to achieve that objective.

Strict scrutiny is the standard that courts use to decide whether legislation restricting certain rights or violating certain liberties is constitutional or not. Under the equal protection clause of the Fourteenth Amendment, the courts have established that the government must demonstrate a compelling state interest, and the legislation must be narrowly tailored to achieve that objective when the state discriminates against a suspect class of people.

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Electroconvulsive therapy is most useful in the treatment of:________

Electroconvulsive therapy (ECT) is most useful in the treatment of severe depression that has not responded to other treatments or when rapid relief is required. Severe depression is a common indication for ECT, although it may also be used for mania and catatonia, as well as other disorders such as schizophrenia.

Electroconvulsive therapy (ECT) is a medical treatment that involves sending electric currents through the brain to trigger a brief seizure . ECT appears to cause changes in brain chemistry that can quickly reverse symptoms of particular mental illnesses. Severe depression is a medical condition that is diagnosed by a healthcare professional. Symptoms of severe depression can include sadness, hopelessness, and a lack of interest in activities that were once enjoyable, as well as other physical and psychological symptoms.

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Learning Classifier Systems as a Solver for the Abstraction and Reasoning Corpus

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