hypothesis in research in tamil

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Translation of hypothesis – English–Tamil dictionary

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(Translation of hypothesis from the Cambridge English–Tamil Dictionary © Cambridge University Press)

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relating to the scientific study of animals, especially their structure

Dead ringers and peas in pods (Talking about similarities, Part 2)

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hypothesis meaning in Tamil | hypothesis தமிழ் பொருள்

hypothesis  கருதுகோள் அனுமானம்

கருதுகோள் அனுமானம்.

hypothesis =  கருதுகோள் அனுமானம்

Pronunciation  =  🔊 bb1.onclick = function(){ if(responsivevoice.isplaying()){ responsivevoice.cancel(); }else{ responsivevoice.speak("hypothesis", "uk english female"); } }; hypothesis, pronunciation in tamil  =  ஹைப்பாதெசிஸ், hypothesis  in tamil : கருதுகோள் அனுமானம், part of speech :  noun  , definition in english : a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation. , definition in  tamil : ஒரு விசாரணையின் தொடக்கத்தில், குறைந்த ஆதாரங்களின் அடிப்படையில் தோன்றும் கற்பனை அல்லது முன்மொழியப்பட்ட விளக்கம், examples in english :.

• I dont believe this hypothesis.

Examples in Tamil :

• இந்த அனுமானத்தை நான் நம்பமாட்டேன்

Synonyms of hypothesis

Antonyms of hypothesis, about english tamil dictionary.

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• திருக்குறள்
• தமிழ்ப்பெயர்கள்
• தமிழாய்ப்பேசு

தமிழ் அகராதி விளக்கம்

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வாராந்திர செய்தி மடல் பெற எங்களோடு இணைந்திருங்கள்

Translation of "hypothesis" into Tamil

ஊகக்கோட்பாடு,கற்பிதக் கோட்பாடு கருதுகோள், எடுகோள், எடுகோள்கருதுகோள் are the top translations of "hypothesis" into Tamil. Sample translated sentence: Tunnel Mystery Hypothesis ↔ சுரங்க இரகசியத்தைப் பற்றிய கோட்பாடு

(sciences) A tentative conjecture explaining an observation, phenomenon or scientific problem that can be tested by further observation, investigation and/or experimentation. [..]

English-Tamil dictionary

ஊகக்கோட்பாடு,கற்பிதக் கோட்பாடு கருதுகோள், எடுகோள்கருதுகோள்.

Less frequent translations

Show algorithmically generated translations

Automatic translations of " hypothesis " into Tamil

Translations with alternative spelling

Phrases similar to "hypothesis" with translations into Tamil

• Avogadro's hypothesis ஆவோ கடரோ கருதுகோள்
• accretion hypothesis அகந்திரள் கருதுகோள் · திரள்மைக் கருதுகோள்
• wooble hypothesis
• hypothesis, alternative மாற்று எடுகோள்
• experimental hypothesis செய்முறைக் கருதுகோள்
• Alternative hypothesis மாற்று ஊகம்
• continuum hypothesis தொடரக எடுகோள்
• valid hypothesis

Translations of "hypothesis" into Tamil in sentences, translation memory

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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 Research Hypothesis: How to Write it, Types, and Examples

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

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

Table of Contents

What is a hypothesis ?  

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

What is a research hypothesis ?  

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

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

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

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

How to create an effective research hypothesis  

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

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

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

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

How to write a research hypothesis  

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

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

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

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

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

Research hypothesis checklist  

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

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

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

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

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

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

2. Alternative hypothesis:

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

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

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

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

4. Non-directional hypothesis:

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

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

5. Simple hypothesis :

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

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

6 . Complex hypothesis :

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

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

7. Associative hypothesis:  

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

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

8 . Causal hypothesis:

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

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

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

Research hypothesis examples  

Here are some good research hypothesis examples :  

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

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

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

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

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

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

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

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

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

Importance of testable hypothesis  

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

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

Frequently Asked Questions (FAQs) on research hypothesis  

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

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

2. When to reject null hypothesis ?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

9. Can research hypotheses be used in qualitative research?

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

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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English Tamil Dictionary | இங்கிலீஷ் தமிழ் நிகண்டு

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hypothesis - Meaning in Tamil

•   எடுகோள் சோதனை

hypothesis Word Forms & Inflections

Definitions and meaning of hypothesis in english, hypothesis noun.

• possibility , theory
• "a scientific hypothesis that survives experimental testing becomes a scientific theory"
• "he proposed a fresh theory of alkalis that later was accepted in chemical practices"
• conjecture , guess , speculation , supposition , surmisal , surmise

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Synonyms of hypothesis

A hypothesis is a proposed explanation for a phenomenon. For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with the available scientific theories. Even though the words "hypothesis" and "theory" are often used interchangeably, a scientific hypothesis is not the same as a scientific theory. A working hypothesis is a provisionally accepted hypothesis proposed for further research in a process beginning with an educated guess or thought.

கருதுகோள் ( hypothesis ) என்பது ஒரு பிரச்சினைக்குத் தீர்வாக முன் வைக்கப்படும் தற்காலிகமான ஓர் ஊகம் ஆகும். இது, ஒரு தோற்றப்பாட்டை விளக்குவதற்காக முன்வைத்த ஒரு கருத்தாகவோ அல்லது பல தோற்றப்பாடுகளுக்கு இடையே இருக்கக்கூடிய தொடர்புகள் குறித்த தர்க்க முறையான ஒரு கருத்தாகவோ இருக்கலாம். அறிவியல் வழிமுறைகளின்படி ஒரு கருதுகோளானது சோதனை செய்து பார்க்கக் கூடியதாக இருத்தல் வேண்டும். அறிவியலாளர்கள், இத்தகைய கருதுகோள்களை, முன்னைய கவனிப்புகளிலிருந்தோ இருந்தோ, அறிவியற் கோட்பாடுகளை விரிவுபடுத்துவதன் மூலமோ ஊகித்து முன்வைக்கிறார்கள்.

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• கணக்கை ஆக்கு
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ஆங் .| பெ .| n .

• கருதுகோள்; எடுகோள்
• புனைவுகோள்; புனை கொள்கை; மேலாய்வுப் புனைக்கருத்து
• கற்பிதம், கற்பிதக்கொள்கை

உசாத்துணை [ தொகு ]

• தமிழ் இணையப் பல்கலைக்கழக அகரமுதலியில் hypothesis

• ஆங்கிலம்-பெயர்ச்சொற்கள்
• ஆங்கில இலக்கணம்
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• ஆங்கிலம்-விலங்கியல்
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• ஆங்கிலம்-வேதிப்பொறியியல்
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• வரையறுக்கப்பட்ட உள்ளடக்க அகலத்தை மாற்று

• Scientific Methods

What is Hypothesis?

We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.

A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

• The hypothesis should be clear and precise to consider it to be reliable.
• If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables.
• The hypothesis must be specific and should have scope for conducting more tests.
• The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance.

Sources of Hypothesis

Following are the sources of hypothesis:

• The resemblance between the phenomenon.
• Observations from past studies, present-day experiences and from the competitors.
• Scientific theories.
• General patterns that influence the thinking process of people.

Types of Hypothesis

There are six forms of hypothesis and they are:

• Simple hypothesis
• Complex hypothesis
• Directional hypothesis
• Non-directional hypothesis
• Null hypothesis
• Associative and casual hypothesis

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Null Hypothesis

It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.

Examples of Hypothesis

Following are the examples of hypotheses based on their types:

• Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis.
• All lilies have the same number of petals is an example of a null hypothesis.
• If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis.

Functions of Hypothesis

Following are the functions performed by the hypothesis:

• Hypothesis helps in making an observation and experiments possible.
• It becomes the start point for the investigation.
• Hypothesis helps in verifying the observations.
• It helps in directing the inquiries in the right direction.

How will Hypothesis help in the Scientific Method?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Formation of question
• Doing background research
• Creation of hypothesis
• Designing an experiment
• Collection of data
• Result analysis
• Summarizing the experiment
• Communicating the results

Frequently Asked Questions – FAQs

What is hypothesis.

A hypothesis is an assumption made based on some evidence.

Give an example of simple hypothesis?

What are the types of hypothesis.

Types of hypothesis are:

• Associative and Casual hypothesis

State true or false: Hypothesis is the initial point of any investigation that translates the research questions into a prediction.

Define complex hypothesis..

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

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The lessons learned from the fine-tuning and evaluation of Vietnamese LLMs could help broaden access to models beyond English speakers.

Large language models are well versed in Standard American English and a few other dominant world languages where training data is plentiful, but how do they perform with languages less well represented online?

Not very well, it turns out. 

Take Tamil, for example, a language spoken by over 78 million people and the official language of Sri Lanka and the Indian state of Tamil Nadu.  When asked to write a poem in the traditional Tamil style of metered poetry called  Venpa , ChatGPT’s English version was a far better representation of the structure and phrasing typical of Venpa than its Tamil counterpart, despite being a style of poetry originating in Tamil. 

How do we create LLMs that serve underrepresented languages and dialects, and how do we evaluate the performance of these LLMs?

This is precisely what  Sanmi Koyejo , assistant professor of computer science at Stanford University and an affiliate of the Stanford Institute for Human-Centered AI , and  Sang Truong , a PhD student in computer science at Stanford, set out to do when fine-tuning an LLM for Vietnamese. 

“The assumption is that English is the de facto standard for everything because it is particularly used in academic settings, and many of the builders are targeting U.S. and European usage, and the effect has been that the data skews toward particular types of English. The models are much less performant beyond that,” says Koyejo.

Read the full study:  Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models

In addition to creating an open-source Vietnamese LLM, the duo collaborated with scholars from Ho Chi Minh City University of Technology and Ontocord.ai to develop a comprehensive evaluation framework encompassing 10 common tasks and 31 metrics. Here Koyejo and Truong discuss the key findings of their work, published this spring on preprint service arXiv.

What does the landscape of LLMs look like for non-English languages and Vietnamese, in particular?

Truong:  The landscape of Vietnamese LLMs is still in an early stage; most of them are commercial models. We are actually the first to train high-quality open-source Vietnamese models and have an assessment of their performance. Prior to our work, there wasn’t a very rigorous evaluation of Vietnamese models. The standard evaluation was based on question answering, and mostly multiple-choice question answering, and we recognized that this wasn’t reflective of real-world use cases in modern-day life. As a consequence, the public doesn’t have much trust in the models and doesn’t know how to use them. There aren’t a lot of people using them in Vietnam. 

Koyejo: There’s a real effect of losing trust. This has implications for accessibility and democratization of technology worldwide because people’s experiences end up being so bad that they don’t think this technology is for them. 

Are these models accessible to non-English speakers?

Truong:  Models like GPT-4 and Gemini can’t be accessed in Vietnam easily. For example, you need to have a U.S. phone number to register for one of these models. You have to pay $20, which isn’t a lot here, but covers food for a week or two in Vietnam. It’s a significant barrier to usage.

Koyejo : Part of our goal was also collaboration with people in these countries to increase access and engagement. We know what that technology could look like in a local context. We have seen some early effects now that some of our tools are available to folks engaging and building models, including thousands of downloads on Hugging Face and enthusiasm for further development from academic institutions as well as industry. 

What are the risks if we don’t have quality LLMs in languages other than English? 

Truong:  One of the risks I’m most worried about is that LLMs we see are boosting the productivity of everyone in English-speaking countries. But for countries that don’t have LLMs, they will experience lags in productivity and slowness in participating in these technological revolutions, and it can set back the economic progress of an entire country. We saw this before when certain countries didn’t have access to diesel engines, and their industries lagged behind.

Koyejo : Our broader goal is the democratization of technology. The goal is to find these anchors that capture what is thought to be hard about modeling language, and hopefully find examples of where we can make it better. Vietnamese is an anchor because of its style and linguistic characteristics, which make it different from, say, a Latin-based language. Our work allows for this coverage, such that long term we’re just better at solving this kind of problem and not leaving many languages behind.

What challenges arise when trying to fine-tune an LLM to be useful for other languages?

Truong: Every language has their own unique structure, which means you have to curate a diverse and high-quality dataset to capture the intricacies of the language. We learned that the dataset needs to be very clean, free of toxicity and grammatical errors and inconsistencies. The other challenge is selecting the appropriate base model – LLaMA, in this case – and fine-tuning technique. There are many base models out there that you can fine-tune off of. As far as datasets, one of the interesting features of Vietnamese Wikipedia is that it is a paired dataset, so the model sees the data in English and also English-Vietnamese, and therefore knows some translation. Those sources may end up fine-tuning faster. This is a hypothesis and intuition that we have coming out of this research: that paired datasets are beneficial when fine-tuning non-English models.

What were the main findings of your research?

Koyejo:  In the process of training LLMs, one of the first choices to make is: How do I choose to represent language in a format that the computer can understand? This is called the  tokenizer . This is a pre-processing step that is underappreciated in English because there are out-of-the-box tools that take care of it for you, but beyond English, this pre-processing tokenization step is important. We found that doing this first step well played a crucial role in the overall performance. This seems to be particularly true beyond English. 

Truong:  Another significant finding is that bigger models do not always guarantee better performance. Instead, the performance of an LLM is heavily dependent on the quality and relevance of the data it has been trained on. Bigger models might exhibit more biases than smaller ones. Our research also suggests that building a foundational LLM may not require an extensive amount of data, provided that proper fine-tuning techniques are used. This is due to the model’s ability to transfer knowledge across languages, leveraging the pre-existing linguistic patterns and structures learned from other languages.

What are the implications of this study on LLMs for other languages, particularly those in the Global South?

Truong: By providing a recipe for fine-tuning a wide range of language models for foreign languages, this research opens up new possibilities for developing robust and effective LLMs in underrepresented languages. One of the key contributions of this study is the development of a comprehensive evaluation framework for assessing the performance of these models. The evaluation methodology presented in this study can serve as a valuable template for researchers working on LLMs in other languages.

Koyejo : This research expands the space of people who feel like they can engage and meaningfully think about the ways this kind of technology can benefit them locally. The community building in engaging with people who are primary speakers of the language of this technology was important as well. And that mode of building tools that have cultural sensitivities built in and can work well in local contexts is salient. The better we can do this, the better linguistic diversity and inclusion in the field.

Stanford HAI’s mission is to advance AI research, education, policy and practice to improve the human condition.  Learn more . 

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Guest Essay

The Troubling Trend in Teenage Sex

By Peggy Orenstein

Ms. Orenstein is the author of “Boys & Sex: Young Men on Hookups, Love, Porn, Consent and Navigating the New Masculinity” and “Girls & Sex: Navigating the Complicated New Landscape.”

Debby Herbenick is one of the foremost researchers on American sexual behavior. The director of the Center for Sexual Health Promotion at Indiana University and the author of the pointedly titled book “Yes, Your Kid,” she usually shares her data, no matter how explicit, without judgment. So I was surprised by how concerned she seemed when we checked in on Zoom recently: “I haven’t often felt so strongly about getting research out there,” she told me. “But this is lifesaving.”

For the past four years, Dr. Herbenick has been tracking the rapid rise of “rough sex” among college students, particularly sexual strangulation, or what is colloquially referred to as choking. Nearly two-thirds of women in her most recent campus-representative survey of 5,000 students at an anonymized “major Midwestern university” said a partner had choked them during sex (one-third in their most recent encounter). The rate of those women who said they were between the ages 12 and 17 the first time that happened had shot up to 40 percent from one in four.

As someone who’s been writing for well over a decade about young people’s attitudes and early experience with sex in all its forms, I’d also begun clocking this phenomenon. I was initially startled in early 2020 when, during a post-talk Q. and A. at an independent high school, a 16-year-old girl asked, “How come boys all want to choke you?” In a different class, a 15-year-old boy wanted to know, “Why do girls all want to be choked?” They do? Not long after, a college sophomore (and longtime interview subject) contacted me after her roommate came home in tears because a hookup partner, without warning, had put both hands on her throat and squeezed.

I started to ask more, and the stories piled up. Another sophomore confided that she enjoyed being choked by her boyfriend, though it was important for a partner to be “properly educated” — pressing on the sides of the neck, for example, rather than the trachea. (Note: There is no safe way to strangle someone.) A male freshman said “girls expected” to be choked and, even though he didn’t want to do it, refusing would make him seem like a “simp.” And a senior in high school was angry that her friends called her “vanilla” when she complained that her boyfriend had choked her.

Sexual strangulation, nearly always of women in heterosexual pornography, has long been a staple on free sites, those default sources of sex ed for teens . As with anything else, repeat exposure can render the once appalling appealing. It’s not uncommon for behaviors to be normalized in porn, move within a few years to mainstream media, then, in what may become a feedback loop, be adopted in the bedroom or the dorm room.

Choking, Dr. Herbenick said, seems to have made that first leap in a 2008 episode of Showtime’s “Californication,” where it was still depicted as outré, then accelerated after the success of “Fifty Shades of Grey.” By 2019, when a high school girl was choked in the pilot of HBO’s “Euphoria,” it was standard fare. A young woman was choked in the opener of “The Idol” (again on HBO and also, like “Euphoria,” created by Sam Levinson; what’s with him ?). Ali Wong plays the proclivity for laughs in a Netflix special, and it’s a punchline in Tina Fey’s new “Mean Girls.” The chorus of Jack Harlow’s “Lovin On Me,” which topped Billboard’s Hot 100 chart for six nonconsecutive weeks this winter and has been viewed over 99 million times on YouTube, starts with, “I’m vanilla, baby, I’ll choke you, but I ain’t no killer, baby.” How-to articles abound on the internet, and social media algorithms feed young people (but typically not their unsuspecting parents) hundreds of #chokemedaddy memes along with memes that mock — even celebrate — the potential for hurting or killing female partners.

I’m not here to kink-shame (or anything-shame). And, anyway, many experienced BDSM practitioners discourage choking, believing it to be too dangerous. There are still relatively few studies on the subject, and most have been done by Dr. Herbenick and her colleagues. Reports among adolescents are now trickling out from the United Kingdom , Australia , Iceland , New Zealand and Italy .

Twenty years ago, sexual asphyxiation appears to have been unusual among any demographic, let alone young people who were new to sex and iffy at communication. That’s changed radically in a short time, with health consequences that parents, educators, medical professionals, sexual consent advocates and teens themselves urgently need to understand.

Sexual trends can spread quickly on campus and, to an extent, in every direction. But, at least among straight kids, I’ve sometimes noticed a pattern: Those that involve basic physical gratification — like receiving oral sex in hookups — tend to favor men. Those that might entail pain or submission, like choking, are generally more for women.

So, while undergrads of all genders and sexualities in Dr. Herbenick’s surveys report both choking and being choked, straight and bisexual young women are far more likely to have been the subjects of the behavior; the gap widens with greater occurrences. (In a separate study , Dr. Herbenick and her colleagues found the behavior repeated across the United States, particularly for adults under 40, and not just among college students.) Alcohol may well be involved, and while the act is often engaged in with a steady partner, a quarter of young women said partners they’d had sex with on the day they’d met also choked them.

Either way, most say that their partners never or only sometimes asked before grabbing their necks. For many, there had been moments when they couldn’t breathe or speak, compromising the ability to withdraw consent, if they’d given it. No wonder that, in a separate study by Dr. Herbenick, choking was among the most frequently listed sex acts young women said had scared them, reporting that it sometimes made them worry whether they’d survive.

Among girls and women I’ve spoken with, many did not want or like to be sexually strangled, though in an otherwise desired encounter they didn’t name it as assault . Still, a sizable number were enthusiastic; they requested it. It is exciting to feel so vulnerable, a college junior explained. The power dynamic turns her on; oxygen deprivation to the brain can trigger euphoria.

That same young woman, incidentally, had never climaxed with a partner: While the prevalence of choking has skyrocketed, rates of orgasm among young women have not increased, nor has the “orgasm gap” disappeared among heterosexual couples. “It indicates they’re not doing other things to enhance female arousal or pleasure,” Dr. Herbenick said.

When, for instance, she asked one male student who said he choked his partner whether he’d ever tried using a vibrator instead, he recoiled. “Why would I do that?” he asked.

Perhaps, she responded, because it would be more likely to produce orgasm without risking, you know, death.

In my interviews, college students have seen male orgasm as a given; women’s is nice if it happens, but certainly not expected or necessarily prioritized (by either partner). It makes sense, then, that fulfillment would be less the motivator for choking than appearing adventurous or kinky. Such performances don’t always feel good.

“Personally, my hypothesis is that this is one of the reasons young people are delaying or having less sex,” Dr. Herbenick said. “Because it’s uncomfortable and weird and scary. At times some of them literally think someone is assaulting them but they don’t know. Those are the only sexual experiences for some people. And it’s not just once they’ve gotten naked. They’ll say things like, ‘I’ve only tried to make out with someone once because he started choking and hitting me.’”

Keisuke Kawata, a neuroscientist at Indiana University’s School of Public Health, was one of the first researchers to sound the alarm on how the cumulative, seemingly inconsequential, sub-concussive hits football players sustain (as opposed to the occasional hard blow) were key to triggering C.T.E., the degenerative brain disease. He’s a good judge of serious threats to the brain. In response to Dr. Herbenick’s work, he’s turning his attention to sexual strangulation. “I see a similarity” to C.T.E., he told me, “though the mechanism of injury is very different.” In this case, it is oxygen-blocking pressure to the throat, frequently in light, repeated bursts of a few seconds each.

Strangulation — sexual or otherwise — often leaves few visible marks and can be easily overlooked as a cause of death. Those whose experiences are nonlethal rarely seek medical attention, because any injuries seem minor: Young women Dr. Herbenick studied mostly reported lightheadedness, headaches, neck pain, temporary loss of coordination and ear ringing. The symptoms resolve, and all seems well. But, as with those N.F.L. players, the true effects are silent, potentially not showing up for days, weeks, even years.

According to the American Academy of Neurology, restricting blood flow to the brain, even briefly, can cause permanent injury, including stroke and cognitive impairment. In M.R.I.s conducted by Dr. Kawata and his colleagues (including Dr. Herbenick, who is a co-author of his papers on strangulation), undergraduate women who have been repeatedly choked show a reduction in cortical folding in the brain compared with a never-choked control group. They also showed widespread cortical thickening, an inflammation response that is associated with elevated risk of later-onset mental illness. In completing simple memory tasks, their brains had to work far harder than the control group, recruiting from more regions to achieve the same level of accuracy.

The hemispheres in the choked group’s brains, too, were badly skewed, with the right side hyperactive and the left underperforming. A similar imbalance is associated with mood disorders — and indeed in Dr. Herbenick’s surveys girls and women who had been choked were more likely than others (or choked men) to have experienced overwhelming anxiety, as well as sadness and loneliness, with the effect more pronounced as the incidence rose: Women who had experienced more than five instances of choking were two and a half times as likely as those who had never been choked to say they had been so depressed within the previous 30 days they couldn’t function. Whether girls and women with mental health challenges are more likely to seek out (or be subjected to) choking, choking causes mood disorders, or some combination of the two is still unclear. But hypoxia, or oxygen deprivation — judging by what research has shown about other types of traumatic brain injury — could be a contributing factor. Given the soaring rates of depression and anxiety among young women, that warrants concern.

Now consider that every year Dr. Herbenick has done her survey, the number of females reporting extreme effects from strangulation (neck swelling, loss of consciousness, losing control of urinary function) has crept up. Among those who’ve been choked, the rate of becoming what students call “cloudy” — close to passing out, but not crossing the line — is now one in five, a huge proportion. All of this indicates partners are pressing on necks longer and harder.

The physical, cognitive and psychological impacts of sexual choking are disturbing. So is the idea that at a time when women’s social, economic, educational and political power are in ascent (even if some of those rights may be in jeopardy), when #MeToo has made progress against harassment and assault, there has been the popularization of a sex act that can damage our brains, impair intellectual functioning, undermine mental health, even kill us. Nonfatal strangulation, one of the most significant indicators that a man will murder his female partner (strangulation is also one of the most common methods used for doing so), has somehow been eroticized and made consensual, at least consensual enough. Yet, the outcomes are largely the same: Women’s brains and bodies don’t distinguish whether they are being harmed out of hate or out of love.

By now I’m guessing that parents are curled under their chairs in a fetal position. Or perhaps thinking, “No, not my kid!” (see: title of Dr. Herbenick’s book above, which, by the way, contains an entire chapter on how to talk to your teen about “rough sex”).

I get it. It’s scary stuff. Dr. Herbenick is worried; I am, too. And we are hardly some anti-sex, wait-till-marriage crusaders. But I don’t think our only option is to wring our hands over what young people are doing.

Parents should take a beat and consider how they might give their children relevant information in a way that they can hear it. Maybe reiterate that they want them to have a pleasurable sex life — you have already said that, right? — and also want them to be safe. Tell them that misinformation about certain practices, including choking, is rampant, that in reality it has grave health consequences. Plus, whether or not a partner initially requested it, if things go wrong, you’re generally criminally on the hook.

Dr. Herbenick suggests reminding them that there are other, lower-risk ways to be exploratory or adventurous if that is what they are after, but it would be wisest to delay any “rough sex” until they are older and more skilled at communicating. She offers language when negotiating with a new partner, such as, “By the way, I’m not comfortable with” — choking, or other escalating behaviors such as name-calling, spitting and genital slapping — “so please don’t do it/don’t ask me to do it to you.” They could also add what they are into and want to do together.

I’d like to point high school health teachers to evidence-based porn literacy curricula, but I realize that incorporating such lessons into their classrooms could cost them their jobs. Shafia Zaloom, a lecturer at the Harvard Graduate School of Education, recommends, if that’s the case, grounding discussions in mainstream and social media. There are plenty of opportunities. “You can use it to deconstruct gender norms, power dynamics in relationships, ‘performative’ trends that don’t represent most people’s healthy behaviors,” she said, “especially depictions of people putting pressure on someone’s neck or chest.”

I also know that pediatricians, like other adults, struggle when talking to adolescents about sex (the typical conversation, if it happens, lasts 40 seconds). Then again, they already caution younger children to use a helmet when they ride a bike (because heads and necks are delicate!); they can mention that teens might hear about things people do in sexual situations, including choking, then explain the impact on brain health and why such behavior is best avoided. They should emphasize that if, for any reason — a fall, a sports mishap or anything else — a young person develops symptoms of head trauma, they should come in immediately, no judgment, for help in healing.

The role and responsibility of the entertainment industry is a tangled knot: Media reflects behavior but also drives it, either expanding possibilities or increasing risks. There is precedent for accountability. The European Union now requires age verification on the world’s largest porn sites (in ways that preserve user privacy, whatever that means on the internet); that discussion, unsurprisingly, had been politicized here. Social media platforms have already been pushed to ban content promoting eating disorders, self-harm and suicide — they should likewise be pressured to ban content promoting choking. Traditional formats can stop glamorizing strangulation, making light of it, spreading false information, using it to signal female characters’ complexity or sexual awakening. Young people’s sexual scripts are shaped by what they watch, scroll by and listen to — unprecedentedly so. They deserve, and desperately need, models of interactions that are respectful, communicative, mutual and, at the very least, safe.

Peggy Orenstein is the author of “Boys & Sex: Young Men on Hookups, Love, Porn, Consent and Navigating the New Masculinity” and “Girls & Sex: Navigating the Complicated New Landscape.”

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An earlier version of this article misstated the network on which “Californication” first appeared. It is Showtime, not HBO. The article also misspelled a book and film title. It is “Fifty Shades of Grey,” not “Fifty Shades of Gray.”

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