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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

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

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

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Step 1: ask a question.

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

Step 2: Do some preliminary research

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

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

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

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

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

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

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

General Education

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

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

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

So let’s get started!

What Is a Hypothesis?

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

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

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

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

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

Independent and Dependent Variables 

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

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

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

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

Elements of a Good Hypothesis

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

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

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

Writing Your Hypothesis

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

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

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

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

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

The Two Types of Hypotheses

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

#1: If-Then Hypotheses

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

If ____ happens, then ____ will happen.

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

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

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

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

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

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

#2: Null Hypotheses

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

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

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

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

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

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

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

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

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

4 Tips to Write the Best Hypothesis

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

#1: Plausibility

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

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

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

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

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

#2: Defined Concepts

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

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

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

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

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

Good hypotheses ensure that you can observe the results. 

#3: Observability

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

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

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

#4: Generalizability 

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

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

Hypothesis Testing Examples

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

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

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

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

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

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

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

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

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

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

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

Here’s what your hypotheses might look like: 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Key Takeaways: Hypothesis Writing

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

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

What’s Next?

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

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

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

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

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

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Table of Contents

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

What is a Hypothesis in Research?

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

Research Question vs Hypothesis

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

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

How to Write Hypothesis in Research

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

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

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

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

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

Research Hypothesis Example

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

Here are a few generic examples to get you started.

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

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

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

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

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How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

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

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

What is the difference between a hypothesis and a prediction?

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

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

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

Types of Research Hypotheses

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

Alternative Hypothesis

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

Null Hypothesis

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

Directional Hypothesis

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

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

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

Your null hypothesis would then be that

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

Nondirectional Hypothesis

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

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

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

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

How to Write a Hypothesis for a Research Paper

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

Writing a Hypothesis Step:1

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

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

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

Writing a Hypothesis Step 2:

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

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

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

Writing a Hypothesis Step 3:

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

Examples of a Good and Bad Hypothesis

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

Good Hypothesis Examples

Bad hypothesis examples, tips for writing a research hypothesis.

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

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

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

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

Perfect Your Manuscript With Professional Editing

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

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

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

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It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

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Learn How To Write A Hypothesis For Your Next Research Project!

Undoubtedly, research plays a crucial role in substantiating or refuting our assumptions. These assumptions act as potential answers to our questions. Such assumptions, also known as hypotheses, are considered key aspects of research. In this blog, we delve into the significance of hypotheses. And provide insights on how to write them effectively. So, let’s dive in and explore the art of writing hypotheses together.

Table of Contents

What is a Hypothesis?

A hypothesis is a crucial starting point in scientific research. It is an educated guess about the relationship between two or more variables. In other words, a hypothesis acts as a foundation for a researcher to build their study.

Here are some examples of well-crafted hypotheses:

• Increased exposure to natural sunlight improves sleep quality in adults.

A positive relationship between natural sunlight exposure and sleep quality in adult individuals.

• Playing puzzle games on a regular basis enhances problem-solving abilities in children.

Engaging in frequent puzzle gameplay leads to improved problem-solving skills in children.

• Students and improved learning hecks.

S tudents using online  paper writing service  platforms (as a learning tool for receiving personalized feedback and guidance) will demonstrate improved writing skills. (compared to those who do not utilize such platforms).

• The use of APA format in research papers. 

Using the  APA format  helps students stay organized when writing research papers. Organized students can focus better on their topics and, as a result, produce better quality work.

The Building Blocks of a Hypothesis

To better understand the concept of a hypothesis, let’s break it down into its basic components:

• Variables . A hypothesis involves at least two variables. An independent variable and a dependent variable. The independent variable is the one being changed or manipulated, while the dependent variable is the one being measured or observed.
• Relationship : A hypothesis proposes a relationship or connection between the variables. This could be a cause-and-effect relationship or a correlation between them.
• Testability : A hypothesis should be testable and falsifiable, meaning it can be proven right or wrong through experimentation or observation.

Types of Hypotheses

When learning how to write a hypothesis, it’s essential to understand its main types. These include; alternative hypotheses and null hypotheses. In the following section, we explore both types of hypotheses with examples. 

Alternative Hypothesis (H1)

This kind of hypothesis suggests a relationship or effect between the variables. It is the main focus of the study. The researcher wants to either prove or disprove it. Many research divides this hypothesis into two subsections: 

• Directional 

This type of H1 predicts a specific outcome. Many researchers use this hypothesis to explore the relationship between variables rather than the groups. 

• Non-directional

You can take a guess from the name. This type of H1 does not provide a specific prediction for the research outcome. 

Here are some examples for your better understanding of how to write a hypothesis.

• Consuming caffeine improves cognitive performance.  (This hypothesis predicts that there is a positive relationship between caffeine consumption and cognitive performance.)
• Aerobic exercise leads to reduced blood pressure.  (This hypothesis suggests that engaging in aerobic exercise results in lower blood pressure readings.)
• Exposure to nature reduces stress levels among employees.  (Here, the hypothesis proposes that employees exposed to natural environments will experience decreased stress levels.)
• Listening to classical music while studying increases memory retention.  (This hypothesis speculates that studying with classical music playing in the background boosts students’ ability to retain information.)
• Early literacy intervention improves reading skills in children.  (This hypothesis claims that providing early literacy assistance to children results in enhanced reading abilities.)
• Time management in nursing students. ( Students who use a  nursing research paper writing service  have more time to focus on their studies and can achieve better grades in other subjects. )

Null Hypothesis (H0)

A null hypothesis assumes no relationship or effect between the variables. If the alternative hypothesis is proven to be false, the null hypothesis is considered to be true. Usually a null hypothesis shows no direct correlation between the defined variables. 

Here are some of the examples

• The consumption of herbal tea has no effect on sleep quality.  (This hypothesis assumes that herbal tea consumption does not impact the quality of sleep.)
• The number of hours spent playing video games is unrelated to academic performance.  (Here, the null hypothesis suggests that no relationship exists between video gameplay duration and academic achievement.)
• Implementing flexible work schedules has no influence on employee job satisfaction.  (This hypothesis contends that providing flexible schedules does not affect how satisfied employees are with their jobs.)
• Writing ability of a 7th grader is not affected by reading editorial example. ( There is no relationship between reading an  editorial example  and improving a 7th grader’s writing abilities.) 
• The type of lighting in a room does not affect people’s mood.  (In this null hypothesis, there is no connection between the kind of lighting in a room and the mood of those present.)
• The use of social media during break time does not impact productivity at work.  (This hypothesis proposes that social media usage during breaks has no effect on work productivity.)

As you learn how to write a hypothesis, remember that aiming for clarity, testability, and relevance to your research question is vital. By mastering this skill, you’re well on your way to conducting impactful scientific research. Good luck!

Importance of a Hypothesis in Research

A well-structured hypothesis is a vital part of any research project for several reasons:

• It provides clear direction for the study by setting its focus and purpose.
• It outlines expectations of the research, making it easier to measure results.
• It helps identify any potential limitations in the study, allowing researchers to refine their approach.

In conclusion, a hypothesis plays a fundamental role in the research process. By understanding its concept and constructing a well-thought-out hypothesis, researchers lay the groundwork for a successful, scientifically sound investigation.

How to Write a Hypothesis?

Here are five steps that you can follow to write an effective hypothesis. 

Step 1: Identify Your Research Question

The first step in learning how to compose a hypothesis is to clearly define your research question. This question is the central focus of your study and will help you determine the direction of your hypothesis.

Step 2: Determine the Variables

When exploring how to write a hypothesis, it’s crucial to identify the variables involved in your study. You’ll need at least two variables:

• Independent variable : The factor you manipulate or change in your experiment.
• Dependent variable : The outcome or result you observe or measure, which is influenced by the independent variable.

Step 3: Build the Hypothetical Relationship

In understanding how to compose a hypothesis, constructing the relationship between the variables is key. Based on your research question and variables, predict the expected outcome or connection. This prediction should be specific, testable, and, if possible, expressed in the “If…then” format.

Step 4: Write the Null Hypothesis

When mastering how to write a hypothesis, it’s important to create a null hypothesis as well. The null hypothesis assumes no relationship or effect between the variables, acting as a counterpoint to your primary hypothesis.

Step 5: Review Your Hypothesis

Finally, when learning how to compose a hypothesis, it’s essential to review your hypothesis for clarity, testability, and relevance to your research question. Make any necessary adjustments to ensure it provides a solid basis for your study.

In conclusion, understanding how to write a hypothesis is crucial for conducting successful scientific research. By focusing on your research question and carefully building relationships between variables, you will lay a strong foundation for advancing research and knowledge in your field.

Hypothesis vs. Prediction: What’s the Difference?

Understanding the differences between a hypothesis and a prediction is crucial in scientific research. Often, these terms are used interchangeably, but they have distinct meanings and functions. This segment aims to clarify these differences and explain how to compose a hypothesis correctly, helping you improve the quality of your research projects.

Hypothesis: The Foundation of Your Research

A hypothesis is an educated guess about the relationship between two or more variables. It provides the basis for your research question and is a starting point for an experiment or observational study.

The critical elements for a hypothesis include:

• Specificity: A clear and concise statement that describes the relationship between variables.
• Testability: The ability to test the hypothesis through experimentation or observation.

To learn how to write a hypothesis, it’s essential to identify your research question first and then predict the relationship between the variables.

Prediction: The Expected Outcome

A prediction is a statement about a specific outcome you expect to see in your experiment or observational study. It’s derived from the hypothesis and provides a measurable way to test the relationship between variables.

Here’s an example of how to write a hypothesis and a related prediction:

• Hypothesis: Consuming a high-sugar diet leads to weight gain.
• Prediction: People who consume a high-sugar diet for six weeks will gain more weight than those who maintain a low-sugar diet during the same period.

Key Differences Between a Hypothesis and a Prediction

While a hypothesis and prediction are both essential components of scientific research, there are some key differences to keep in mind:

• A hypothesis is an educated guess that suggests a relationship between variables, while a prediction is a specific and measurable outcome based on that hypothesis.
• A hypothesis can give rise to multiple experiment or observational study predictions.

To conclude, understanding the differences between a hypothesis and a prediction, and learning how to write a hypothesis, are essential steps to form a robust foundation for your research. By creating clear, testable hypotheses along with specific, measurable predictions, you lay the groundwork for scientifically sound investigations.

Here’s a wrap-up for this guide on how to write a hypothesis. We’re confident this article was helpful for many of you. We understand that many students struggle with writing their school research . However, we hope to continue assisting you through our blog tutorial on writing different aspects of academic assignments.

For further information, you can check out our reverent blog or contact our professionals to avail amazing writing services. Paper perk experts tailor assignments to reflect your unique voice and perspectives. Our professionals make sure to stick around till your satisfaction. So what are you waiting for? Pick your required service and order away!

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How to Write a Hypothesis

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

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

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

Hypothesis Definition

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

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

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

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

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

Characteristics and Sources of a Hypothesis

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

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

The main sources of a hypothesis are:

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

Types of Hypothesis

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

• Alternative Hypothesis

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

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

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

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

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

• Null Hypothesis

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

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

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

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

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

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

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

For example: Eating more vegetables leads to better immunity.

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

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

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

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

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Hypothesis vs Prediction

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

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

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

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

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

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

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

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

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

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

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

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

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

1. Define Your Research Question

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

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

2. Conduct Your Basic Initial Research

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

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

3. Formulate a Hypothesis

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

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

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

4. Refine Your Hypothesis

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

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

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Hypothesis Examples

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

Ask Pros to Make a Perfect Hypothesis for You!

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

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• 2018/03/28/How-to-write-a-hypothesis

How to write a hypothesis

This is a sticking point for many students. We are used to using and writing questions and statements in day to day communications, as well as reading popular media. But hypotheses (the plural of hypothesis) only rarely float across our desks. So how do we write one, and how do we know if our hypothesis is good?

Although I’m going to write about what I think, there is already some good information out there on the web, and it’s worth looking at this too: (e.g. Wikihow , Wikipedia , etc.). There’s also some dodgy stuff, so read critically.

What is a hypothesis?

A hypothesis is a statement of your research intent. It tells the reader (because just like all of your other written work, it has an audience who reads it), what you planned to do in your research. But there’s a little more to it than this. The hypothesis becomes a part of the scientific method if it is testable, and informed from previous published work on the subject.

Yes, your hypothesis must  be informed by the literature, which is why you spent so much time and effort crafting your introduction to inform your reader of the same. This is also why your hypothesis usually comes at the end of your introduction, because you spend all of the introduction telling your reader about it (see blog entry here ). There’s not much point in writing more after the hypothesis, because once your reader has read that, they are ready to learn about how you went about testing it (in the Materials & Methods). The other important point to make is that the literature should dictate how you write your hypothesis, and the variables that you include. If, for example, you know that temperature is the most important variable but all of the literature suggests that it is oxygen, you can’t ignore oxygen and you should also frame your hypothesis using this variable (you can have more than one hypothesis after all!). In this case, you will also need to provide a sufficient introduction to temperature as a variable to justify its inclusion in your hypothesis. Perversely, your aim is not to prove that your idea is right, but to show that the hypothesis is wrong.

We usually try to write a hypothesis that is falsifiable: i.e. you can prove (usually using statistical tests) that it is not correct (or at least show that the likelihood that it is correct is very low). That’s why it is conventional to provide the ‘Null hypothesis’ that is the falsified version of the statement, suggesting that there is no relationship between the variables you have proposed to measure. The convention is to label this H 0 , while the ‘alternative hypothesis’ (the one that says your variables are related as you suggested) is written as H 1 . You can write you alternative hypothesis to show the directionality of your tested variables, or simply that there is a relationship.

Most importantly, your hypothesis must come first, before you do the experiment or study! Setting the hypothesis after the work is already done is fraudulent, and goes against the scientific method. Obviously, it isn’t fair to pose the hypothesis once you already know the answer. This is why there is so much emphasis put on formulating your hypothesis during your research proposal. Getting it right will determine what you do and how you test it. If you think of an extra hypothesis that would be really useful to test once you’ve already done your study, you can conduct a post hoc test, but this should have more stringent levels of statistical assessment.

Writing a hypothesis isn’t easy, but it is essential and once you’ve understood what to do, most of the rest of what you are writing for should make sense.

What a hypothesis isn’t

It is not a question and so should never have a question mark after it.

It isn’t really a simple prediction: if this then that. You will see many times on the internet that hypotheses are explained in this simple predictive framework. I say that it isn't ' really ' a simple prediction because these are not good hypotheses. They lack the mechanistic and scholarly aspect of a good hypothesis, which is what we want to achieve.

A formulaic way to start writing your hypothesis: “ If… then… because… ”

Above, I emphasised that you must have introduced all the variables that you plan to use to test your hypothesis in your introduction. This usually comes in the second paragraph ( see blog entry here ), where you emphasise the utility of the dependent variable/s (what you are planning to measure) and your independent variable (what you will manipulate). Both of these variables should then feature in your hypothesis. Next, by paragraph four you will have identified the problem that you are interested in tackling. In addition, your introduction will provide all of the pertinent literature that has relevance to this hypothesis, giving the all important context.

A simple way to consider making your hypothesis is to adopt an “ If… then… because… ” construction where you add in your problem statement using your independent variable after ‘ if ’ and your prediction using your dependent variable after ‘ then ’, and finally the expected mechanism after ‘ because ’. Using our example above with the “If… then… because…” construction, we would say: “ If environmental temperatures in which tadpoles develop are increased then tadpole development rate is faster because they follow the classic metabolism of ectotherms”. Both independent variable (temperature) and dependent variable (tadpole development rate) are present in this hypothesis, and the predicted relationship between them is clear. In addition, the causal mechanism is stated. You can watch a video about using the “If… then… because…” construction here , or read more here . I say that this is a formulaic way to start writing your hypothesis, because it usually ends up as an inelegant statement, which can be better refined for a reader. A citation for your stated mechanism might also help clarify exactly where the justification for this comes from.

A good hypothesis will often take an existing hypothesis further, to try to better refine the knowledge on a subject. Hence, it is perfectly acceptable to state that you are building on existing hypotheses (and giving the appropriate statement) when making your own.

How to evaluate your hypothesis

Once you’ve written your hypothesis, how do you decide whether or not it is good? To do this, you might think that you need plenty of experience (and yes, that does help). But really, you just need to look for the elements that are discussed above. So once you’ve written your hypothesis, try to objectively answer the questions below (for more see Bartos 1992 and here ):

• Is there a clear prediction (if… then… statement)?
• Does the prediction use independent and dependent variables correctly?
• Is the mechanism supported by the literature?
• Is the hypothesis testable/falsifiable?
• Does the hypothesis use concise wording and precise terminology?

If your hypothesis meets all of the criteria above, then you’ve done a good job!

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An hypothesis is a specific statement of prediction. It describes in concrete (rather than theoretical) terms what you expect will happen in your study. Not all studies have hypotheses. Sometimes a study is designed to be exploratory (see inductive research ). There is no formal hypothesis, and perhaps the purpose of the study is to explore some area more thoroughly in order to develop some specific hypothesis or prediction that can be tested in future research. A single study may have one or many hypotheses.

Actually, whenever I talk about an hypothesis, I am really thinking simultaneously about two hypotheses. Let’s say that you predict that there will be a relationship between two variables in your study. The way we would formally set up the hypothesis test is to formulate two hypothesis statements, one that describes your prediction and one that describes all the other possible outcomes with respect to the hypothesized relationship. Your prediction is that variable A and variable B will be related (you don’t care whether it’s a positive or negative relationship). Then the only other possible outcome would be that variable A and variable B are not related. Usually, we call the hypothesis that you support (your prediction) the alternative hypothesis, and we call the hypothesis that describes the remaining possible outcomes the null hypothesis. Sometimes we use a notation like HA or H1 to represent the alternative hypothesis or your prediction, and HO or H0 to represent the null case. You have to be careful here, though. In some studies, your prediction might very well be that there will be no difference or change. In this case, you are essentially trying to find support for the null hypothesis and you are opposed to the alternative.

If your prediction specifies a direction, and the null therefore is the no difference prediction and the prediction of the opposite direction, we call this a one-tailed hypothesis . For instance, let’s imagine that you are investigating the effects of a new employee training program and that you believe one of the outcomes will be that there will be less employee absenteeism. Your two hypotheses might be stated something like this:

The null hypothesis for this study is:

HO: As a result of the XYZ company employee training program, there will either be no significant difference in employee absenteeism or there will be a significant increase .

which is tested against the alternative hypothesis:

HA: As a result of the XYZ company employee training program, there will be a significant decrease in employee absenteeism.

In the figure on the left, we see this situation illustrated graphically. The alternative hypothesis – your prediction that the program will decrease absenteeism – is shown there. The null must account for the other two possible conditions: no difference, or an increase in absenteeism. The figure shows a hypothetical distribution of absenteeism differences. We can see that the term “one-tailed” refers to the tail of the distribution on the outcome variable.

When your prediction does not specify a direction, we say you have a two-tailed hypothesis . For instance, let’s assume you are studying a new drug treatment for depression. The drug has gone through some initial animal trials, but has not yet been tested on humans. You believe (based on theory and the previous research) that the drug will have an effect, but you are not confident enough to hypothesize a direction and say the drug will reduce depression (after all, you’ve seen more than enough promising drug treatments come along that eventually were shown to have severe side effects that actually worsened symptoms). In this case, you might state the two hypotheses like this:

HO: As a result of 300mg./day of the ABC drug, there will be no significant difference in depression.

HA: As a result of 300mg./day of the ABC drug, there will be a significant difference in depression.

The figure on the right illustrates this two-tailed prediction for this case. Again, notice that the term “two-tailed” refers to the tails of the distribution for your outcome variable.

The important thing to remember about stating hypotheses is that you formulate your prediction (directional or not), and then you formulate a second hypothesis that is mutually exclusive of the first and incorporates all possible alternative outcomes for that case. When your study analysis is completed, the idea is that you will have to choose between the two hypotheses. If your prediction was correct, then you would (usually) reject the null hypothesis and accept the alternative. If your original prediction was not supported in the data, then you will accept the null hypothesis and reject the alternative. The logic of hypothesis testing is based on these two basic principles:

• the formulation of two mutually exclusive hypothesis statements that, together, exhaust all possible outcomes
• the testing of these so that one is necessarily accepted and the other rejected

OK, I know it’s a convoluted, awkward and formalistic way to ask research questions. But it encompasses a long tradition in statistics called the hypothetical-deductive model , and sometimes we just have to do things because they’re traditions. And anyway, if all of this hypothesis testing was easy enough so anybody could understand it, how do you think statisticians would stay employed?

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Career in Consulting

Hypothesis-driven approach: the definitive guide

Imagine you are walking in one of McKinsey’s offices.

Around you, there are a dozen of busy consultants.

The word “hypothesis” would be one of the words you would hear the most.

Along with “MECE” or “what’s the so-what?”.

This would also be true in any BCG, Bain & Company office or other major consulting firms.

Because strategy consultants are trained to use a hypothesis-driven approach to solve problems.

And as a candidate, you must demonstrate your capacity to be hypothesis-driven in your case interviews .

There is no turnaround:

If you want a consulting offer, you MUST know how to use a hypothesis-driven approach .

Like a consultant would be hypothesis-driven on a real project for a real client?

Hell, no! Big mistake!

Because like any (somehow) complex topics in life, the context matters.

What is correct in one context becomes incorrect if the context changes.

And this is exactly what’s happening with using a hypothesis-driven approach in case interviews.

This should be different from the hypothesis-driven approach used by consultant solving a problem for a real client .

And that’s why many candidates get it wrong (and fail their interviews).

They use a hypothesis-driven approach like they were already a consultant.

Thus, in this article, you’ll learn the correct definition of being hypothesis-driven in the context of case interviews .

Plus, you’ll learn how to use a hypothesis in your case interviews to “crack the case”, and more importantly get the well-deserved offer!

Ready? Let’s go. It will be super interesting!

Table of Contents

The wrong hypothesis-driven approach in case interviews.

Let’s start with a definition:

Hypothesis-driven thinking is a problem-solving method whereby you start with the answer and work back to prove or disprove that answer through fact-finding.

Concretely, here is how consultants use a hypothesis-driven approach to solve their clients’ problems:

• Form an initial hypothesis, which is what they think the answer to the problem is.
• Craft a logic issue tree , by asking themselves “what needs to be true for the hypothesis to be true?”
• Walk their way down the issue tree and gather the necessary data to validate (or refute) the hypothesis.
• Reiterate the process from step 1 – if their first hypothesis was disproved by their analysis – until they get it right.

With this answer-first approach, consultants do not gather data to fish for an answer. They seek to test their hypotheses , which is a very efficient problem-solving process.

The answer-first thinking works well if the initial hypothesis has been carefully formed.

This is why – in top consulting firms like McKinsey , BCG , or Bain & Company – the hypothesis is formed by a Partner with 20+ years of work experience.

And this is why this is NOT the right approach for case interviews.

Imagine a candidate doing a case interview at McKinsey and using answer-first thinking.

At the beginning of a case, this candidate forms a hypothesis (a potential answer to the problem), builds a logic tree, and gathers data to prove the hypothesis.

Here, there are two options:

The initial hypothesis is right

The initial hypothesis is wrong

If the hypothesis is right, what does it mean for the candidate?

That the candidate was lucky.

Nothing else.

And it certainly does not prove the problem-solving skills of this candidate (which is what is tested in case interviews).

Now, if the hypothesis is wrong, what’s happening next?

The candidate reiterates the process.

Imagine how disorganized the discussion with the interviewer can be.

Most of the time, such candidates cannot form another hypothesis, the case stops, and the candidate feels miserable.

This leads us to the right hypothesis-driven approach for case interviews.

The right hypothesis-driven approach in case interviews

To make my point clear between the wrong and right approach, I’ll take a non-business example.

Let’s imagine you want to move from point A to point B.

And for that, you have the choice among a multitude of roads.

Using the answer-first approach presented in the last section, you’d know which road to take to move from A to B (for instance the red line in the drawing below).

Again, this would not demonstrate your capacity to find the “best” road to go from A to B.

(regardless of what “best” means. It can be the fastest or the safest for instance.)

Now, a correct hypothesis-driven approach consists in drawing a map with all the potential routes between A and B, and explaining at each intersection why you want to turn left or right (” my hypothesis is that we should turn right ”).

And in the context of case interviews?

In the above analogy:

• A is the problem
• B is the solution
• All the potential routes are the issues in your issue tree

And the explanation of why you want to take a certain road instead of another would be your hypothesis.

Is the difference between the wrong and right hypothesis-driven approach clearer?

If not, don’t worry. You’ll find many more examples below in this article.

But, next, let’s address another important question.

Why you must (always) use a hypothesis in your case interviews

You must use a hypothesis in your case interviews for two reasons.

A hypothesis helps you focus on what’s important to solve the case

Using a hypothesis-driven approach is critical to solving a problem efficiently.

In other words:

A hypothesis will limit the number of analysis you need to perform to solve a problem.

Thus, this is a way to apply the 80/20 principle and prioritize the issues (from your MECE issue tree ) you want to investigate.

And this is very important because your time with your interviewer is limited (like is the time with your client on a real project).

Let’s take a simple example of a hypothesis:

The profits of your client have dropped.

And your initial analysis shows increasing costs and stagnating revenues.

So your hypothesis can be:

“I think something happened in our cost structure, causing the profit drop. Next, I’d like to understand better the cost structure of our clients and which cost items have changed recently.”

Here the candidate is rigorously “cutting” half of his/her issue tree (the revenue side) and will focus the case discussion on the cost side.

And this is a good example of a hypothesis in case interviews.

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A hypothesis tells your interviewers why you want to do an analysis

There is a road that you NEVER want to take.

On this road, the purpose of the questions asked by a candidate is not clear.

Here are a few examples:

“What’s the market size? growth?”

“Who are the main competitors? what are their market shares?”

“Have customer preferences changed in this market?”

This list of questions might be relevant to solve the problem at stake.

But how these questions help solve the problem is not addressed.

Or in other words, the logical connection between these questions and the problem needs to be included.

So, a better example would be:

“We discovered that our client’s sales have declined for the past three years. I would like to know if this is specific to our client or if the whole market has the same trend. Can you tell me how the market size has changed over the past three years? »

In the above question, the reason why the candidate wants to investigate the market is clear: to narrow down the analysis to an internal root cause or an external root cause.

Yet, I see only a few (great) candidates asking clear and purposeful questions.

You want to be one of these candidates.

How to use a hypothesis-driven approach in your case interviews?

At this stage, you understand the importance of a hypothesis-driven approach in case interviews:

You want to identify the most promising areas to analyze (remember that time is money ).

And there are two (and only two) ways to create a good hypothesis in your case interviews:

• a quantitative way
• a qualitative way

Let’s start with the quantitative way to develop a good hypothesis in your case interviews.

The quantitative approach: use the available data

Let’s use an example to understand this data-driven approach:

Interviewer: your client is manufacturing computers. They have been experiencing increasing costs and want to know how to address this issue.

Candidate: to begin with, I want to know the breakdown of their cost structure. Do you have information about the % breakdown of their costs?

Interviewer: their materials costs count for 30% and their manufacturing costs for 60%. The last 10% are SG&A costs.

Candidate: Given the importance of manufacturing costs, I’d like to analyze this part first. Do we know if manufacturing costs go up?

Interviewer: yes, manufacturing costs have increased by 20% over the past 2 years.

Candidate: interesting. Now, it would be interesting to understand why such an increase happened.

You can notice in this example how the candidate uses data to drive the case discussion and prioritize which analysis to perform.

The candidate made a (correct) hypothesis that the increasing costs were driven by the manufacturing costs (the biggest chunk of the cost structure).

Even if the hypothesis were incorrect, the candidate would have moved closer to the solution by eliminating an issue (manufacturing costs are not causing the overall cost increase).

That said, there is another way to develop a good hypothesis in your case interviews.

The qualitative approach: use your business acumen

Sometimes you don’t have data (yet) to make a good hypothesis.

Thus, you must use your business judgment and develop a hypothesis.

Again, let’s take an example to illustrate this approach.

Interviewer: your client manufactures computers and has been losing market shares to their direct competitors. They hired us to find the root cause of this problem.

Candidate: I think of many reasons explaining the drop in market shares. First, our client manufactures and sells not-competitive products. Secondly, we might price our products too high. Third, we need to use the right distribution channels. For instance, we might sell in brick-and-mortars stores when consumers buy their computers in e-stores like Amazon. Finally, I think of our marketing expenses. There may be too low or not used strategically.

Candidate: I see these products as commodities where consumers use price as the main buying decision criteria. That’s why I’d like to explore how our client prices their products. Do you have information about how our prices compare to competitors’?

Interviewer: this is a valid point. Here is the data you want to analyze.

Note how this candidate explains what she/he wants to analyze first (prices) and why (computers are commodities).

In this case interview, the hypothesis-driven approach looks like this:

This is a commodity industry —> consumers buying behavior is driven by pricing —> our client’s prices are too high.

Again, note how the candidate first listed the potential root causes for this situation and did not use an answer-first approach.

Want to learn more?

In this free training , I explain in detail how to use data or your business acumen to prioritize the issues to analyze and “crack the case.”

Also, you’ll learn what to do if you don’t have data or can’t use your business acumen.

Sign up now for free .

Form a hypothesis in these two critical moments of your case interviews

After you’ve presented your initial structure.

The first moment to form a hypothesis in your case interview?

In the beginning, after you’ve presented your structure.

When you’ve presented your issue tree, mention which issue you want to analyze first.

Also, explain why you want to investigate this first issue.

Make clear how the outcome of the analysis of this issue will help you solve the problem.

After an analysis

The second moment to form a hypothesis in your case interview?

After you’ve derived an insight from data analysis.

This insight has proved (or disproved) your hypothesis.

Either way, after you have developed an insight, you must form a new hypothesis.

This can be the issue you want to analyze next.

Or what a solution to the problem is.

Hypothesis-driven approach in case interviews: a conclusion

Having spent about 10 years coaching candidates through the consulting recruitment process , one commonality of successful candidates is that they truly understand how to be hypothesis-driven and demonstrate efficient problem-solving.

Plus, per my experience in coaching candidates , not being able to use a hypothesis is the second cause of rejection in case interviews (the first being the lack of MECEness ).

This means you can’t afford NOT to master this concept in a case study.

So, sign up now for this free course to learn how to use a hypothesis-driven approach in your case interviews and land your dream consulting job.

More than 7,000 people have already signed up.

Don’t waste one more minute!

See you there.

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The Science of Siblings

Gay people often have older brothers. why and does it matter.

Selena Simmons-Duffin

Credit: Lily Padula for NPR

The Science of Siblings is a new series exploring the ways our siblings can influence us, from our money and our mental health all the way down to our very molecules. We'll be sharing these stories over the next several weeks.

This is something I learned years ago through gay bar chatter: Gay people are often the youngest kids in their families. I liked the idea right away — as a gay youngest sibling, it made me feel like there was a statistical order to things and I fit neatly into that order.

When I started to report on the science behind it, I learned it's true: There is a well-documented correlation between having older siblings (older brothers, specifically) and a person's chance of being gay. But parts of the story also struck me as strange and dark. I thought of We the Animals , Justin Torres' haunting semi-autobiographical novel about three brothers — the youngest of whom is queer — growing up in New York state. So I called Torres to get his take on the idea.

Torres' first reaction was to find it considerably less appealing than I did. This makes sense — his latest novel, Blackouts , won a National Book Award last year, and it grapples with the sinister history of how scientists have studied sexuality. "My novel is interested in the pre-Kinsey sexology studies, specifically this one called Sex Variants ," he told me. "It's really informed by eugenics. They were looking for the cause of homosexuality in the body in order to treat it or cure it or get rid of it."

That's why, when he saw my inquiry about a statistical finding that connects sexuality and birth order, he was wary. "To be frank, I find these kinds of studies that're looking for something rooted in the body to explain sexuality to be kind of bunk. I think they rely on a really binary understanding of sexuality itself," he said.

"That's fair," I conceded. But this connection between queerness and older brothers has been found so many times in so many places that one researcher told me it's "a kind of truth" in the science of sexuality.

Rooted in a dark past

The first research on this topic did indeed begin in the 1940s and '50s, during that era of investigations into what causes homosexuality, to be able to cure it. At the time, the queer people whom scientists were studying were living in a world where this facet of their identity was dangerous. Plus, the studies themselves didn't find much, says Jan Kabátek , a senior research fellow at the University of Melbourne.

"Most of it fell flat," he told me. "But there is an exception to this, and that is the finding that men, specifically, who exhibit attraction to the same sex are likely to have more older brothers than other types of siblings."

In the 1990s, this was dubbed the "fraternal birth order effect." In the years since, it has been found again and again, all over the world.

"This pattern has been documented around Canada and the United States, but it goes well beyond that," says Scott Semenyna , a psychology professor at Stetson University. "There's been now many confirmations that this pattern exists in countries like Samoa. It exists in southern Mexico. It exists in places like Turkey and Brazil."

Huge study, consistent findings

An impressive recent study established that this pattern held up in an analysis of a huge sample — over 9 million people from the Netherlands. It confirmed all those earlier studies and added a twist.

"Interestingly enough — and this is quite different from what has been done before — we also showed that the same association manifests for women," explains Kabátek, one of the study's authors. Women who were in same-sex marriages were also more likely to have older brothers than other types of siblings.

At baseline, the chance that someone will be gay is pretty small. "Somewhere around 2 to 3% — we can call it 2% just for the sake of simplicity," Semenyna says. "The fraternal birth order effect shows that you're going to run into about a 33% increase in the probability of, like, male same-sex attraction for every older brother that you have."

The effect is cumulative: The more older brothers someone has, the bigger it is. If you have one older brother, your probability of being gay nudges up to about 2.6%. "And then that probability would increase another 33% if there was a second older brother, to about 3.5%," Semenyna says.

If you have five older brothers, your chance of being gay is about 8% — so, four times the baseline probability.

The author, Selena Simmons-Duffin, at age 3, with her brother, David Simmons-Duffin, at age 5. The Simmons-Duffin family hide caption

The author, Selena Simmons-Duffin, at age 3, with her brother, David Simmons-Duffin, at age 5.

Still, even 8% is pretty small. "The vast majority of people who have a lot of older brothers are still going to come out opposite-sex attracted," Semenyna says. Also, plenty of gay people have no brothers at all, or they're the oldest in their families. Having older brothers is definitely not the only influence on a person's sexuality.

"But just the fact that we are observing effects that are so strong, relatively speaking, implies that there's a good chance that there is, at least partially, some biological mechanism that is driving these associations," Kabátek says.

A hypothesis, but no definitive mechanism

For decades, the leading candidate for that biological mechanism has been the "maternal immune hypothesis," Semenyna explains. "The basic version of this hypothesis is that when a male fetus is developing, the Y chromosome of the male produces proteins that are going to be recognized as foreign by the mother's immune system and it forms somewhat of an immune response to those proteins."

That immune response has some effect on the development of subsequent male fetuses, Semenyna says. The plausibility of this hypothesis was bolstered by a 2017 study that found "that mothers of gay sons have more of these antibodies that target these male-specific proteins than mothers of sons who are not gay or mothers who have no sons whatsoever," he says.

But now that Kabátek's study of the Dutch population has found that this pattern was present among women in same-sex marriages as well, there are new questions about whether this hypothesis is correct.

"One option is that the immune hypothesis works for both men and women," Kabátek says. "Of course, there can be also other explanations. It's for prospective research to make this clearer."

Fun to think about, but concerning too

In a way, I tell Justin Torres, this effect seems simple and fun to me. It's a concrete statistical finding, documented all over the world, and there's an intriguing hypothesis about why it may happen biologically. But darker undercurrents in all of it worry me, like raising a dangerous idea that becoming gay in the womb is the only version of gayness that is real — or a repackaged version of the old idea that mothers are to "blame."

"It is the undercurrents that worry me immensely," he responds. "I remember when I was a kid — I have this memory of watching daytime television. I must have been staying home from school sick in the late '80s or early '90s. The host polled the audience and said, 'If there was a test [during pregnancy] and you could know if your child was gay, would you abort?' I remember being so horrified and disturbed watching all those hands go up in the audience — just feeling so hated. At that young age, I knew this thing about myself, even if I wasn't ready to admit it."

Even if tolerance for queer people in American society has grown a lot since then, he says, "I think that tolerance waxes and wanes, and I worry about that line of thinking."

At the same time, he agrees that the idea of a connection with gay people being the youngest kids in their families is kind of hilarious. "One thing that pops into my mind is, like, maybe if you're just surrounded by a lot of men, you either choose or don't choose men, right?" he laughs.

Essentially, in his view, it's fun to think about, but probably not deeper than that.

"As a humanist, I just don't know why we need to look for explanations for something as complex and joyous and weird as sexuality," Torres says.

Then again, scientists are unlikely to be able to resist that mysterious, weird complexity. Even if the joy and self-expression and community and so many other parts of queerness and sexuality will always be more than statistics can explain.

More from the Science of Siblings series:

• A gunman stole his twin from him. This is what he's learned about grieving a sibling
• In the womb, a brother's hormones can shape a sister's future
• These identical twins both grew up with autism, but took very different paths
• Science of Siblings
• queer community
• homosexuality

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Andrew Scott (from left), Marisol Amaya, Caitlin Coyle, and Ashwin Vasan discuss changing attitudes toward aging with moderator Kay Lazar.

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America’s graying. We need to change the way we think about age.

Experts say instead of disability, focus needs to shift to ability, health, with greater participation, economically and socially

Alvin Powell

Harvard Staff Writer

People in their 70s, 80s, and even 90s run marathons, write books, and go to work daily. But the predominant national conversation on aging focuses on disability rather than ability, something experts say is a problem as participation of America’s older adults grows more important, economically and socially, as the nation ages.

And it’s not just the young with the attitude problem. Caitlin Coyle, director of UMass Boston’s Center for Social & Demographic Research on Aging, said some of the biggest perpetuators of negative stereotypes are those growing older themselves.

“How we talk about it is powerful,” Coyle said Wednesday, as part of a discussion of the topic at the Harvard T.H. Chan School of Public Health . “We do a lot of internalized ageism with self-talk like ‘Oh, I feel so old today,’ or ‘I can’t do that,’ ‘I’m too old for that,’ or ‘I can’t stay up late.’ I think if we start to engage people in thinking about how they talk about aging outwardly — and also how they think about aging internally — we can really start to shift the societal narrative.”

The panelists at the event “A reexamination of aging: Living longer, happier, and healthier” agreed attitudes about aging set expectations for ourselves and others, but what’s also important are programs and policies crafted to encourage healthy aging — via prevention and risk-factor reduction, along with involvement in society, through work, volunteerism, family relationships, religious organizations, or other ways of engaging that can bring meaning to life.

“I think if we start to engage people in thinking about how they talk about aging outwardly — and also how they think about aging internally — we can really start to shift the societal narrative.” Caitlin Coyle

“We are expecting to live much longer than our parents and grandparents. And the structures we put in place to support healthy aging are really crucial,” said New York City Health Commissioner Ashwin Vasan, a Chan School graduate and member of the panel. “At every turn, we are not just looking at the averted negative consequences of unhealthy aging, but looking at the aspirational, forward-looking indicators of healthy aging.”

The discussion hosted by The Studio at the Chan School was moderated by Kay Lazar, a reporter at The Boston Globe, and also featured Marisol Amaya, executive director of La Alianza Hispana, and Andrew Scott, professor of economics at London Business School and author of the book “The Longevity Imperative: How to Build a Healthier and More Productive Society to Support Our Longer Lives.”

Panelists acknowledged that declining health and rising disability are part of the landscape as one grows old, but Scott said it seems that when people think about aging they “go straight to the end” where those factors take on greater importance.

He attributed part of that to the “medicalization” of aging, which focuses on health problems and death. But what’s lost in between are, for many, years of increased life satisfaction, greater acceptance of oneself and others, decreased pressure to live up to ideals of how to look and behave, greater emotional stability during crises, and new opportunities to learn and develop new skills.

“The trouble with aging is we tend to go straight to the end of life. And that’s one of the reasons why we don’t like to think about a longer life being about having more future,” Scott said. “How do you think about that? How do you prepare for it? We see aging as an event — you’re 65 years old — but it’s a process that is relevant to all ages. What can you do to manage that process and how do you make sure you make the most of it?”

The tendency to view aging through a medical lens is not the only issue, however. Family structure in the nation has changed as people have fewer children and multigenerational living arrangements grow less common. This has led to an increasing tendency to segregate society by age and worsened the problem of isolation among the elderly.

At La Alianza Hispana, a social service agency focused on the Latinx community of Greater Boston, the elderly are provided programming based on individual preferences — bingo, for instance, isn’t for everyone and should be reserved for those who love it, Amaya said. Others want to play instruments, others to learn something new, and still others to teach.

Accordingly, the nonprofit’s elder-services program emphasizes flexibility, even hiring people still willing to work and able to draw on a lifetime of experience. Some are lacking in computer and other tech skills, but those can be taught, which the organization does, she said.

“We are flexible. We always encourage them to do more, empower them, because they see barriers and say, ‘I cannot do it,’” Amaya said.

As we create a society that is more elderly-friendly, Scott said thinking about the economics of living longer is important, since many fear outliving their resources.

But it’s also important to think about how to allow people to stay productive longer, which has to begin earlier in life by guarding one’s health through better habits, thinking about how to make jobs more age-friendly, and perhaps shifting roles from those that require strength to those that emphasize skill and experience.

A significant amount of attention must also be paid, panelists agreed, to reducing inequality in healthful aging. The trend toward healthier aging is not universal, with Black and brown communities making up a disproportionate number of those who die before 65, Vasan said. That highlights the importance of addressing preventative health in those communities in the decades before 65.

“I think inequity is a real missing piece in the dialogue, the public narrative, around longevity and aging,” Vasan said. “How do we create a civic expectation that healthier, longer lives are more equitably experienced? These are not mutually exclusive agendas. The equity agenda is central to the life expectancy agenda. There is no path that doesn’t go through equity. Addressing the causes of premature death gets us to healthier, longer lives for everybody.”

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Key Bridge collapse: A tugboat escort could…

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Key bridge collapse: a tugboat escort could have prevented tragedy, some believe.

As the Dali slid out of its berth at the Port of Baltimore in the early hours of March 26, it wasn’t alone.

A pair of 5,000-horsepower tugboats guided the massive container ship into the deep channel in the Patapsco River, pointing it toward the Chesapeake Bay as it began its voyage to Sri Lanka. Then, off they went, according to maritime tracking data.

If they’d still been with the ship or close by, the tugs may have averted the ensuing disaster, as the Dali apparently lost power and drifted into a bridge support, toppling the Francis Scott Key Bridge, killing six road workers, two of whom have yet to be found.

There is no policy requiring tugboats to escort ships like the Dali beneath the Key Bridge, the largest obstacle for a ship navigating the Patapsco outbound into the bay, a bit more than 2 nautical miles from the terminal.

The typical practice for tugboat escorts at the Port of Baltimore, as at many other U.S. ports, is governed by agreements between key stakeholders, rather than formal policy. According to the Maryland Port Administration, decisions are made by ship’s captains and the Maryland pilots who board cargo vessels to guide them into and out of port.

Still, the fact that the Dali didn’t have a tugboat escort until it passed the bridge came as a surprise to some maritime experts.

“It makes no sense that that tug escort would be abandoned before she exited the critical area of the port, which is the bridge,” said maritime attorney Hugh “Skip” Lambert, of New Orleans.

But industry representatives said preventing crashes like the Dali’s cannot be solved simply by throwing extra tugboats at ships moving through ports across the nation, a solution that would be onerous, unnecessary and could drive container traffic away from more restrictive ports.

“It’s not like: ‘Oh, man, maybe we need two more tugs, three more tugs, four more tugs. Well, now nobody’s going to come to your port,” said Jennifer Carpenter, president and CEO of the American Waterways Operators, a trade organization representing the tugboat, towboat and barge industry.

The cost of tug escorts is part of the calculus, said Jeffrey Slesinger, a longtime tug captain who now trains mariners through his company Delphi Maritime, based in Edmonds, Washington.

“The analogy is this: Why don’t we have pedestrian lights at every street crossing in the U.S.? Why isn’t there a walk-don’t walk sign at every intersection we have?” Slesinger said.

The cost should be weighed against the risk — and the potential price of catastrophe, he said.

“The technology is there,” he said. “It’s a political and environmental balance, between using that technology and the expense that’s incurred by it, and the risk of whatever that vessel is either carrying or the risk of where it’s traveling.”

Ports would do well to conduct comprehensive safety assessments, and hopefully the Key Bridge disaster sparks those evaluations, Carpenter said. Tugboats are not the silver bullet, but rather one potential improvement, alongside beefing up protection for bridges and other infrastructure , she said.

These sorts of evaluations must be updated periodically, as changes in vessel traffic and vessel size alter the threat level. Any changes should happen port by port, Carpenter said, rather than through a blanket policy, given geographic, tidal and traffic differences between harbors. Often, organizations such as “harbor safety committees” discuss a port’s tug practices, but pilots play a leading role, she said.

The Maryland Association of Pilots did not respond to requests for comment for this article. McAllister Towing, the owner of the two tugboats that escorted the Dali from the pier to the channel, declined to comment.

Following the Key Bridge collapse, those conversations have begun already, said former merchant mariner Sal Mercogliano.

“It changed things that day, I can tell you that much,” he said. “There were discussions going on in ports that day.”

When the Key Bridge is rebuilt, it’s possible the policy in Baltimore changes. For the time being, large, deep-draft vessels like container ships must use two tugboat escorts as they pass through the temporary channel beside the wreckage, which opened for a few days starting Thursday , per an order from the Coast Guard’s Captain of the Port .

Any permanent change also likely would come from the Coast Guard, said Richard Scher, spokesman for the Maryland Port Administration. Baltimore has a harbor safety committee, which includes representatives from the Association of Maryland Pilots, the Coast Guard, police, the Army Corps of Engineers and others. But Scher said he is not aware of the committee discussing tugboat best practices for the Port of Baltimore.

The efficacy of tugboats was on full display less than two weeks after the Key Bridge collapsed, when another massive container ship, the APL Qingdao, lost propulsion in the Kill Van Kull Channel, which separates New York and New Jersey, on its way to the Verrazano-Narrows Bridge. When emergency struck, the vessel already had tugs attached, a “routine safety measure” that is common practice for the waterway approaching the bridge, according to the Coast Guard.

To be sure, the situation aboard the Qingdao wasn’t as dire as the circumstances aboard the Dali on March 26, when the ship apparently lost power, affecting not only its propulsion systems, but others as well. The Qingdao also was farther from the Verrazano-Narrows, which connects the New York boroughs of Staten Island and Brooklyn, when its propulsion systems failed.

For the crew of the Dali, power loss came at precisely the wrong moment, said retired Navy Capt. Lawrence Brennan, an attorney who teaches maritime law at Fordham Law School. The pilot on board the Dali issued a last-ditch request for tugboats as the ship drifted toward the Key Bridge, but it was too late.

“They were just too close,” he said. “I don’t know if assisting tugs would have been able to make a difference. They certainly wouldn’t have harmed the situation, if they were able to assist. But it’s geography and speed, time, distance — and fortuity.”

Slesinger said he has no doubt that, if there had been a properly equipped escort tug still attached to the Dali when it lost power in the Patapsco, it most likely would have saved the Key Bridge.

Modern tugs are “purpose-built,” he said, as opposed to the multipurpose vessels that dominated in the 1970s and ’80s. They’re more powerful and agile, specially designed to escort cargo vessels many times their size.

From the “escort position,” tugboats tethered to the stern of cargo ships exert steering and braking forces on ships like the Dali, Slesinger said.

For the U.S. tugboat industry, the Key Bridge collapse could have ripple effects similar to the Exxon Valdez oil spill in 1989, Slesinger said.

“It has the potential to be that kind of a game-changer,” Slesinger said.

That incident, considered among the worst environmental disasters in U.S. history, saw 11 million gallons of oil spilled into Alaska’s Prince William Sound after an oil tanker ran aground. It spawned the Oil Pollution Prevention Act of 1990, and the requirement that two tugboats accompany oil tankers exiting the Sound.

But past U.S. bridge collapses caused by vessel crashes haven’t spurred that level of transformative change, Slesinger said.

In 2002, Oklahoma’s Interstate 40 bridge was struck by a barge and plunged into the Arkansas River, killing 14 people and injuring 11.

“It raised awareness, people were talking about bridge infrastructure: ‘We gotta fix this.’ The industry, American Waterways Operators, they did some best practice stuff,” Slesinger said. “But it never went much beyond that. It never really got into the big political arena.”

The difference very well could be the video footage, which shows the Key Bridge dropping span by span into the river, mere moments after it was struck: a proud highway one second and a mangled wreck the next.

“That bridge just immediately collapsed,” Slesinger said. “That will burn itself into many people’s minds. And then they’ll be like: ‘How did this happen?’”

Baltimore Sun reporter Alex Mann contributed to this article.

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Insider Q&A: Avelo Airlines CEO Andrew Levy describes the challenges of starting a new carrier

It’s not easy to break into the U.S. airline industry, which is dominated by four big carriers and a sprinkling of other niche players, but that didn’t scare away Andrew Levy.

Neither did a pandemic that briefly caused air travel to plummet more than 90%.

In April 2021, while COVID-19 still raged and billions of dollars from taxpayers were propping up big airlines, Levy launched Avelo Airlines with flights between Burbank, California, and Las Vegas.

The airline saves money by flying older Boeing 737 jets that can be bought at relatively low prices. It operates out of less-crowded and less-costly secondary airports, and flies routes that are ignored by the big airlines.

Levy was involved in the launch of ValuJet, which became Allegiant Air, and he also did a stint as the chief financial officer of United Airlines before starting Avelo (it rhymes with yellow).

He spoke with The Associated Press about the challenges of starting a new airline, how the carrier is doing, and plans to sell shares to the public. The answers were edited for length and clarity.

Q: Why did you think you should start a new airline?

A: Shortly after I left Allegiant in 2014, I actually started thinking about about doing this. The market had become very consolidated, and there was a lot of opportunity that was out there that wasn’t being served by the existing, incumbent carriers. You have these four behemoths that are massive, that are protected by the government it seems, because certainly they’re stronger than they have ever been, after the pandemic. My view was we had room for more.

Q: What have you learned?

A: While there are these four behemoths, the toughest challenge, quite honestly, might even be the regulatory regime. For smaller companies like ours, it imposes these really substantial burdens on us. I’ll give you an example. For the (Department of Justice) lawsuit (against) JetBlue and Spirit, we had to go spend a ton of money on our end to produce documents for something that we really didn’t care how it ended up. And they’re trying to get us to do the same thing for Alaska-Hawaiian, which again, we could care less if Alaska and Hawaiian merge.

Q: How do you get people to fly on a new airline?

A: Number one, you have an awareness issue. You want people to know that you exist. So that’s one challenge, which is more of a marketing challenge. The other challenge is of course getting people to trust you. Like, ‘Who are these people? Are they going to really get me there? What’s the airplane going to look like? Is it safe? Is it reliable? What happens if something goes wrong?’ All those questions that most consumers may have when they think about choosing an airline that perhaps they’re unfamiliar with. You just have to focus on doing a really great job. Obviously not every flight is on time, but as time goes on I think people recognize that, hey, you know what? These guys offer a lot of value. We offer great convenience.

Q: Where does the name, Avelo, come from?

A: There’s no great story there. I wish I could tell you it was. It was a play on two words: velocity, which is swift in Latin, and convenience.

Q: Avelo reported a profit for fourth quarter 2023 but gave no details. Was that on a GAAP (generally accepted accounting principles) basis? And how much was it?

A: We actually have cash that generates interest income nowadays. Those are GAAP numbers where we have audited financials from Ernst & Young. These are real numbers with no adjustments or anything else. I’m not going to give you the numbers because we are a private company and so we have no real need to provide that kind of information. I’ll tell you that we made money in the first quarter as well.

Q: You've talked about your cost advantage as a startup. Is that sustainable?

A: Most costs creep up over time for every airline because our labor costs are tied to pay scales, but it is very sustainable. It’s based on how we designed the business. We distribute our product directly to the customer so we don’t use third-party intermediaries. We go in to smaller, more convenient, less-expensive airports. Your taxi times are lower; you’re not burning gas. We spend money on things that matter, and that includes our people. Our pilot pay is very competitive. It’s not the same as United Airlines, but it’s extremely competitive. We operate older equipment also — midlife (Boeing) 737 NGs, and those are certainly less expensive than brand-new aircraft. They burn a little bit more gas, but not much, and we like that trade.

Q: Do you plan to sell stock to the public, and when?

There’s obviously two issues. The single biggest one is one we don’t control, which is when are the IPO markets going to be actually open and vibrant, and they’re not right now. Beyond that, we have to be ready as a company. We put two straight quarters of profits ... so we expect every quarter this year to be profitable. We hope that we’ll have a company that people would want to own, and hopefully by year end or sometime next year. There’s no magic to being public for us. It’s just that historically that is typically the best way to access the capital markets for companies like ours. It is a very capital-intensive industry.

Q: What advice would you give to somebody else looking to start a business?

A: There’s nothing more rewarding than taking control of of your destiny. Just make sure you know what you think you know about whatever it is you’re going to start. I think you have to be wired a certain way to want to do something like this because it’s unbelievably difficult. I’ve been at this now for almost six years. When we get to a certain point, I’ll look back and feel really good about what we’ve done. We’re not there yet, but we’re getting close.

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I Thought the Bragg Case Against Trump Was a Legal Embarrassment. Now I Think It’s a Historic Mistake.

By Jed Handelsman Shugerman

Mr. Shugerman is a law professor at Boston University.

About a year ago, when Alvin Bragg, the Manhattan district attorney, indicted former President Donald Trump, I was critical of the case and called it an embarrassment. I thought an array of legal problems would and should lead to long delays in federal courts.

After listening to Monday’s opening statement by prosecutors, I still think the district attorney has made a historic mistake. Their vague allegation about “a criminal scheme to corrupt the 2016 presidential election” has me more concerned than ever about their unprecedented use of state law and their persistent avoidance of specifying an election crime or a valid theory of fraud.

To recap: Mr. Trump is accused in the case of falsifying business records. Those are misdemeanor charges. To elevate it to a criminal case, Mr. Bragg and his team have pointed to potential violations of federal election law and state tax fraud. They also cite state election law, but state statutory definitions of “public office” seem to limit those statutes to state and local races.

Both the misdemeanor and felony charges require that the defendant made the false record with “intent to defraud.” A year ago, I wondered how entirely internal business records (the daily ledger, pay stubs and invoices) could be the basis of any fraud if they are not shared with anyone outside the business. I suggested that the real fraud was Mr. Trump’s filing an (allegedly) false report to the Federal Election Commission, and that only federal prosecutors had jurisdiction over that filing.

A recent conversation with Jeffrey Cohen, a friend, Boston College law professor and former prosecutor, made me think that the case could turn out to be more legitimate than I had originally thought. The reason has to do with those allegedly falsified business records: Most of them were entered in early 2017, generally before Mr. Trump filed his Federal Election Commission report that summer. Mr. Trump may have foreseen an investigation into his campaign, leading to its financial records. He may have falsely recorded these internal records before the F.E.C. filing as consciously part of the same fraud: to create a consistent paper trail and to hide intent to violate federal election laws, or defraud the F.E.C.

In short: It’s not the crime; it’s the cover-up.

Looking at the case in this way might address concerns about state jurisdiction. In this scenario, Mr. Trump arguably intended to deceive state investigators, too. State investigators could find these inconsistencies and alert federal agencies. Prosecutors could argue that New York State agencies have an interest in detecting conspiracies to defraud federal entities; they might also have a plausible answer to significant questions about whether New York State has jurisdiction or whether this stretch of a state business filing law is pre-empted by federal law.

However, this explanation is a novel interpretation with many significant legal problems. And none of the Manhattan district attorney’s filings or today’s opening statement even hint at this approach.

Instead of a theory of defrauding state regulators, Mr. Bragg has adopted a weak theory of “election interference,” and Justice Juan Merchan described the case , in his summary of it during jury selection, as an allegation of falsifying business records “to conceal an agreement with others to unlawfully influence the 2016 election.”

As a reality check: It is legal for a candidate to pay for a nondisclosure agreement. Hush money is unseemly, but it is legal. The election law scholar Richard Hasen rightly observed , “Calling it election interference actually cheapens the term and undermines the deadly serious charges in the real election interference cases.”

In Monday’s opening argument, the prosecutor Matthew Colangelo still evaded specifics about what was illegal about influencing an election, but then he claimed , “It was election fraud, pure and simple.” None of the relevant state or federal statutes refer to filing violations as fraud. Calling it “election fraud” is a legal and strategic mistake, exaggerating the case and setting up the jury with high expectations that the prosecutors cannot meet.

The most accurate description of this criminal case is a federal campaign finance filing violation. Without a federal violation (which the state election statute is tethered to), Mr. Bragg cannot upgrade the misdemeanor counts into felonies. Moreover, it is unclear how this case would even fulfill the misdemeanor requirement of “intent to defraud” without the federal crime.

In stretching jurisdiction and trying a federal crime in state court, the Manhattan district attorney is now pushing untested legal interpretations and applications. I see three red flags raising concerns about selective prosecution upon appeal.

First, I could find no previous case of any state prosecutor relying on the Federal Election Campaign Act either as a direct crime or a predicate crime. Whether state prosecutors have avoided doing so as a matter of law, norms or lack of expertise, this novel attempt is a sign of overreach.

Second, Mr. Trump’s lawyers argued that the New York statute requires that the predicate (underlying) crime must also be a New York crime, not a crime in another jurisdiction. The district attorney responded with judicial precedents only about other criminal statutes, not the statute in this case. In the end, the prosecutors could not cite a single judicial interpretation of this particular statute supporting their use of the statute (a plea deal and a single jury instruction do not count).

Third, no New York precedent has allowed an interpretation of defrauding the general public. Legal experts have noted that such a broad “election interference” theory is unprecedented, and a conviction based on it may not survive a state appeal.

Mr. Trump’s legal team also undercut itself for its decisions in the past year: His lawyers essentially put all of their eggs in the meritless basket of seeking to move the trial to federal court, instead of seeking a federal injunction to stop the trial entirely. If they had raised the issues of selective or vindictive prosecution and a mix of jurisdictional, pre-emption and constitutional claims, they could have delayed the trial past Election Day, even if they lost at each federal stage.

Another reason a federal crime has wound up in state court is that President Biden’s Justice Department bent over backward not to reopen this valid case or appoint a special counsel. Mr. Trump has tried to blame Mr. Biden for this prosecution as the real “election interference.” The Biden administration’s extra restraint belies this allegation and deserves more credit.

Eight years after the alleged crime itself, it is reasonable to ask if this is more about Manhattan politics than New York law. This case should serve as a cautionary tale about broader prosecutorial abuses in America — and promote bipartisan reforms of our partisan prosecutorial system.

Nevertheless, prosecutors should have some latitude to develop their case during trial, and maybe they will be more careful and precise about the underlying crime, fraud and the jurisdictional questions. Mr. Trump has received sufficient notice of the charges, and he can raise his arguments on appeal. One important principle of “ our Federalism ,” in the Supreme Court’s terms, is abstention , that federal courts should generally allow state trials to proceed first and wait to hear challenges later.

This case is still an embarrassment, in terms of prosecutorial ethics and apparent selectivity. Nevertheless, each side should have its day in court. If convicted, Mr. Trump can fight many other days — and perhaps win — in appellate courts. But if Monday’s opening is a preview of exaggerated allegations, imprecise legal theories and persistently unaddressed problems, the prosecutors might not win a conviction at all.

Jed Handelsman Shugerman (@jedshug) is a law professor at Boston University.

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

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Survivor 50 Is Going To Feature Returning Players, And I Think It’s Obvious How The Tribes Should Be Set Up

Make it happen.

Surprise! Longtime Survivor host Jeff Probst was at an event for the beloved reality show last night when he asked the fans in attendance what they wanted to see out of the upcoming 50th season. It quickly became clear the answer was returning players; so, in a huge win, he decided to commit to that on the spot. Exactly who those players might be or how it’ll all work is unclear and likely will be for the foreseeable future, but even without those details, I think it’s very obvious how the tribes should be set up.

Survivor has slowly evolved over the years from the survival-heavy reality experiment it started as to the relentlessly strategic game of blindsides and transitory coalition building we see now. All that change, however, didn’t come incrementally. Things like the immunity idols and the addition of a third tribe served as inflection points, and there’s perhaps been no bigger inflection point than Season 41. Dubbed The New Era by Jeff Probst and the producers, the reality show has prided itself on aggressively shaking things up since Covid. This has resulted in key changes like the Beware Advantage, the Shot In The Dark and Mergatory, as well as shortening the game to 26 days from the original 39, all of which continue to this day.

Most hardcore fans have complicated feelings about The New Era. Lack of villains and over emphasis on superfans aside, they’ve mostly loved the casts and some of the individual players . They’ve also enjoyed the increased focus on strategy, the longer episodes and some of the individual twists. The shortened time on the island, the disastrous hourglass twist and the relentless meddling by producers shaking things up too many times each season, however, have been viewed much more negatively. Personally, I agree with most of those takes and am somewhere in the middle on The New Era. I like change and keeping things fresh. I’ve loved a lot of the players, but there have been too many times in which the show hasn’t trusted the players to just play the normal game.

Regardless, I think it’s pretty obvious what Season 50 should be: OG Players vs New Era Players. Give us two tribes. Populate one with all our favorite older players who originally appeared sometime before Season 41. I’ll gladly take legends like Sandra, Parv, Tony and Boston Rob, but I’m also open to some of our favorites who didn’t win their seasons. Just give us a good mix of fun players who we all want to see play the game again. And then give us a tribe of great New Era players. There are so many castaways that fans are dying to see come back. Shan, Ricard, Xander, Cody, Karla, Jesse, Maryanne, Carolyn, Yam Yam and Carson to just name a few. 

I know Survivor is down this path right now of preferring 3 tribes, but sometimes you need to let the basic premise of the season dictate the specifics. Players from The New Era clearly feel they’ve been battle tested in a way older players haven’t given all the twists and turns. Older players clearly feel New Era castaways don’t really know what’s up given they get off the island in 26 days. So, I think we let them battle it out. Give us two opposing tribes, give us some of The New Era twists and let them go the full 39 days. It would be fantastic television, and fans from every era would be so fired up. 

Survivor is currently airing Season 46 on Wednesday nights on CBS or via other streaming options . That means producers have a year or two to figure out all the specifics since all we’ve gotten is the basic commitment it’s happening . I can't wait.

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Mack Rawden is the Editor-In-Chief of CinemaBlend. He first started working at the publication as a writer back in 2007 and has held various jobs at the site in the time since including Managing Editor, Pop Culture Editor and Staff Writer. He now splits his time between working on CinemaBlend’s user experience, helping to plan the site’s editorial direction and writing passionate articles about niche entertainment topics he’s into. He graduated from Indiana University with a degree in English (go Hoosiers!) and has been interviewed and quoted in a variety of publications including Digiday. Enthusiastic about Clue, case-of-the-week mysteries, a great wrestling promo and cookies at Disney World. Less enthusiastic about the pricing structure of cable, loud noises and Tuesdays.

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