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What is a Hypothesis – Types, Examples and Writing Guide

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Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

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A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject.

In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.

In the study of logic, a hypothesis is an if-then proposition, typically written in the form, "If X , then Y ."

In common usage, a hypothesis is simply a proposed explanation or prediction, which may or may not be tested.

Writing a Hypothesis

Most scientific hypotheses are proposed in the if-then format because it's easy to design an experiment to see whether or not a cause and effect relationship exists between the independent variable and the dependent variable . The hypothesis is written as a prediction of the outcome of the experiment.

• Null Hypothesis and Alternative Hypothesis

Statistically, it's easier to show there is no relationship between two variables than to support their connection. So, scientists often propose the null hypothesis . The null hypothesis assumes changing the independent variable will have no effect on the dependent variable.

In contrast, the alternative hypothesis suggests changing the independent variable will have an effect on the dependent variable. Designing an experiment to test this hypothesis can be trickier because there are many ways to state an alternative hypothesis.

For example, consider a possible relationship between getting a good night's sleep and getting good grades. The null hypothesis might be stated: "The number of hours of sleep students get is unrelated to their grades" or "There is no correlation between hours of sleep and grades."

An experiment to test this hypothesis might involve collecting data, recording average hours of sleep for each student and grades. If a student who gets eight hours of sleep generally does better than students who get four hours of sleep or 10 hours of sleep, the hypothesis might be rejected.

But the alternative hypothesis is harder to propose and test. The most general statement would be: "The amount of sleep students get affects their grades." The hypothesis might also be stated as "If you get more sleep, your grades will improve" or "Students who get nine hours of sleep have better grades than those who get more or less sleep."

In an experiment, you can collect the same data, but the statistical analysis is less likely to give you a high confidence limit.

Usually, a scientist starts out with the null hypothesis. From there, it may be possible to propose and test an alternative hypothesis, to narrow down the relationship between the variables.

Example of a Hypothesis

Examples of a hypothesis include:

• If you drop a rock and a feather, (then) they will fall at the same rate.
• Plants need sunlight in order to live. (if sunlight, then life)
• Eating sugar gives you energy. (if sugar, then energy)
• White, Jay D.  Research in Public Administration . Conn., 1998.
• Schick, Theodore, and Lewis Vaughn.  How to Think about Weird Things: Critical Thinking for a New Age . McGraw-Hill Higher Education, 2002.
• Null Hypothesis Definition and Examples
• Definition of a Hypothesis
• What Are the Elements of a Good Hypothesis?
• Six Steps of the Scientific Method
• Independent Variable Definition and Examples
• What Are Examples of a Hypothesis?
• Understanding Simple vs Controlled Experiments
• Scientific Method Flow Chart
• Scientific Method Vocabulary Terms
• What Is a Testable Hypothesis?
• Null Hypothesis Examples
• What 'Fail to Reject' Means in a Hypothesis Test
• How To Design a Science Fair Experiment
• What Is an Experiment? Definition and Design
• Hypothesis Test for the Difference of Two Population Proportions

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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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McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 22 April 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

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What is a scientific hypothesis?

It's the initial building block in the scientific method.

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

• If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
• If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
• If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

• Read more about writing a hypothesis, from the American Medical Writers Association.
• Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
• Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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Module 1: Introduction to Biology

Experiments and hypotheses, learning outcomes.

• Form a hypothesis and use it to design a scientific experiment

Now we’ll focus on the methods of scientific inquiry. Science often involves making observations and developing hypotheses. Experiments and further observations are often used to test the hypotheses.

A scientific experiment is a carefully organized procedure in which the scientist intervenes in a system to change something, then observes the result of the change. Scientific inquiry often involves doing experiments, though not always. For example, a scientist studying the mating behaviors of ladybugs might begin with detailed observations of ladybugs mating in their natural habitats. While this research may not be experimental, it is scientific: it involves careful and verifiable observation of the natural world. The same scientist might then treat some of the ladybugs with a hormone hypothesized to trigger mating and observe whether these ladybugs mated sooner or more often than untreated ones. This would qualify as an experiment because the scientist is now making a change in the system and observing the effects.

Forming a Hypothesis

When conducting scientific experiments, researchers develop hypotheses to guide experimental design. A hypothesis is a suggested explanation that is both testable and falsifiable. You must be able to test your hypothesis, and it must be possible to prove your hypothesis true or false.

For example, Michael observes that maple trees lose their leaves in the fall. He might then propose a possible explanation for this observation: “cold weather causes maple trees to lose their leaves in the fall.” This statement is testable. He could grow maple trees in a warm enclosed environment such as a greenhouse and see if their leaves still dropped in the fall. The hypothesis is also falsifiable. If the leaves still dropped in the warm environment, then clearly temperature was not the main factor in causing maple leaves to drop in autumn.

In the Try It below, you can practice recognizing scientific hypotheses. As you consider each statement, try to think as a scientist would: can I test this hypothesis with observations or experiments? Is the statement falsifiable? If the answer to either of these questions is “no,” the statement is not a valid scientific hypothesis.

Practice Questions

Determine whether each following statement is a scientific hypothesis.

Air pollution from automobile exhaust can trigger symptoms in people with asthma.

• No. This statement is not testable or falsifiable.
• No. This statement is not testable.
• No. This statement is not falsifiable.
• Yes. This statement is testable and falsifiable.

Natural disasters, such as tornadoes, are punishments for bad thoughts and behaviors.

a: No. This statement is not testable or falsifiable. “Bad thoughts and behaviors” are excessively vague and subjective variables that would be impossible to measure or agree upon in a reliable way. The statement might be “falsifiable” if you came up with a counterexample: a “wicked” place that was not punished by a natural disaster. But some would question whether the people in that place were really wicked, and others would continue to predict that a natural disaster was bound to strike that place at some point. There is no reason to suspect that people’s immoral behavior affects the weather unless you bring up the intervention of a supernatural being, making this idea even harder to test.

Testing a Vaccine

Let’s examine the scientific process by discussing an actual scientific experiment conducted by researchers at the University of Washington. These researchers investigated whether a vaccine may reduce the incidence of the human papillomavirus (HPV). The experimental process and results were published in an article titled, “ A controlled trial of a human papillomavirus type 16 vaccine .”

Preliminary observations made by the researchers who conducted the HPV experiment are listed below:

• Human papillomavirus (HPV) is the most common sexually transmitted virus in the United States.
• There are about 40 different types of HPV. A significant number of people that have HPV are unaware of it because many of these viruses cause no symptoms.
• Some types of HPV can cause cervical cancer.
• About 4,000 women a year die of cervical cancer in the United States.

Practice Question

Researchers have developed a potential vaccine against HPV and want to test it. What is the first testable hypothesis that the researchers should study?

• HPV causes cervical cancer.
• People should not have unprotected sex with many partners.
• People who get the vaccine will not get HPV.
• The HPV vaccine will protect people against cancer.

Experimental Design

You’ve successfully identified a hypothesis for the University of Washington’s study on HPV: People who get the HPV vaccine will not get HPV.

The next step is to design an experiment that will test this hypothesis. There are several important factors to consider when designing a scientific experiment. First, scientific experiments must have an experimental group. This is the group that receives the experimental treatment necessary to address the hypothesis.

The experimental group receives the vaccine, but how can we know if the vaccine made a difference? Many things may change HPV infection rates in a group of people over time. To clearly show that the vaccine was effective in helping the experimental group, we need to include in our study an otherwise similar control group that does not get the treatment. We can then compare the two groups and determine if the vaccine made a difference. The control group shows us what happens in the absence of the factor under study.

However, the control group cannot get “nothing.” Instead, the control group often receives a placebo. A placebo is a procedure that has no expected therapeutic effect—such as giving a person a sugar pill or a shot containing only plain saline solution with no drug. Scientific studies have shown that the “placebo effect” can alter experimental results because when individuals are told that they are or are not being treated, this knowledge can alter their actions or their emotions, which can then alter the results of the experiment.

Moreover, if the doctor knows which group a patient is in, this can also influence the results of the experiment. Without saying so directly, the doctor may show—through body language or other subtle cues—their views about whether the patient is likely to get well. These errors can then alter the patient’s experience and change the results of the experiment. Therefore, many clinical studies are “double blind.” In these studies, neither the doctor nor the patient knows which group the patient is in until all experimental results have been collected.

Both placebo treatments and double-blind procedures are designed to prevent bias. Bias is any systematic error that makes a particular experimental outcome more or less likely. Errors can happen in any experiment: people make mistakes in measurement, instruments fail, computer glitches can alter data. But most such errors are random and don’t favor one outcome over another. Patients’ belief in a treatment can make it more likely to appear to “work.” Placebos and double-blind procedures are used to level the playing field so that both groups of study subjects are treated equally and share similar beliefs about their treatment.

The scientists who are researching the effectiveness of the HPV vaccine will test their hypothesis by separating 2,392 young women into two groups: the control group and the experimental group. Answer the following questions about these two groups.

• This group is given a placebo.
• This group is deliberately infected with HPV.
• This group is given nothing.
• This group is given the HPV vaccine.
• a: This group is given a placebo. A placebo will be a shot, just like the HPV vaccine, but it will have no active ingredient. It may change peoples’ thinking or behavior to have such a shot given to them, but it will not stimulate the immune systems of the subjects in the same way as predicted for the vaccine itself.
• d: This group is given the HPV vaccine. The experimental group will receive the HPV vaccine and researchers will then be able to see if it works, when compared to the control group.

Experimental Variables

A variable is a characteristic of a subject (in this case, of a person in the study) that can vary over time or among individuals. Sometimes a variable takes the form of a category, such as male or female; often a variable can be measured precisely, such as body height. Ideally, only one variable is different between the control group and the experimental group in a scientific experiment. Otherwise, the researchers will not be able to determine which variable caused any differences seen in the results. For example, imagine that the people in the control group were, on average, much more sexually active than the people in the experimental group. If, at the end of the experiment, the control group had a higher rate of HPV infection, could you confidently determine why? Maybe the experimental subjects were protected by the vaccine, but maybe they were protected by their low level of sexual contact.

To avoid this situation, experimenters make sure that their subject groups are as similar as possible in all variables except for the variable that is being tested in the experiment. This variable, or factor, will be deliberately changed in the experimental group. The one variable that is different between the two groups is called the independent variable. An independent variable is known or hypothesized to cause some outcome. Imagine an educational researcher investigating the effectiveness of a new teaching strategy in a classroom. The experimental group receives the new teaching strategy, while the control group receives the traditional strategy. It is the teaching strategy that is the independent variable in this scenario. In an experiment, the independent variable is the variable that the scientist deliberately changes or imposes on the subjects.

Dependent variables are known or hypothesized consequences; they are the effects that result from changes or differences in an independent variable. In an experiment, the dependent variables are those that the scientist measures before, during, and particularly at the end of the experiment to see if they have changed as expected. The dependent variable must be stated so that it is clear how it will be observed or measured. Rather than comparing “learning” among students (which is a vague and difficult to measure concept), an educational researcher might choose to compare test scores, which are very specific and easy to measure.

In any real-world example, many, many variables MIGHT affect the outcome of an experiment, yet only one or a few independent variables can be tested. Other variables must be kept as similar as possible between the study groups and are called control variables . For our educational research example, if the control group consisted only of people between the ages of 18 and 20 and the experimental group contained people between the ages of 30 and 35, we would not know if it was the teaching strategy or the students’ ages that played a larger role in the results. To avoid this problem, a good study will be set up so that each group contains students with a similar age profile. In a well-designed educational research study, student age will be a controlled variable, along with other possibly important factors like gender, past educational achievement, and pre-existing knowledge of the subject area.

What is the independent variable in this experiment?

• Sex (all of the subjects will be female)
• Presence or absence of the HPV vaccine
• Presence or absence of HPV (the virus)

List three control variables other than age.

What is the dependent variable in this experiment?

• Sex (male or female)
• Rates of HPV infection
• Age (years)
• Revision and adaptation. Authored by : Shelli Carter and Lumen Learning. Provided by : Lumen Learning. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Scientific Inquiry. Provided by : Open Learning Initiative. Located at : https://oli.cmu.edu/jcourse/workbook/activity/page?context=434a5c2680020ca6017c03488572e0f8 . Project : Introduction to Biology (Open + Free). License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Hypothesis Examples

A hypothesis is a prediction of the outcome of a test. It forms the basis for designing an experiment in the scientific method . A good hypothesis is testable, meaning it makes a prediction you can check with observation or experimentation. Here are different hypothesis examples.

Null Hypothesis Examples

The null hypothesis (H 0 ) is also known as the zero-difference or no-difference hypothesis. It predicts that changing one variable ( independent variable ) will have no effect on the variable being measured ( dependent variable ). Here are null hypothesis examples:

• Plant growth is unaffected by temperature.
• If you increase temperature, then solubility of salt will increase.
• Incidence of skin cancer is unrelated to ultraviolet light exposure.
• All brands of light bulb last equally long.
• Cats have no preference for the color of cat food.
• All daisies have the same number of petals.

Sometimes the null hypothesis shows there is a suspected correlation between two variables. For example, if you think plant growth is affected by temperature, you state the null hypothesis: “Plant growth is not affected by temperature.” Why do you do this, rather than say “If you change temperature, plant growth will be affected”? The answer is because it’s easier applying a statistical test that shows, with a high level of confidence, a null hypothesis is correct or incorrect.

Research Hypothesis Examples

A research hypothesis (H 1 ) is a type of hypothesis used to design an experiment. This type of hypothesis is often written as an if-then statement because it’s easy identifying the independent and dependent variables and seeing how one affects the other. If-then statements explore cause and effect. In other cases, the hypothesis shows a correlation between two variables. Here are some research hypothesis examples:

• If you leave the lights on, then it takes longer for people to fall asleep.
• If you refrigerate apples, they last longer before going bad.
• If you keep the curtains closed, then you need less electricity to heat or cool the house (the electric bill is lower).
• If you leave a bucket of water uncovered, then it evaporates more quickly.
• Goldfish lose their color if they are not exposed to light.
• Workers who take vacations are more productive than those who never take time off.

Is It Okay to Disprove a Hypothesis?

Yes! You may even choose to write your hypothesis in such a way that it can be disproved because it’s easier to prove a statement is wrong than to prove it is right. In other cases, if your prediction is incorrect, that doesn’t mean the science is bad. Revising a hypothesis is common. It demonstrates you learned something you did not know before you conducted the experiment.

Test yourself with a Scientific Method Quiz .

• Mellenbergh, G.J. (2008). Chapter 8: Research designs: Testing of research hypotheses. In H.J. Adèr & G.J. Mellenbergh (eds.), Advising on Research Methods: A Consultant’s Companion . Huizen, The Netherlands: Johannes van Kessel Publishing.
• Popper, Karl R. (1959). The Logic of Scientific Discovery . Hutchinson & Co. ISBN 3-1614-8410-X.
• Schick, Theodore; Vaughn, Lewis (2002). How to think about weird things: critical thinking for a New Age . Boston: McGraw-Hill Higher Education. ISBN 0-7674-2048-9.
• Tobi, Hilde; Kampen, Jarl K. (2018). “Research design: the methodology for interdisciplinary research framework”. Quality & Quantity . 52 (3): 1209–1225. doi: 10.1007/s11135-017-0513-8

Related Posts

1.3: The Scientific Method

Chapter 1: scientific inquiry, chapter 2: chemistry of life, chapter 3: macromolecules, chapter 4: cell structure and function, chapter 5: membranes and cellular transport, chapter 6: cell signaling, chapter 7: metabolism, chapter 8: cellular respiration, chapter 9: photosynthesis, chapter 10: cell cycle and division, chapter 11: meiosis, chapter 12: classical and modern genetics, chapter 13: dna structure and function, chapter 14: gene expression, chapter 15: biotechnology, chapter 16: viruses, chapter 17: nutrition and digestion, chapter 18: nervous system, chapter 19: sensory systems, chapter 20: musculoskeletal system, chapter 21: endocrine system, chapter 22: circulatory and pulmonary systems, chapter 23: osmoregulation and excretion, chapter 24: immune system, chapter 25: reproduction and development, chapter 26: behavior, chapter 27: ecosystems, chapter 28: population and community ecology, chapter 29: biodiversity and conservation, chapter 30: speciation and diversity, chapter 31: natural selection, chapter 32: population genetics, chapter 33: evolutionary history, chapter 34: plant structure, growth, and nutrition, chapter 35: plant reproduction, chapter 36: plant responses to the environment.

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The scientific method is a detailed, stepwise process for answering questions. For example, a scientist makes an observation that the slugs destroy some cabbages but not those near garlic.

Such observations lead to asking questions, "Could garlic be used to deter slugs from ruining a cabbage patch?" After formulating questions, the scientist can then develop hypotheses —potential explanations for the observations that lead to specific, testable predictions.

In this case, a hypothesis could be that garlic repels slugs, which predicts that cabbages surrounded by garlic powder will suffer less damage than the ones without it. 

The hypothesis is then tested through a series of experiments designed to eliminate hypotheses.

The experimental setup involves defining variables. An independent variable is an item that is being tested, in this case, garlic addition. The dependent variable describes the measurement used to determine the outcome, such as the number of slugs on the cabbages.

In addition, the slugs must be divided into groups, experimental and control. These groups are identical, except that the experimental group is exposed to garlic powder.

After data are collected and analyzed, conclusions are made, and results are communicated to other scientists.

The scientific method is a detailed, empirical problem-solving process used by biologists and other scientists. This iterative approach involves formulating a question based on observation, developing a testable potential explanation for the observation (called a hypothesis), making and testing predictions based on the hypothesis, and using the findings to create new hypotheses and predictions.

Generally, predictions are tested using carefully-designed experiments. Based on the outcome of these experiments, the original hypothesis may need to be refined, and new hypotheses and questions can be generated. Importantly, this illustrates that the scientific method is not a stepwise recipe. Instead, it is a continuous refinement and testing of ideas based on new observations, which is the crux of scientific inquiry.

Science is mutable and continuously changes as scientists learn more about the world, physical phenomena and how organisms interact with their environment. For this reason, scientists avoid claiming to ‘prove' a specific idea. Instead, they gather evidence that either supports or refutes a given hypothesis.

Making Observations and Formulating Hypotheses

A hypothesis is preceded by an initial observation, during which information is gathered by the senses (e.g., vision, hearing) or using scientific tools and instruments. This observation leads to a question that prompts the formation of an initial hypothesis, a (testable) possible answer to the question. For example, the observation that slugs eat some cabbage plants but not cabbage plants located near garlic may prompt the question: why do slugs selectively not eat cabbage plants near garlic? One possible hypothesis, or answer to this question, is that slugs have an aversion to garlic. Based on this hypothesis, one might predict that slugs will not eat cabbage plants surrounded by a ring of garlic powder.

A hypothesis should be falsifiable, meaning that there are ways to disprove it if it is untrue. In other words, a hypothesis should be testable. Scientists often articulate and explicitly test for the opposite of the hypothesis, which is called the null hypothesis. In this case, the null hypothesis is that slugs do not have an aversion to garlic. The null hypothesis would be supported if, contrary to the prediction, slugs eat cabbage plants that are surrounded by garlic powder.

Testing a Hypothesis

When possible, scientists test hypotheses using controlled experiments that include independent and dependent variables, as well as control and experimental groups.

An independent variable is an item expected to have an effect (e.g., the garlic powder used in the slug and cabbage experiment or treatment given in a clinical trial). Dependent variables are the measurements used to determine the outcome of an experiment. In the experiment with slugs, cabbages, and garlic, the number of slugs eating cabbages is the dependent variable. This number is expected to depend on the presence or absence of garlic powder rings around the cabbage plants.

Experiments require experimental and control groups. An experimental group is treated with or exposed to the independent variable (i.e., the manipulation or treatment). For example, in the garlic aversion experiment with slugs, the experimental group is a group of cabbage plants surrounded by a garlic powder ring. A control group is subject to the same conditions as the experimental group, with the exception of the independent variable. Control groups in this experiment might include a group of cabbage plants in the same area that is surrounded by a non-garlic powder ring (to control for powder aversion) and a group that is not surrounded by any particular substance (to control for cabbage aversion). It is essential to include a control group because, without one, it is unclear whether the outcome is the result of the treatment or manipulation.

Refining a Hypothesis

If the results of an experiment support the hypothesis, further experiments may be designed and carried out to provide support for the hypothesis. The hypothesis may also be refined and made more specific. For example, additional experiments could determine whether slugs also have an aversion to other plants of the Allium genus, like onions.

If the results do not support the hypothesis, then the original hypothesis may be modified based on the new observations. It is important to rule out potential problems with the experimental design before modifying the hypothesis. For example, if slugs demonstrate an aversion to both garlic and non-garlic powder, the experiment can be carried out again using fresh garlic instead of powdered garlic. If the slugs still exhibit no aversion to garlic, then the original hypothesis can be modified.

Communication

The results of the experiments should be communicated to other scientists and the public, regardless of whether the data support the original hypothesis. This information can guide the development of new hypotheses and experimental questions.

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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."

Choose Your Test

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

General Education

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

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

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

So let’s get started!

What Is a Hypothesis?

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

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

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

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

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

Independent and Dependent Variables 

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

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

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

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

Elements of a Good Hypothesis

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

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

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

Writing Your Hypothesis

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

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

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

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

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

The Two Types of Hypotheses

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

#1: If-Then Hypotheses

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

If ____ happens, then ____ will happen.

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

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

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

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

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

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

#2: Null Hypotheses

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

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

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

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

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

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

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

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

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

4 Tips to Write the Best Hypothesis

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

#1: Plausibility

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

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

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

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

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

#2: Defined Concepts

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

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

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

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

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

Good hypotheses ensure that you can observe the results. 

#3: Observability

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

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

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

#4: Generalizability 

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

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

Hypothesis Testing Examples

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

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

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

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

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

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

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

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

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

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

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

Here’s what your hypotheses might look like: 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Key Takeaways: Hypothesis Writing

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

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

What’s Next?

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

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

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

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

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4.14: Experiments and Hypotheses

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Now we’ll focus on the methods of scientific inquiry. Science often involves making observations and developing hypotheses. Experiments and further observations are often used to test the hypotheses.

A scientific experiment is a carefully organized procedure in which the scientist intervenes in a system to change something, then observes the result of the change. Scientific inquiry often involves doing experiments, though not always. For example, a scientist studying the mating behaviors of ladybugs might begin with detailed observations of ladybugs mating in their natural habitats. While this research may not be experimental, it is scientific: it involves careful and verifiable observation of the natural world. The same scientist might then treat some of the ladybugs with a hormone hypothesized to trigger mating and observe whether these ladybugs mated sooner or more often than untreated ones. This would qualify as an experiment because the scientist is now making a change in the system and observing the effects.

Forming a Hypothesis

When conducting scientific experiments, researchers develop hypotheses to guide experimental design. A hypothesis is a suggested explanation that is both testable and falsifiable. You must be able to test your hypothesis, and it must be possible to prove your hypothesis true or false.

For example, Michael observes that maple trees lose their leaves in the fall. He might then propose a possible explanation for this observation: “cold weather causes maple trees to lose their leaves in the fall.” This statement is testable. He could grow maple trees in a warm enclosed environment such as a greenhouse and see if their leaves still dropped in the fall. The hypothesis is also falsifiable. If the leaves still dropped in the warm environment, then clearly temperature was not the main factor in causing maple leaves to drop in autumn.

In the Try It below, you can practice recognizing scientific hypotheses. As you consider each statement, try to think as a scientist would: can I test this hypothesis with observations or experiments? Is the statement falsifiable? If the answer to either of these questions is “no,” the statement is not a valid scientific hypothesis.

Practice Questions

Determine whether each following statement is a scientific hypothesis.

• No. This statement is not testable or falsifiable.
• No. This statement is not testable.
• No. This statement is not falsifiable.
• Yes. This statement is testable and falsifiable.

[reveal-answer q=”429550″] Show Answers [/reveal-answer] [hidden-answer a=”429550″]

• d: Yes. This statement is testable and falsifiable. This could be tested with a number of different kinds of observations and experiments, and it is possible to gather evidence that indicates that air pollution is not linked with asthma.
• a: No. This statement is not testable or falsifiable. “Bad thoughts and behaviors” are excessively vague and subjective variables that would be impossible to measure or agree upon in a reliable way. The statement might be “falsifiable” if you came up with a counterexample: a “wicked” place that was not punished by a natural disaster. But some would question whether the people in that place were really wicked, and others would continue to predict that a natural disaster was bound to strike that place at some point. There is no reason to suspect that people’s immoral behavior affects the weather unless you bring up the intervention of a supernatural being, making this idea even harder to test.

[/hidden-answer]

Testing a Vaccine

Let’s examine the scientific process by discussing an actual scientific experiment conducted by researchers at the University of Washington. These researchers investigated whether a vaccine may reduce the incidence of the human papillomavirus (HPV). The experimental process and results were published in an article titled, “ A controlled trial of a human papillomavirus type 16 vaccine .”

Preliminary observations made by the researchers who conducted the HPV experiment are listed below:

• Human papillomavirus (HPV) is the most common sexually transmitted virus in the United States.
• There are about 40 different types of HPV. A significant number of people that have HPV are unaware of it because many of these viruses cause no symptoms.
• Some types of HPV can cause cervical cancer.
• About 4,000 women a year die of cervical cancer in the United States.

Practice Question

Researchers have developed a potential vaccine against HPV and want to test it. What is the first testable hypothesis that the researchers should study?

• HPV causes cervical cancer.
• People should not have unprotected sex with many partners.
• People who get the vaccine will not get HPV.
• The HPV vaccine will protect people against cancer.

[reveal-answer q=”20917″] Show Answer [/reveal-answer] [hidden-answer a=”20917″]Hypothesis A is not the best choice because this information is already known from previous studies. Hypothesis B is not testable because scientific hypotheses are not value statements; they do not include judgments like “should,” “better than,” etc. Scientific evidence certainly might support this value judgment, but a hypothesis would take a different form: “Having unprotected sex with many partners increases a person’s risk for cervical cancer.” Before the researchers can test if the vaccine protects against cancer (hypothesis D), they want to test if it protects against the virus. This statement will make an excellent hypothesis for the next study. The researchers should first test hypothesis C—whether or not the new vaccine can prevent HPV.[/hidden-answer]

Experimental Design

You’ve successfully identified a hypothesis for the University of Washington’s study on HPV: People who get the HPV vaccine will not get HPV.

The next step is to design an experiment that will test this hypothesis. There are several important factors to consider when designing a scientific experiment. First, scientific experiments must have an experimental group. This is the group that receives the experimental treatment necessary to address the hypothesis.

The experimental group receives the vaccine, but how can we know if the vaccine made a difference? Many things may change HPV infection rates in a group of people over time. To clearly show that the vaccine was effective in helping the experimental group, we need to include in our study an otherwise similar control group that does not get the treatment. We can then compare the two groups and determine if the vaccine made a difference. The control group shows us what happens in the absence of the factor under study.

However, the control group cannot get “nothing.” Instead, the control group often receives a placebo. A placebo is a procedure that has no expected therapeutic effect—such as giving a person a sugar pill or a shot containing only plain saline solution with no drug. Scientific studies have shown that the “placebo effect” can alter experimental results because when individuals are told that they are or are not being treated, this knowledge can alter their actions or their emotions, which can then alter the results of the experiment.

Moreover, if the doctor knows which group a patient is in, this can also influence the results of the experiment. Without saying so directly, the doctor may show—through body language or other subtle cues—his or her views about whether the patient is likely to get well. These errors can then alter the patient’s experience and change the results of the experiment. Therefore, many clinical studies are “double blind.” In these studies, neither the doctor nor the patient knows which group the patient is in until all experimental results have been collected.

Both placebo treatments and double-blind procedures are designed to prevent bias. Bias is any systematic error that makes a particular experimental outcome more or less likely. Errors can happen in any experiment: people make mistakes in measurement, instruments fail, computer glitches can alter data. But most such errors are random and don’t favor one outcome over another. Patients’ belief in a treatment can make it more likely to appear to “work.” Placebos and double-blind procedures are used to level the playing field so that both groups of study subjects are treated equally and share similar beliefs about their treatment.

The scientists who are researching the effectiveness of the HPV vaccine will test their hypothesis by separating 2,392 young women into two groups: the control group and the experimental group. Answer the following questions about these two groups.

• This group is given a placebo.
• This group is deliberately infected with HPV.
• This group is given nothing.
• This group is given the HPV vaccine.

[reveal-answer q=”918962″] Show Answers [/reveal-answer] [hidden-answer a=”918962″]

• a: This group is given a placebo. A placebo will be a shot, just like the HPV vaccine, but it will have no active ingredient. It may change peoples’ thinking or behavior to have such a shot given to them, but it will not stimulate the immune systems of the subjects in the same way as predicted for the vaccine itself.
• d: This group is given the HPV vaccine. The experimental group will receive the HPV vaccine and researchers will then be able to see if it works, when compared to the control group.

Experimental Variables

A variable is a characteristic of a subject (in this case, of a person in the study) that can vary over time or among individuals. Sometimes a variable takes the form of a category, such as male or female; often a variable can be measured precisely, such as body height. Ideally, only one variable is different between the control group and the experimental group in a scientific experiment. Otherwise, the researchers will not be able to determine which variable caused any differences seen in the results. For example, imagine that the people in the control group were, on average, much more sexually active than the people in the experimental group. If, at the end of the experiment, the control group had a higher rate of HPV infection, could you confidently determine why? Maybe the experimental subjects were protected by the vaccine, but maybe they were protected by their low level of sexual contact.

To avoid this situation, experimenters make sure that their subject groups are as similar as possible in all variables except for the variable that is being tested in the experiment. This variable, or factor, will be deliberately changed in the experimental group. The one variable that is different between the two groups is called the independent variable. An independent variable is known or hypothesized to cause some outcome. Imagine an educational researcher investigating the effectiveness of a new teaching strategy in a classroom. The experimental group receives the new teaching strategy, while the control group receives the traditional strategy. It is the teaching strategy that is the independent variable in this scenario. In an experiment, the independent variable is the variable that the scientist deliberately changes or imposes on the subjects.

Dependent variables are known or hypothesized consequences; they are the effects that result from changes or differences in an independent variable. In an experiment, the dependent variables are those that the scientist measures before, during, and particularly at the end of the experiment to see if they have changed as expected. The dependent variable must be stated so that it is clear how it will be observed or measured. Rather than comparing “learning” among students (which is a vague and difficult to measure concept), an educational researcher might choose to compare test scores, which are very specific and easy to measure.

In any real-world example, many, many variables MIGHT affect the outcome of an experiment, yet only one or a few independent variables can be tested. Other variables must be kept as similar as possible between the study groups and are called control variables . For our educational research example, if the control group consisted only of people between the ages of 18 and 20 and the experimental group contained people between the ages of 30 and 35, we would not know if it was the teaching strategy or the students’ ages that played a larger role in the results. To avoid this problem, a good study will be set up so that each group contains students with a similar age profile. In a well-designed educational research study, student age will be a controlled variable, along with other possibly important factors like gender, past educational achievement, and pre-existing knowledge of the subject area.

What is the independent variable in this experiment?

• Sex (all of the subjects will be female)
• Presence or absence of the HPV vaccine
• Presence or absence of HPV (the virus)

[reveal-answer q=”68680″]Show Answer[/reveal-answer] [hidden-answer a=”68680″]Answer b. Presence or absence of the HPV vaccine. This is the variable that is different between the control and the experimental groups. All the subjects in this study are female, so this variable is the same in all groups. In a well-designed study, the two groups will be of similar age. The presence or absence of the virus is what the researchers will measure at the end of the experiment. Ideally the two groups will both be HPV-free at the start of the experiment.

List three control variables other than age.

[practice-area rows=”3″][/practice-area] [reveal-answer q=”903121″]Show Answer[/reveal-answer] [hidden-answer a=”903121″]Some possible control variables would be: general health of the women, sexual activity, lifestyle, diet, socioeconomic status, etc.

What is the dependent variable in this experiment?

• Sex (male or female)
• Rates of HPV infection
• Age (years)

[reveal-answer q=”907103″]Show Answer[/reveal-answer] [hidden-answer a=”907103″]Answer b. Rates of HPV infection. The researchers will measure how many individuals got infected with HPV after a given period of time.[/hidden-answer]

Hypothesis and Experimental Design

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Two important elements of The Scientific Method that will help you design your research approach more efficiently are “Generating Hypotheses” and “Designing Controlled Experiments” to test these hypotheses. A well-designed experiment that you deeply understand will save time and resources and facilitate easier data analysis/interpretation. Many people reading this may be working on a project that focuses on designing a product, or discovery research where the hypothesis it is not immediately obvious. We encourage you to read on however as the exercise of generating a hypothesis will likely help you think about the assumptions you are making in your research and the physical principles your work builds upon.     

These activities will help you …  

• Begin formulating an appropriate hypothesis related to your research.  
• Apply a systematic process for designing experiments.  

What is a Hypothesis?  

A hypothesis is an “educated guess/prediction”  or  “ proposed explanation ”  of how a system will behave based on the available evidence .  A hypothesis is a starting point for further investigation and testing   because a hypothesis makes a prediction about the behavior of a measurable outcome of an experiment.  A hypothesis should be:  

• Testable – you can design an experiment to test it  
• Falsifiable – it can be proven wrong (note it cannot be “proved”)  
• Useful – the outcome must give valuable information  

A useful hypothesis may relate to the underlying question of your research. For example:  

“We hypothesize that therapy resistant cell populations will be enriched in hypoxic microenvironments. “  

“We hypothesize that  increasing the number of boreholes simulated in 3D geological models minimizes the variation of the geological model results.”    

Some research projects do not have an obvious hypothesis to test, but the design strategy/concept chosen is based on an underlying assumption about how the system being designed works (i.e. the hypothesis). For example:  

“We hypothesize that decreasing the baking temperature of the photoresist layer will reduce thermal expansion and device cracking”   

In this case the researcher is troubleshooting poor device quality and is proposing to vary different fabrication parameters (one being baking temperature). Understanding the assumptions (working hypotheses) of why different variables might improve device quality is useful as it provides a basis to prioritize what variables to focus on first. The core goal of this research is not to test a specific hypothesis, but using the scientific method to troubleshoot a design challenge will enable the researcher to understand the parameters that control the behavior of different designs and to identify a design that is successful more efficiently.  

In all the examples above, the hypothesis helps to guide the design of a useful and interpretable experiment with appropriate controls that rule out alternative explanations of the experimental observation. Hypotheses are therefore likely essential and useful parts of all research projects.  

Suggested Activity – Create a Hypothesis for Your Research  

Estimated time: 30 mins    

• Write down the parameters you are varying or testing in your experimental system or model and how you think the behaviour of the system is going to vary with these parameters.   
• (Alternative) If your project goal is to design a device, write down the parameters you believe control whether the device will work.  
• (Alternative) If your project goal involves optimizing a process,  write down the underlying physics or chemistry controlling the process you are studying.  
• With these parameters in mind, write down the key assumption(s) you are making about how your system works. 
• Try to formulate each one of these assumptions into a hypothesis that might be useful for your research project.  If you have multiple aims each one may have a separate hypothesis. Make sure the hypothesis meets each of the three key elements above.
• Share your hypothesis with a peer or your supervisor to discuss if this is a good hypothesis – is it testable? Does it make a useful prediction? Does it capture the key underlying assumptions your research is based upon?  

Remember that writing a good research hypothesis is challenging and will take a lot of careful thought about the underlying science that governs your system.  

Designing Experiments  

Designing experiments appropriately is very important to avoid wasting resources (time!) and to ensure results can be interpreted correctly. It is often very useful to discuss the design of your planned experiments in your meetings with your supervisor to get feedback before you start doing experiments. This will also ensure you and your supervisor have a consistent understanding of experimental design and that all the appropriate controls required to interpret your data have been considered.  

The factors that must be considered when you design experiments is going to depend on your specific area of research. S ome important things to think about when designing experiments include:  

Rationale:  What is the purpose of this experiment? Is this the best experiment I can do?  Does my experiment answer  any question ?  Does this experiment help answer  the question  I am trying to ask?  What hypothesis am I trying to test?  

Will my experiment be interpretable?   What controls can I use to distinguish my results from other potential explanations? Can I add a control to distinguish between explanations? Can I add a control to further test my hypothesis?  

Is my experiment/model rigorous?   What is the  sensitivity of the method  I am using and can it measure accurately what I want to measure? What  outcomes  (metrics) will I measure and is this measurement appropriate?  How many  replicates  (technical replicates versus independent replicates) will I do?  Am I only changing the  variable  that I am testing? What am I keeping constant? What  statistical tests  do I plan to carry out and what considerations are needed? Is my statistical design appropriate (power analysis, sufficient replicates)?  

What logistics do I need to consider?  Are the equipment/resources I need available? Do I need additional training or equipment access? Are there important safety or ethical issues/permits to consider? Are pilot experiments needed to assess feasibility and what would these be? What is my planned experimental protocol and are there important timing issues to consider? What experimental outputs and parameters need to be documented throughout experiment?  

This list is not exhaustive and you should consider what is missing for your particular situation.  

Suggested Activity – Design an Experiment Using a Template  

Estimated time: 45 min  

• Explore the excel template for experimental design ( Resource 1 )   or modelling  ( Resource 2 ).  A template like this is very useful for keeping track of protocols as well as improving the reproducibility of your experiments. Note this template is simply a  starting point  to get you thinking systematically and should be adapted to best suit  your  needs.   
• Fill out the template for an experiment or modelling project you are planning to complete soon.  
• Consider how you can modify this template to be more applicable to your specific project.  
• Using the template document, explain your experimental design/model design to a peer or your supervisor. Let them ask questions to understand your design and provide feedback. Alternatively, if there is a part of your design that you are unclear about this is a great starting point for a targeted and efficient discussion with your supervisor.   
• Revise your design based on feedback.  

Activity Examples

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Three Famous Hypotheses and How They Were Tested

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Key Takeaways

• Ivan Pavlov's experiment demonstrated conditioned responses in dogs.
• Pavlov's work exemplifies the scientific method, starting with a hypothesis about conditioned responses and testing it through controlled experiments.
• Pavlov's findings not only advanced an understanding of animal physiology but also laid foundational principles for behaviorism, a major school of thought in psychology that emphasizes the study of observable behaviors.

Coho salmon ( Oncorhynchus kisutch ) are amazing fish. Indigenous to the Pacific Northwest, they begin their lives in freshwater streams and then relocate to the open ocean. But when a Coho salmon reaches breeding age, it'll return to the waterway of its birth , sometimes traveling 400 miles (644 kilometers) to get there.

Enter the late Arthur Davis Hasler. While an ecologist and biologist at the University of Wisconsin, he was intrigued by the question of how these creatures find their home streams. And in 1960, he used a Hypothesis-Presentation.pdf">basic tenet of science — the hypothesis — to find out.

So what is a hypothesis? A hypothesis is a tentative, testable explanation for an observed phenomenon in nature. Hypotheses are narrow in scope — unlike theories , which cover a broad range of observable phenomena and draw from many different lines of evidence. Meanwhile, a prediction is a result you'd expect to get if your hypothesis or theory is accurate.

So back to 1960 and Hasler and those salmon. One unverified idea was that Coho salmon used eyesight to locate their home streams. Hasler set out to test this notion (or hypothesis). First, he rounded up several fish who'd already returned to their native streams. Next, he blindfolded some of the captives — but not all of them — before dumping his salmon into a faraway stretch of water. If the eyesight hypothesis was correct, then Hasler could expect fewer of the blindfolded fish to return to their home streams.

Things didn't work out that way. The fish without blindfolds came back at the same rate as their blindfolded counterparts. (Other experiments demonstrated that smell, and not sight, is the key to the species' homing ability.)

Although Hasler's blindfold hypothesis was disproven, others have fared better. Today, we're looking at three of the best-known experiments in history — and the hypotheses they tested.

Ivan Pavlov and His Dogs (1903-1935)

Isaac newton's radiant prisms (1665), robert paine's revealing starfish (1963-1969).

The Hypothesis : If dogs are susceptible to conditioned responses (drooling), then a dog who is regularly exposed to the same neutral stimulus (metronome/bell) before it receives food will associate this neutral stimulus with the act of eating. Eventually, the dog should begin to drool at a predictable rate when it encounters said stimulus — even before any actual food is offered.

The Experiment : A Nobel Prize-winner and outspoken critic of Soviet communism, Ivan Pavlov is synonymous with man's best friend . In 1903, the Russian-born scientist kicked off a decades-long series of experiments involving dogs and conditioned responses .

Offer a plate of food to a hungry dog and it'll salivate. In this context, the stimulus (the food) will automatically trigger a particular response (the drooling). The latter is an innate, unlearned reaction to the former.

By contrast, the rhythmic sound of a metronome or bell is a neutral stimulus. To a dog, the noise has no inherent meaning and if the animal has never heard it before, the sound won't provoke an instinctive reaction. But the sight of food sure will .

So when Pavlov and his lab assistants played the sound of the metronome/bell before feeding sessions, the researchers conditioned test dogs to mentally link metronomes/bells with mealtime. Due to repeated exposure, the noise alone started to make the dogs' mouths water before they were given food.

According to " Ivan Pavlov: A Russian Life in Science " by biographer Daniel P. Todes, Pavlov's big innovation here was his discovery that he could quantify the reaction of each pooch by measuring the amount of saliva it generated. Every canine predictably drooled at its own consistent rate when he or she encountered a personalized (and artificial) food-related cue.

Pavlov and his assistants used conditioned responses to look at other hypotheses about animal physiology, as well. In one notable experiment, a dog was tested on its ability to tell time . This particular pooch always received food when it heard a metronome click at the rate of 60 strokes per minute. But it never got any food after listening to a slower, 40-strokes-per-minute beat. Lo and behold, Pavlov's animal began to salivate in response to the faster rhythm — but not the slower one . So clearly, it could tell the two rhythmic beats apart.

The Verdict : With the right conditioning — and lots of patience — you can make a hungry dog respond to neutral stimuli by salivating on cue in a way that's both predictable and scientifically quantifiable.

The Hypothesis : If white sunlight is a mixture of all the colors in the visible spectrum — and these travel at varying wavelengths — then each color will refract at a different angle when a beam of sunlight passes through a glass prism.

The Experiments : Color was a scientific mystery before Isaac Newton came along. During the summer of 1665, he started experimenting with glass prisms from the safety of a darkened room in Cambridge, England.

He cut a quarter-inch (0.63-centimeter) circular hole into one of the window shutters, allowing a single beam of sunlight to enter the place. When Newton held up a prism to this ray, an oblong patch of multicolored light was projected onto the opposite wall.

This contained segregated layers of red, orange, yellow, green, blue, indigo and violet light. From top to bottom, this patch measured 13.5 inches (33.65 centimeters) tall, yet it was only 2.6 inches (6.6 centimeters) across.

Newton deduced that these vibrant colors had been hiding within the sunlight itself, but the prism bent (or "refracted") them at different angles, which separated the colors out.

Still, he wasn't 100 percent sure. So Newton replicated the experiment with one small change. This time, he took a second prism and had it intercept the rainbow-like patch of light. Once the refracted colors entered the new prism, they recombined into a circular white sunbeam. In other words, Newton took a ray of white light, broke it apart into a bunch of different colors and then reassembled it. What a neat party trick!

The Verdict : Sunlight really is a blend of all the colors in the rainbow — and yes, these can be individually separated via light refraction.

The Hypothesis : If predators limit the populations of the organisms they attack, then we'd expect the prey species to become more common after the eradication of a major predator.

The Experiment : Meet Pisaster ochraceus , also known as the purple sea star (or the purple starfish if you prefer).

Using an extendable stomach , the creature feeds on mussels, limpets, barnacles, snails and other hapless victims. On some seaside rocks (and tidal pools) along the coast of Washington state, this starfish is the apex predator.

The animal made Robert Paine a scientific celebrity. An ecologist by trade, Paine was fascinated by the environmental roles of top predators. In June 1963, he kicked off an ambitious experiment along Washington state's Mukkaw Bay. For years on end, Paine kept a rocky section of this shoreline completely starfish-free.

It was hard work. Paine had to regularly pry wayward sea stars off "his" outcrop — sometimes with a crowbar. Then he'd chuck them into the ocean.

Before the experiment, Paine observed 15 different species of animals and algae inhabiting the area he decided to test. By June 1964 — one year after his starfish purge started — that number had dropped to eight .

Unchecked by purple sea stars, the barnacle population skyrocketed. Subsequently, these were replaced by California mussels , which came to dominate the terrain. By anchoring themselves to rocks in great numbers, the mussels edged out other life-forms. That made the outcrop uninhabitable to most former residents: Even sponges, anemones and algae — organisms that Pisaster ochraceus doesn't eat — were largely evicted.

All those species continued to thrive on another piece of shoreline that Paine left untouched. Later experiments convinced him that Pisaster ochraceus is a " keystone species ," a creature who exerts disproportionate influence over its environment. Eliminate the keystone and the whole system gets disheveled.

The Verdict : Apex predators don't just affect the animals that they hunt. Removing a top predator sets off a chain reaction that can fundamentally transform an entire ecosystem.

Contrary to popular belief, Pavlov almost never used bells in his dog experiments. Instead, he preferred metronomes, buzzers, harmoniums and electric shocks.

Frequently Asked Questions

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Biology library

Course: biology library   >   unit 1.

• The scientific method

Controlled experiments

• The scientific method and experimental design

Introduction

How are hypotheses tested.

• One pot of seeds gets watered every afternoon.
• The other pot of seeds doesn't get any water at all.

Control and experimental groups

Independent and dependent variables, independent variables, dependent variables, variability and repetition, controlled experiment case study: co 2 ‍   and coral bleaching.

• What your control and experimental groups would be
• What your independent and dependent variables would be
• What results you would predict in each group

Experimental setup

• Some corals were grown in tanks of normal seawater, which is not very acidic ( pH ‍   around 8.2 ‍   ). The corals in these tanks served as the control group .
• Other corals were grown in tanks of seawater that were more acidic than usual due to addition of CO 2 ‍   . One set of tanks was medium-acidity ( pH ‍   about 7.9 ‍   ), while another set was high-acidity ( pH ‍   about 7.65 ‍   ). Both the medium-acidity and high-acidity groups were experimental groups .
• In this experiment, the independent variable was the acidity ( pH ‍   ) of the seawater. The dependent variable was the degree of bleaching of the corals.
• The researchers used a large sample size and repeated their experiment. Each tank held 5 ‍   fragments of coral, and there were 5 ‍   identical tanks for each group (control, medium-acidity, and high-acidity). Experimental setup to test effects of water acidity on coral bleaching. Control group: Coral fragments are placed in a tank of normal seawater (pH 8.2). Experimental group 1: Coral fragments are placed in a tank of slightly acidified seawater (pH 7.9). Experimental group 2: Coral fragments are placed in a tank of more strongly acidified seawater (pH 7.65). The water acidity is the independent variable. 8 weeks are allowed to pass for each of the tanks... Control group: Corals are about 10% bleached on average. Experimental group 1 (medium acidity): Corals are about 20% bleached on average. Experimental group 2 (higher acidity): Corals are about 40% bleached on average. Degree of coral bleaching is the dependent variable. Note: None of these tanks was "acidic" on an absolute scale. That is, the pH ‍   values were all above the neutral pH ‍   of 7.0 ‍   . However, the two groups of experimental tanks were moderately and highly acidic to the corals , that is, relative to their natural habitat of plain seawater.

Analyzing the results

Non-experimental hypothesis tests, case study: coral bleaching and temperature, attribution:, works cited:.

• Hoegh-Guldberg, O. (1999). Climate change, coral bleaching, and the future of the world's coral reefs. Mar. Freshwater Res. , 50 , 839-866. Retrieved from www.reef.edu.au/climate/Hoegh-Guldberg%201999.pdf.
• Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S., and Hoegh-Guldberg, O. (2008). Ocean acidification causes bleaching and productivity loss in coral reef builders. PNAS , 105 (45), 17442-17446. http://dx.doi.org/10.1073/pnas.0804478105 .
• University of California Museum of Paleontology. (2016). Misconceptions about science. In Understanding science . Retrieved from http://undsci.berkeley.edu/teaching/misconceptions.php .
• Hoegh-Guldberg, O. and Smith, G. J. (1989). The effect of sudden changes in temperature, light and salinity on the density and export of zooxanthellae from the reef corals Stylophora pistillata (Esper, 1797) and Seriatopora hystrix (Dana, 1846). J. Exp. Mar. Biol. Ecol. , 129 , 279-303. Retrieved from http://www.reef.edu.au/ohg/res-pic/HG%20papers/HG%20and%20Smith%201989%20BLEACH.pdf .

Additional references:

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Researchers detect a new molecule in space

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New research from the group of MIT Professor Brett McGuire has revealed the presence of a previously unknown molecule in space. The team's open-access paper, “ Rotational Spectrum and First Interstellar Detection of 2-Methoxyethanol Using ALMA Observations of NGC 6334I ,” appears in April 12 issue of The Astrophysical Journal Letters .

Zachary T.P. Fried , a graduate student in the McGuire group and the lead author of the publication, worked to assemble a puzzle comprised of pieces collected from across the globe, extending beyond MIT to France, Florida, Virginia, and Copenhagen, to achieve this exciting discovery. 

“Our group tries to understand what molecules are present in regions of space where stars and solar systems will eventually take shape,” explains Fried. “This allows us to piece together how chemistry evolves alongside the process of star and planet formation. We do this by looking at the rotational spectra of molecules, the unique patterns of light they give off as they tumble end-over-end in space. These patterns are fingerprints (barcodes) for molecules. To detect new molecules in space, we first must have an idea of what molecule we want to look for, then we can record its spectrum in the lab here on Earth, and then finally we look for that spectrum in space using telescopes.”

Searching for molecules in space

The McGuire Group has recently begun to utilize machine learning to suggest good target molecules to search for. In 2023, one of these machine learning models suggested the researchers target a molecule known as 2-methoxyethanol. 

“There are a number of 'methoxy' molecules in space, like dimethyl ether, methoxymethanol, ethyl methyl ether, and methyl formate, but 2-methoxyethanol would be the largest and most complex ever seen,” says Fried. To detect this molecule using radiotelescope observations, the group first needed to measure and analyze its rotational spectrum on Earth. The researchers combined experiments from the University of Lille (Lille, France), the New College of Florida (Sarasota, Florida), and the McGuire lab at MIT to measure this spectrum over a broadband region of frequencies ranging from the microwave to sub-millimeter wave regimes (approximately 8 to 500 gigahertz). 

The data gleaned from these measurements permitted a search for the molecule using Atacama Large Millimeter/submillimeter Array (ALMA) observations toward two separate star-forming regions: NGC 6334I and IRAS 16293-2422B. Members of the McGuire group analyzed these telescope observations alongside researchers at the National Radio Astronomy Observatory (Charlottesville, Virginia) and the University of Copenhagen, Denmark. 

“Ultimately, we observed 25 rotational lines of 2-methoxyethanol that lined up with the molecular signal observed toward NGC 6334I (the barcode matched!), thus resulting in a secure detection of 2-methoxyethanol in this source,” says Fried. “This allowed us to then derive physical parameters of the molecule toward NGC 6334I, such as its abundance and excitation temperature. It also enabled an investigation of the possible chemical formation pathways from known interstellar precursors.”

Looking forward

Molecular discoveries like this one help the researchers to better understand the development of molecular complexity in space during the star formation process. 2-methoxyethanol, which contains 13 atoms, is quite large for interstellar standards — as of 2021, only six species larger than 13 atoms were detected outside the solar system , many by McGuire’s group, and all of them existing as ringed structures.  

“Continued observations of large molecules and subsequent derivations of their abundances allows us to advance our knowledge of how efficiently large molecules can form and by which specific reactions they may be produced,” says Fried. “Additionally, since we detected this molecule in NGC 6334I but not in IRAS 16293-2422B, we were presented with a unique opportunity to look into how the differing physical conditions of these two sources may be affecting the chemistry that can occur.”

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Screen Rant

Shocking bad batch theory reveals a palpatine experiment that would've made him invincible.

An exciting Bad Batch season 3 theory suggests a sabotaged Palpatine experiment could have made him invincible in the established Star Wars canon.

Warning! This post contains SPOILERS for Star Wars: The Bad Batch season 3, episode 14, "Flash Strike"

• Empire's Project Necromancer may involve the cloned Zillo Beast revealed to still be on Tantiss.
• One surprising theory is that Hemlock's strand-casting could have involved a theory regarding a Palpatine/Zillo hybrid, explaining its presence and continued study.
• The Zillo Beast will likely be set free to help Clone Force 99 sabotage Hemlock's operations.

An exciting new Star Wars theory for The Bad Batch season 3 suggests the true purpose of the Empire's Zillo Beast . Kept within the bowels of the Empire's secret cloning facility on Mount Tantiss, it's been confirmed that the Empire is still holding the massive kaiju-like creature captive. To that end, one has to wonder what the Empire might be planning for the impressive and powerful creature going forward.

The conflict of the Clone Wars awoke one of the last surviving Zillo Beasts on Malastare where it was captured and taken to Coruscant to be studied by the Republic. However, the semi-sentient Zillo Beast ultimately escaped before it was later killed. However, Chancellor Palpatine ordered the creature's body to be taken and studied, and The Bad Batch season 2 confirmed that the newly formed Empire had successfully cloned the Zillo Beast. Now, The Bad Batch season 3 is teasing big things for the creature, and an exciting theory suggests the Zillo Beast may be tied to an off-shoot of Palpatine's Project Necromancer .

Star Wars Movies In Order: How To Watch Release Order, Chronologically & With The TV Shows

The bad batch season 3's zillo beast plot makes no sense, why is the zillo beast on tantiss.

Having returned in The Bad Batch season 2 as a juvenile, it was logical that the Zillo Beast was being held at the Mount Tantiss cloning facility run by Doctor Hemlock. However, the purpose for why it was cloned and what Palpatine wants with the Zillo Beast has never been fully confirmed beyond an implied interest in replicating its durable armor for the Republic's ships (now the Empire's) and that its genetic material could be weaponized in general. Following its breakout and recapture, the cloned Zillo Beast was returned to the planet Weyland and held in Mount Tantiss.

That said, future reveals about Mount Tantiss have made the Zillo Beast's return in The Bad Batch season 3 somewhat odd. It's been revealed that Hemlock is in charge of Project Necromancer , meant to produce a viable Force-sensitive clone for Palpatine to transfer his consciousness into in the event of his original body's death. With this new context, it's hard to see why Hemlock still has an interest in the Zillo Beast and why his science division is still in charge of containing the creature as seen when Omega briefly escapes her cell and sneaks through the facility's walls.

Hemlock's Cloning Experiments Involve The Creation Of Strandcasts

Cloning beyond the original template.

Narratively, it's not hard to see why the Zillo Beast has returned in The Bad Batch season 3. It will likely be used to aid in Omega and Clone Force 99's break-out attempt, dismantling Hemlock's operations and freeing the many clones who became imprisoned test subjects following the rise of the Empire. While its purpose to the Empire remains to be seen, it's worth noting that much of Hemlock's work involves stand-casting, a variation of the cloning process where an original host's genetic template is spliced with other genetic materials to create artificial clones with traits beyond those of the original .

In the case of Project Necromancer, strand-casting was seen as the solution to circumvent the problem of clones lacking force sensitivity. By testing hundreds of different blood samples from clones and searching for the right genetic materials, it was determined that Omega's blood had the traits to ensure that a Palpatine clone could retain its power in the Force without degradation. It's why she's so valuable to Doctor Hemlock and the Empire. In the same vein, this concept also connects to Grogu, Supreme Leader Snoke , and Rey's father Dathan.

As seen in The Mandalorian , Grogu is like Omega in that his blood has a high enough M-count and the right genetic makeup to make him a viable test subject for Project Necromancer. Likewise, both Snoke and Dathan have been identified as strandcasts, albeit ones that were not seen as viable options for Palpatine, Snoke due to his physical deformities and Dathan due to his lack of Force sensitivity. However, perhaps Force sensitivity wasn't the only thing Hemlock was looking for with his strandcast experiments on Tantiss .

Is Hemlock Trying To Create A Clone Body For Palpatine... From the Zillo Beast?

A palpatine/zillo beast hybrid would be insane.

Keeping all this in mind, there is a surprising Bad Batch theory that might just fit regarding the Zillo Beast: perhaps Hemlock was looking to create a strandcast of Emperor Palpatine and the Zillo Beast's genetic DNA . After all, having a body resistant to lightsabers and the ability to absorb energy like a Zillo Beast would likely be seen as beneficial traits in the eyes of the Palpatine, assuming those traits could be isolated and applied to a human Force-sensitive body. In theory, a Zillo Beast/human hybrid body could have made Palpatine even more powerful (and very hard to kill again).

Another factor that may play a role was a brief image of Hemlock's tablet in The Bad Batch season 3, episode 10, just before his conversation with Governor Tarkin . Featuring never-before-seen designs for new trooper armor, perhaps Hemlock was looking to create unique forces of his own using the Zillo Beast's unique durability. This would mirror the future Moff Gideon who was revealed in The Mandalorian season 3 to be creating his own clones on the side while working on Project Necromancer for the Imperial Shadow Council .

The possible destruction of the Tantiss facility could help explain why the Empire's study of the Zillo Beast's genetic material seemingly never goes anywhere in the future of the Star Wars canon.

At any rate, there will hopefully be a greater purpose and reasoning revealed as to why the Zillo Beast has remained on Tantiss, beyond being a convenient way for Clone Force 99 and Omega to take down Hemlock's operations. That said, the possible destruction of the Tantiss facility could help explain why the Empire's study of the Zillo Beast's genetic material seemingly never goes anywhere in the future of the Star Wars canon. Regardless, it will be very exciting to see what happens to the Zillo Beast in The Bad Batch season 3's imminent finale.

The Bad Batch season 3 finale airs May 1st on Disney+

Star Wars: The Bad Batch

Star Wars: The Bad Batch is an action-adventure animated series set after the events of The Clone Wars, following Clone Force 99 (a.k.a. the Bad Batch.) Finding themselves immune to the brainwashing effects of Order 66, the Bad Batch become mercenaries for hire while outrunning the empire, now seeing them as fugitives of the law.

Advertisement

A new kind of experiment at the LHC could unravel quantum reality

The Large Hadron Collider is testing entanglement in a whole new energy range, probing the meaning of quantum theory – and the possibility that an even stranger reality lies beneath

By Michael Brooks

24 April 2024

Kyle Ellingson

For  Alan Barr , it started during the covid-19 lockdowns. “I had a bit more time. I could sit and think,” he says.

He had enjoyed being part of the success at CERN’s Large Hadron Collider (LHC) near Geneva, Switzerland — the particle collider that discovered the Higgs boson . But now, he wondered, were they missing a trick? “I had spent long hours screwing bits of it together. And I thought, ‘Well, we’ve built this beautiful piece of apparatus, but maybe we could be doing more with it,’ ” he says.

The LHC is typically seen as a machine for finding new particles. But now Barr and a slew of other physicists are asking if it can also be used to probe the underlying meaning of quantum theory and why it paints reality as being so deeply weird.

That’s exactly what Barr and his colleagues are now investigating in earnest. Last year, they published the results of an experiment in which they showed that pairs of fundamental particles called top quarks could be put into the quantum state known as entanglement .

This was just the first of many entanglement experiments at particle colliders that could open up a whole new way of studying the nature of the universe. We can now ask why reality in quantum mechanics is so hard to pin down and what this has to do with experimenters — or even particles — having free will. Doing so could reveal whether space-time is fundamental or perhaps unveil a deeper reality that is even stranger than quantum mechanics. “We can do really different things with this collider,” says Barr.

Rethinking reality: Is the entire universe a single quantum object?

In the face of new evidence, physicists are starting to view the cosmos not as made up of disparate layers, but as a quantum whole linked by entanglement

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How Peter Higgs revealed the forces that hold the universe together

Peter higgs, physicist who theorised the higgs boson, has died aged 94, we've glimpsed something that behaves like a particle of gravity, how to wrap your head around the most mind-bending theories of reality.

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"Unlocked: A Jail Experiment": 7 things we learned from Netflix's free-roaming detainees experiment

What happens in a detention facility with cells left open and little supervision, by gabriella ferrigine.

Sherriff Eric Higgins' proposal seemed almost too radical to believe.

Allow 46 incarcerated men, many of whom were serving time for capital charges, to roam free in their unit with open cell doors for weeks at a time with minimal supervision from deputies. And yet, the sheriff, who helmed the social experiment turned unscripted Netflix series conducted at Pulaski County Regional Detention Facility in Little Rock, Arkansas, felt it could be exactly what detainees needed. 

"We thought, ‘What can we do to create some ownership for those detainees in that unit?’” Higgins told Netflix’s  Tudum  of the eight-part docuseries, "Unlocked: A Jail Experiment," which premiered on April 10.

 “How do we make the facility safer, and what can we do to still hold them accountable but empower them at the same time?”

In endeavoring to create an environment with less direct supervision, Higgins wanted to both humanize incarcerated people and give the men in the facility's H-unit the autonomy to foster an environment grounded in community. Doing so, he hoped, would not only spur accountability and a sense of collectiveness but also lower the detention center's recidivism rates — the tendency of an incarcerated person to re-offend, often after being released — and discourage detainees from committing future crimes. 

“In this country, we have a certain perception of someone who goes to jail — the assumption being that they’re guilty,” Higgins told the outlet. “But they deserve dignity. These individuals, they’re fathers, they’re uncles, they’re sons. People care about them . . . they’re not just a number. I believe that if you treat people right, and you hold them accountable . . . I think they take that with them when they walk out of this facility. I think we have proven that people will rise to the expectation.”

Here are key moments from the experiment: 

"Unlocked" is streaming on Netflix.

about this topic

• The public health case for decarcerating America's prison system
• Who understands "lockdown" and isolation? The formerly incarcerated are the experts we need to know
• Desert bloom: My wedding day, inside a California prison

Gabriella Ferrigine is a staff writer at Salon. Originally from the Jersey Shore, she moved to New York City in 2016 to attend Columbia University, where she received her B.A. in English and M.A. in American Studies. Formerly a staff writer at NowThis News, she has an M.A. in Magazine Journalism from NYU and was previously a news fellow at Salon.

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Sols 2536-2537: SAM Wet Chemistry Experiment

Searching for organic molecules in rocks on Mars is no easy task. Curiosity’s Sample Analysis at Mars (SAM) instrument is designed to analyze the chemical composition of gases, which it creates by slowly heating rock samples in an oven. The volatile gases that are driven off the heated rock sample get sent to SAM’s gas chromatograph and mass spectrometer (GCMS), which can identify the different gaseous compounds. However, organic molecules are tough to detect with this technique, because instead of transforming straight into gases when heated, they can decompose into simpler molecules.

But if organic molecules are “derivatized” before they’re heated – meaning that they react with other chemicals first in order to become more volatile – then the compounds are more likely to enter the GCMS without breaking down, and SAM has a better chance of detecting them. This derivatization process uses solvents of chemicals, so we call it a “wet chemistry” experiment. Curiosity only has nine cups containing these solvents, so we are careful to save our wet chemistry experiments for only the most interesting rock samples.

The “Glen Etive” site, which we have been studying for the past month, is enticing enough for this special experiment! Last week, to prepare for the experiment, Curiosity dropped the “Glen Etive 2” drill sample into the SAM inlet on the rover’s deck on Sol 2531 , and took a picture of the inlet afterwards with Mastcam (shown above). Today’s plan for Curiosity includes performing the SAM wet chemistry experiment on the “Glen Etive 2” drill sample on Sol 2536.

Afterwards, Curiosity will spend most of the day on Sol 2537 recharging from the energy-intensive SAM activities, with a small number of additional science observations (including ChemCam observations of the “Glen Etive 1” sample dump pile and pebble targets “Peeblesshire” and “Perthshire,” and a Navcam movie to watch for clouds).

Written by Melissa Rice, Planetary Geologist at Western Washington University

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Sols 4164-4165: What’s Around the Ridge-bend?

Sols 4161-4163: Double Contact Science

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Yellowstone’s Wolves: A Debate Over Their Role in the Park’s Ecosystem

New research questions the long-held theory that reintroduction of such a predator caused a trophic cascade, spawning renewal of vegetation and spurring biodiversity.

Yellowstone’s ecological transformation through the reintroduction of wolves has become a case study for how to correct out-of-balance ecosystems. But new research challenges that notion. Credit... Elizabeth Boehm/Danita Delimont, via Alamy

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By Jim Robbins

• April 23, 2024

In 1995, 14 wolves were delivered by truck and sled to the heart of Yellowstone National Park in Wyoming, where the animal had long been absent. Others followed.

Since then, a story has grown up, based on early research, that as the wolves increased in number, they hunted the park’s elk herds, significantly reducing them by about half from 17,000.

The wolves’ return and predatory dominance was believed to have had a widespread effect known as a trophic cascade, by decreasing grazing and restoring and expanding forests, grasses and other wildlife. It supposedly even changed the course of rivers as streamside vegetation returned.

Yellowstone’s dramatic transformation through the reintroduction of wolves has become a global parable for how to correct out-of-balance ecosystems.

In recent years, however, new research has walked that story back. Yes, stands of aspen and willows are thriving again — in some places. But decades of damage from elk herds’ grazing and trampling so thoroughly changed the landscape that large areas remain scarred and may not recover for a long time, if ever.

Wolf packs, in other words, are not magic bullets for restoring ecosystems.

“I would say it’s exaggerated, greatly exaggerated,” said Thomas Hobbs, a professor of natural resource ecology at Colorado State University and the lead author of a long-term study that adds new fuel to the debate over whether Yellowstone experienced a trophic cascade.

“You could argue a trophic trickle maybe,” said Daniel Stahler, the park’s lead wolf biologist who has studied the phenomenon. “Not a trophic cascade.”

Not only is the park’s recovery far less robust than first thought, but the story as it has been told is more complex, Dr. Hobbs said.

But the legend of the wolves’ influence on the park persists.

“How in the world does this lovely story — and it is a beautiful story — come to be seen as fact?” Dr. Hobbs wondered. A chapter of a book tried to answer that, concluding that a video called “ How Wolves Change Rivers ,” which has received tens of millions of views, contributed mightily to the tale.

The ecological record is complicated by the fact that, as elk declined, the number of bison increased substantially, continuing some of the same patterns, like heavy grazing in some places. Moreover, Yellowstone is growing warmer and drier with climate change.

Large numbers of elk in the north of the park had caused significant ecological changes — vegetation disappeared, trampled streams led to extensive erosion, and invasive plant species took hold. Riparian vegetation, or the grasses, the trees and the shrubs along riverbanks and streams, provides a critical habitat for birds, insects and other species to flourish and to maintain biodiversity in the park.

Once elk numbers dwindled, willows and aspens returned along rivers and streams and flourished. The beaver, an engineer of ecosystems, reappeared, using the dense new growth of willows for both food and construction materials. Colonies built new dams, creating ponds that enhanced stream habitats for birds, fish, grizzlies and other bears as well as promoting the growth of more willows and spring vegetation.

But wolves were only one piece of a larger picture, argue Dr. Hobbs and other skeptics of a full-blown trophic cascade at Yellowstone. Grizzly bears and humans played a role, too. For eight years after wolves re-entered the park, hunters killed more elk than the wolves did.

“The other members of the predator guild increased, and human harvest outside of the park has been clearly shown to be responsible for the decline in elk numbers the first 10 years after the wolves were introduced,” Dr. Hobbs said.

The changes attributed to the presence of stalking wolves, some research showed, weren’t only the result of fewer elk, but of a change in elk behavior called “the ecology of fear.” Scientists suggested that the big ungulates could no longer safely hang out along river or stream banks and eat everything in sight. They became extremely cautious, hiding in places where they could be vigilant. That allowed a return of vegetation in those places.

Dr. Hobbs and others contend that subsequent research has not borne that theory out.

Another overlooked factor is that around the same time wolves were returning, 129 beavers were reintroduced by the U.S. Forest Service onto streams north of the park. So it wasn’t just wolf predation on elk and the subsequent return of wolves that enabled an increase in beavers, experts say.

Some researchers say the so-called trophic cascade and rebirth of streamside ecosystems would have been far more robust if it weren’t for the park’s growing bison herd. The bison population is at an all-time high — the most recent count last summer found nearly 5,000 animals. Much larger than elk, bison are less likely to be vulnerable to wolves, which numbered 124 this winter.

The park’s bison, some researchers say, are overgrazing and otherwise seriously damaging the ecosystems — allowing the spread of invasive species and trampling and destroying native plants.

The heavily grazed landscape is why, critics say, some 4,000 bison, also a record, left Yellowstone for Montana in the winter of 2023-24, when an unusually heavy snow buried forage. Because some bison harbor a disease, called brucellosis, that state officials say could infect cattle, they are not welcome outside the park’s borders. (There are no documented cases of transmission between bison and cattle.)

Montana officials say killing animals that may carry disease as they leave the park is the only way to stem the flow. During a hunt that began in the winter of 2023, Native Americans from tribes around the region took part. All told, hunters killed about 1,085 bison; 88 more were shipped to slaughter and 282 were transferred to tribes. This year, just a few animals have left the park.

The Park Service is expected to release a bison management plan in the coming months. It is considering three options: to allow for 3,500 to 5,000 animals, 3,500 to 6,000, or a more natural population that could reach 7,000.

Richard Keigley, who was a research ecologist for the federal Geological Survey in the 1990s, has become an outspoken critic of the park’s bison management.

“They have created this juggernaut where we’ve got thousands of bison and the public believes this is the way things always were,” he said. “The bison that are there now have destroyed and degraded their primary ranges. People have to realize there’s something wrong in Yellowstone.”

Dr. Keigley said the bison population in the park fluctuated in the early years of the park, with about 229 animals in 1967. It has grown steadily since and peaked last year at 5,900.

“There is a hyperabundant bison population in our first national park,” said Robert Beschta, a professor emeritus of forest ecosystems at Oregon State University who has studied Yellowstone riparian areas for 20 years. He pointed to deteriorating conditions along the Lamar River from bison overgrazing.

“They are hammering it,” Mr. Beschta said. “The Lamar ranks right up there with the worst cattle allotments I’ve seen in the American West. Willows can’t grow. Cottonwoods can’t grow.”

A warmer and drier climate, he said, is making matters worse.

Such opinions, however, are not settled science. Some park experts believe that the presence of thousands of bison enhances park habitats because of something called the Green Wave Hypothesis.

Chris Geremia, a park biologist, is an author of a paper that makes the case that a large numbers of bison can stimulate plant growth by grazing grasses to the length of a suburban lawn. “By creating these grazing lawns bison and other herbivores — grasshoppers, elk — these lawns are sustaining more nutritious food for these animals,” he said.

Dr. Geremia contends that a tiny portion — perhaps one-tenth of one percent — of the park may be devoid of some plants. “The other 99.9 percent of those habitats exists in all different levels of willow, aspen and cottonwood,” he said.

The Greater Yellowstone Coalition, a conservation organization, favors a bison population of 4,000 to 6,000 animals. Shana Drimal, who heads the group’s bison conservation program, said that park officials needed to monitor closely changing conditions like climate, drought and bison movement to ensure the ecosystems wouldn’t become further degraded.

Several scientists propose allowing the bison to migrate to the buffer zones beyond the park’s borders, where they are naturally inclined to travel. But it remains controversial because of the threat of disease.

“The only solution is to provide suitable winter range outside the park where they should be tolerated,” said Robert Crabtree, a chief scientist for the Yellowstone Ecological Research Center, a nonprofit. “When they migrate outside the park now it’s to habitat they evolved to prefer — and instead we kill them and ship them away.”

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April 24, 2024

A Singular Climate Experiment Takes Shape in the Amazon

After years of delay, researchers are ready to inject carbon dioxide into jungle plots.

By Daniel Grossman

Patrick Vanier/Scientific American

This story was produced with assistance from the Pulitzer Center and additional support from the Pendleton Mazer Family Fund.

[CLIP: Sound of milling crowd and forest noises]

Daniel Grossman: Several dozen people pile out of a charter bus into a clearing in the central Amazon jungle. They’ve driven to a research site called ZF2 from Manaus, the Amazon’s largest city. It’s October 2023.

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Beto Quesada: Please, everybody, come closer, come closer.

Grossman: They’re the scientific advisers for one of the largest—and some say most important—ecological experiments in the world, now in its final stages of construction. Beto Quesada , an ecologist and one of the three leaders of the project, herds them into a semicircle.

Quesada: It is so great to have you all here.

Grossman: I’m Daniel Grossman, and this is Science, Quickly . Today we present the second of three episodes about AmazonFACE —FACE is an acronym I’ll explain later—an experiment that could help forecast the future health of the Amazon forest—and the rest of the planet as a result.

The experiment’s elaborate setup in six circular plots is almost done. Around the perimeter of each of the two plots that are closest to completion, 16 metal spires rise up off the forest floor, pierce the tree canopy and lord over the tallest crowns. The trees inside these circles have been monitored as closely as patients in an intensive care unit. Researchers have measured trunks, photographed roots, collected dead leaves and quantified sap flow. And now these experts are getting their first look at what, thanks to the cost of construction and long-term monitoring, Beto says are the world’s most expensive square feet of jungle.

Quesada: And I hope you enjoy what we are going to see. [Applause]

[CLIP: Crowd noise fades, followed by faint forest sounds]

Grossman: Okay, so here’s the background.

[CLIP: “Working It Out,” by Jon Björk ]

Amazon trees soak up more than a billion tons of carbon dioxide from the air every year and convert it into starches and sugars. The carbon in these compounds is sequestered for decades—or even centuries—in wood and soil, slowing the advance of global warming.

But scientists worry that increasing temperatures and drought caused by climate change will weaken the forest. If that were to happen, this forest’s ingesting of carbon could slow or stop entirely . And CO 2 would build up faster in the atmosphere and worsen the climate emergency.

Climate researchers realized decades ago that an experiment like this one was essential for forecasting the Amazon’s future. But it took years to get traction. I first heard about the project in 2015, a couple of years after it got underway.

[CLIP: Faint forest noises, the sound of feet climbing the metal steps of a tower, forest sounds at the top of the tower and various bird calls]

Grossman: At dawn one day early the next year, I slogged up the 18-flight open-air staircase of a metal observation tower. Looking down I saw beams of early morning sunlight bathing the canopy in orange.

Next to me stood David Lapola , a climate scientist at Brazil’s State University of Campinas and another of the project’s leaders. He described to me one of his nightmares. It’s called the dieback hypothesis. We heard about it briefly in Episode One.

David Lapola: The forest dieback hypothesis is the transformation of this forest—a catastrophic loss of biomass—due to global climate change.

Grossman: A dieback would occur if the Amazon jungle and global climate interact in a disastrous, vicious cycle. It would work like this: Hotter, drier weather caused by climate change weakens and kills Amazon trees, reducing the amount of CO 2 from fossil fuel burning that the forest soaks up. As a result, more CO 2 accumulates in the atmosphere, which causes more warming, degrading the forest more, and so on, until this place—and the rest of the planet—is catastrophically hot.

Lapola: Certain scenarios predict that you would have a climate here—regional climate—that would not sustain a forest anymore.

Grossman: Some scientists say there is still hope—that an effect called carbon fertilization will disrupt this feedback loop and save the forest. The idea is that extra CO 2 in the air might help trees grow better, counteracting the impacts of increased heat and drought.

Lapola: If we have CO 2 fertilization, the forest will be conserved. If we don't have it, the forest wouldn’t survive anymore.

Grossman: But scientists can’t yet say if carbon fertilization will save the Amazon, in part because the forest is so diverse. This jungle has something like 10 times as many species of trees as North America—and scientists know practically nothing about a lot of them.

Lapola: When you look here, you see this number of species here, this high number.

[CLIP: “Let There Be Rain,” by Silver Maple ]

Grossman: In the first episode of this miniseries I mentioned an important carbon fertilization experiment at Duke University. It started in the mid-1990s and continued for more than a decade. It showed that when sprayed with extra CO 2 , plots of pine trees grew faster and absorbed more carbon. The benefit of the carbon dioxide was sustained—at least over the course of the experiment. It did vary from plot to plot, suggesting that other factors, like natural variation in water and nutrient availability, could affect how much of a boost trees get from additional CO 2 .

What I didn't say was that a related experiment that started a couple of years later highlighted the important role that nutrients play. Plots of sweet gum trees in Tennessee that were sprayed with extra CO 2 initially took up additional carbon. But after the first several years, the benefit dissipated. The researchers concluded that enhanced growth caused by the increase in CO 2 reduced the supply of plant-available nitrogen in the soil. That, in turn, limited the benefit that extra carbon dioxide could provide.

Will the Amazon see a benefit from extra CO 2 , like what happened at Duke? That could protect it from some of the hazards climate change is expected to continue bringing, like higher temperatures and reduced rainfall. Or will it act more like the sweet gums in Tennessee and not get the long-term CO 2 boost and the protection from climate change that could bring?

Lapola: That’s the important thing this experiment will bring about: how the forest may be resilient to this future increase in temperature and CO 2 .

Grossman: Researchers have called for this experiment for decades. But there are good reasons why nobody has conducted it before.

Amazon trees are huge—up to 110 feet tall here at ZF2—so they require extra-large plots. That means bigger, more expensive gear for spraying CO 2 . Infrastructure such as roads and technical support is relatively poor. And funding for tropical forest research is scarce, a problem that we’ll see hindered AmazonFACE.

Incidentally, the “FACE” in AmazonFACE stands for Free-Air CO 2 Enrichment. It’s called “free air” because the plots have no walls, making the experiment more realistic than any lab study.

[CLIP: Bird vocalizations and other forest sounds]

Grossman: That was 2016, when I climbed that tower with David. Several years earlier he and his collaborators had staked out the first two plots and had begun making measurements of the trees there, establishing their behavior under natural conditions. The researchers said the site could be the world’s most intensely studied patch of tropical forest.

Then in 2017 AmazonFACE ran out of money. David told me that they wanted about $20 million for construction and staff salaries, an amount that only a country or a very rich person could afford. But Brazil was in political turmoil. The president had recently been removed from office, and the government was stuck. Undaunted, David, Beto and their collaborators in the U.S. and Europe reached out to other governments.

Lapola: You never know where a yes will come from the middle of many noes.

Grossman: They even tried Jeff Bezos, then president and CEO of the other Amazon. He seemed like a good candidate. He’d just christened a mini rain forest in his Seattle headquarters. But courting the world’s then-richest person didn’t work, and for four years they got one no after another.

Lapola: I was about to give up. I confess, I wasn’t believing this would happen anymore.

Grossman: Beto also despaired at times. Fortunately Bruno Takeshi , the third core member of the team, never gave up hope.

Bruno Takeshi: I actually never thought that we would just end the idea to do the AmazonFACE, to be honest. I just imagined it’s a matter to find the proper door to be knocked because the AmazonFACE idea is a lot good that we cannot just forget about it.

Grossman: In 2021 the U.K. finally agreed to chip in funding.

Lapola: The support from the British government was decisive, so the Brazilian government came to match it.

Grossman: Brazil gave roughly $6 million, and the U.K. funding would eventually total about $9 million.

Lapola: And then this became a reality.

Grossman: According to David, Brazil is currently considering another multimillion-dollar grant, money that he says AmazonFACE needs to buy the massive amount of carbon dioxide required to run the experiment in the years ahead.

[CLIP: Sounds of trucks going by and backing up and other construction noises]

Grossman: I go back to the ZF2 site for my fourth or fifth visit in July 2023. Now construction is in full swing. The formerly tranquil forest sounds, feels and smells like a mining camp. Diesel fumes waft through the air. Bulldozers, dump trucks and backhoes lumber up and down a new road that snakes into the jungle to the six experimental plots.

[CLIP: Clinking of metal parts, ratchet sound from socket wrench and other construction noises]

Grossman: In a clearing, a group of men wearing hard hats and carrying oversized wrenches swarm like ants around a long lattice of metal tubes laid out on the ground. They’re assembling a section of a tower they’ve bolted together from precut metal parts. The 16 towers assembled here will be secured to huge concrete blocks around the perimeter of one of the plots. The purpose of the towers is to hold up the tubing that will spread CO 2 into the forest.

Takeshi: And then we’re now in this stage to make the assembly.

Grossman: Bruno is the logistics whiz who oversees all this activity. Today he brought a guest, Susan Trumbore , to see the progress.

Takeshi: So we have two teams—actually, three teams on the field.

Grossman: Susan is practically scientific royalty. She is a director of the Max Planck Institute for Biogeochemistry in Jena, Germany, and is an expert in the stability of carbon in ecosystems.

Susan Trumbore: Amazing. I never thought it would actually happen.

Takeshi: Yes, yes, thank you so much.

Grossman: Bruno says it’s essential to get every detail right. The hostile climate—with intense heat and humidity year-round—and the great distance from the city make building and running equipment here tricky at best. He’s just noticed that workers have installed some bolts upside down, making them more likely to fail.

[CLIP: Takeshi speaks with metal workers in Portuguese]

Grossman: He tells them to start over.

“Are you this fastidious about everything?” I ask Bruno. He says, “Yes.”

Takeshi: I’m a guy that, every day before I move my car from the garage, I open my hood and take a look in the oil level, the water level.

Grossman (tape): Every day?

Takeshi: Every single day [laughs].

Grossman: Looming above the worksite and reaching high over the forest horizontally is the bright yellow arm of a crane. It built skyscrapers and bridges before being shipped to the Amazon. The operator, Mabel Marques, says lifting loads here is a challenge.

Mabel Marques (speaking in Portuguese with English translation): It’s quite different from other places I’ve worked at. There are many trees near the areas where the towers will be set up. There will be moments of difficulty, but I’m sure we’ll find the best and most efficient way to do the work.

Grossman: Mabel holds the crane’s remote control, which looks like an industrial version of an XBox controller. Using two small joysticks operated with his fingertips, he lifts a 30-foot section of tower—four stout legs laced together with braces—off the ground.

[CLIP: Walkie-talkie beeps and voices]

Once it’s upright and dangling above the forest, he swings it over the plot. Guided by helpers equipped with walkie-talkies, he threads the steel latticework through the canopy. Waiting workers bolt it to the concrete base. Next he’ll swing over a second piece and then a third.

Several completed towers already stick up above the treetops like shiny, leafless stalks. The circle of towers is taking shape.

[CLIP: Construction sounds]

Grossman: A brawny man holding a handsaw has just shimmied up a tall, thin trunk and tied himself to a branch. Maria Juliana Monte , one of Bruno’s assistants, says the climber is preparing the canopy here for the next tower.

Maria Juliana Monte: He’s trying to come to the middle of the branch, so we need to clear the way of this tower. So he needs to get closer to the side.

Grossman (tape): So you have to have a complete hole right there, right? There has to be a hole in the canopy right above me?

Monte: Yes, they have to have the space clear.

Grossman: It’s tricky because a mass of vines wraps around the tree’s limbs. The man in the tree has to untangle the vines so other limbs or trunks aren’t ripped down when the branches are cut. That could complicate calculations of the carbon that the plot absorbs and put the crew in danger.

Monte: You see, it’s one of the highest trees. Also, its canopy—it’s really, really big. You know, a branch could kill all of us if it’s not cut the right way.

[CLIP: Plot sounds fade]

Quesada and others: Let’s go inside! Let’s go inside.

Grossman: I’m back at the science advisers’ tour of ZF2 last October.

Beto leads them to the two completed plots, each now ringed by a full set of towers.

One of these plots will be exposed to twice as much carbon dioxide as existed in the air before the beginning of global industrialization in the 19th century.

The other plot, for comparison, will not receive extra CO 2 . Two more matched sets are still under construction.

But the feeling this day is not about the details—it’s about the moment. It’s emotional for the project’s leaders to be showing it off to some of the world’s foremost carbon fertilization experts.

Quesada: Humans, we need crazy ideas, right? To really push boundaries. And for a very long time it sounded, like, too crazy. But here we are—we are doing it.

Grossman: These plots will test CO 2 in the next few months. The full set should be running in early 2025.

In the next and final episode of this three-part podcast, we’ll learn what the researchers running the experiment think will happen when they turn on the carbon dioxide.

[CLIP: Theme music]

Science, Quickly is produced by Jeff DelViscio, Rachel Feltman, Kelso Harper and Madison Goldberg. Our theme music was composed by Dominic Smith.

Shayna Posses and Aaron Shattuck fact-checked this miniseries.

Special thanks to Dado Galdieri and Patrick Vanier for logistical support and to Lucas Pinheiro for providing translations.

Don’t forget to subscribe to Science, Quickly . And for more in-depth science news, visit ScientificAmerican.com.

For Scientific American ’s Science, Quickly , I’m Daniel Grossman.