science fair hypothesis definition

What is a hypothesis?

No.  A hypothesis is sometimes described as an educated guess.  That's not the same thing as a guess and not really a good description of a hypothesis either.  Let's try working through an example.

If you put an ice cube on a plate and place it on the table, what will happen?  A very young child might guess that it will still be there in a couple of hours.  Most people would agree with the hypothesis that:

An ice cube will melt in less than 30 minutes.

You could put sit and watch the ice cube melt and think you've proved a hypothesis.  But you will have missed some important steps.

For a good science fair project you need to do quite a bit of research before any experimenting.  Start by finding some information about how and why water melts.  You could read a book, do a bit of Google searching, or even ask an expert.  For our example, you could learn about how temperature and air pressure can change the state of water.  Don't forget that elevation above sea level changes air pressure too.

Now, using all your research, try to restate that hypothesis.

An ice cube will melt in less than 30 minutes in a room at sea level with a temperature of 20C or 68F.

But wait a minute.  What is the ice made from?  What if the ice cube was made from salt water, or you sprinkled salt on a regular ice cube?  Time for some more research.  Would adding salt make a difference?  Turns out it does.  Would other chemicals change the melting time?

Using this new information, let's try that hypothesis again.

An ice cube made with tap water will melt in less than 30 minutes in a room at sea level with a temperature of 20C or 68F.

Does that seem like an educated guess?  No, it sounds like you are stating the obvious.

At this point, it is obvious only because of your research.  You haven't actually done the experiment.  Now it's time to run the experiment to support the hypothesis.

A hypothesis isn't an educated guess.  It is a tentative explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation.

Once you do the experiment and find out if it supports the hypothesis, it becomes part of scientific theory.

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Introduction

1. get your idea and do some research, 2. ask a testable question, 3. design and conduct your experiment, 4. examine your results, 5. communicate your experiment and results.

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How to Do a Science Fair Project

To get started on your science fair project, you'll learn to observe the world around you and ask questions about the things you observe.

Observe the world around you and ask questions about the things you observe.

Develop your idea into a question you can test. Your question should follow the format, "How does [input] affect [output]?"

Design your experiment and keep track of the results. Remember to only change one variable and conduct your experiment multiple times for each trial. Each trial should be repeated in exactly the same way.

Now that your experiment is done, it's time to examine your results. You want to look for trends in your results and draw conclusions from those trends. You also want to examine your data for possible influences from factors you didn't consider at first.

Make a poster display that summarizes your experiment so you can share your results. Be sure to include the question you were trying to answer (your hypothesis), the steps you took to answer that question, your results and any factors that may have influenced your results. Your poster should be visually appealing, but also clear about what you did and why people should care.

Science Fair Central

Scientific Steps

Students who want to find out things as a scientist, will want to conduct a hands-on investigation. While scientists study a whole area of science, each investigation is focused on learning just one thing at a time. This is essential if the results are to be trusted by the entire science community.

Follow the Scientific Steps below to complete your scientific process for your investigation.

What do scientists think they already know about the topic? What are the processes involved and how do they work? Background research can be gathered first hand from primary sources such as interviews with a teacher, scientist at a local university, or other person with specialized knowledge. Or use secondary sources such as books, magazines, journals, newspapers, online documents, or literature from non-profit organizations. Don’t forget to make a record of any resource used so that credit can be given in a bibliography.

After gathering background research, the next step is to formulate a hypothesis. More than a random guess, a hypothesis is a testable statement based on background knowledge, research, or scientific reason. A hypothesis states the anticipated cause and effect that may be observed during the investigation.

Consider the following hypothesis: If ice is placed in a Styrofoam container, it will take longer to melt than if placed in a plastic or glass container. I think this is true because my research shows that a lot of people purchase Styrofoam coolers to keep drinks cool.

The time it takes for ice to melt (dependent variable) depends on the type of container used (independent variable.). A hypothesis shows the relationship among variables in the investigation and often (but not always) uses the words if and then.

Design Experiment

Once a hypothesis has been formulated, it is time to design a procedure to test it. A well-designed investigation contains procedures that take into account all of the factors that could impact the results of the investigation. These factors are called variables.

There are three types of variables to consider when designing the investigation procedure.

• The independent variable is the one variable the investigator chooses to change.
• Controlled variables are variables that are kept the same each time.
• The dependent variable is the variable that changes as a result of /or in response to the independent variable.

Step A – Clarify Variable

Clarify the variables involved in the investigation by developing a table such as the one below.

Step B – List Materials Make a list of materials that will be used in the investigation.

Step C – List Steps List the steps needed to carry out the investigation.

Step D – Estimate Time Estimate the time it will take to complete the investigation. Will the data be gathered in one sitting or over the course of several weeks?

Step E – Check Work Check the work. Ask someone else to read the procedure to make sure the steps are clear. Are there any steps missing? Double check the materials list to be sure all to the necessary materials are included.

Data Collection

After designing the experiment and gathering the materials, it is time to set up and to carry out the investigation.

When setting up the investigation, consider...

Carrying out the investigation involves data collection. There are two types of data that may be collected—quantitative data and qualitative data.

Quantitative Data

• Uses numbers to describe the amount of something.
• Involves tools such as rulers, timers, graduated cylinders, etc.
• Uses standard metric units (For instance, meters and centimeters for length, grams for mass, and degrees Celsius for volume.
• May involve the use of a scale such as in the example below.

Qualitative Data

• Uses words to describe the data.
• Describes physical properties such as how something looks, feels, smells, tastes, or sounds.

As data is collected it can be organized into lists and tables. Organizing data will be helpful for identifying relationships later when making an analysis. Using technology, such as spreadsheets, to organize the data can make it easily accessible to add to and edit.

Analyze Data

After data has been collected, the next step is to analyze it. The goal of data analysis is to determine if there is a relationship between the independent and dependent variables. In student terms, this is called “looking for patterns in the data.” Did the change I made have an effect that can be measured?

Recording data on a table or chart makes it much easier to observe relationships and trends. There are many observations that can be made when looking at a data table. Comparing mean average or median numbers of objects, observing trends of increasing or decreasing numbers, comparing modes or numbers of items that occur most frequently are just a few examples of quantitative analysis.

Besides analyzing data on tables or charts, graphs can be used to make a picture of the data. Graphing the data can often help make those relationships and trends easier to see. Graphs are called “pictures of data.” The important thing is that appropriate graphs are selected for the type of data. For example, bar graphs, pictographs, or circle graphs should be used to represent categorical data (sometimes called “side by side” data). Line plots are used to show numerical data. Line graphs should be used to show how data changes over time. Graphs can be drawn by hand using graph paper or generated on the computer from spreadsheets for students who are technically able.

These questions can help with analyzing data:

• What can be learned from looking at the data?
• How does the data relate to the student’s original hypothesis?
• Did what you changed (independent variable) cause changes in the results (dependent variable)?

Draw Conclusions

After analyzing the data, the next step is to draw conclusions. Do not change the hypothesis if it does not match the findings.The accuracy of a hypothesis is NOT what constitutes a successful science fair investigation. Rather, Science Fair judges will want to see that the conclusions stated match the data that was collected.

Application of the Results: Students may want to include an application as part of their conclusion. For example, after investigating the effectiveness of different stain removers, a student might conclude that vinegar is just as effective at removing stains as are some commercial stain removers. As a result, the student might recommend that people use vinegar as a stain remover since it may be the more eco-friendly product.

In short, conclusions are written to answer the original testable question proposed at the beginning of the investigation. They also explain how the student used science process to develop an accurate answer.

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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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How to Write an Effective Hypothesis for a Science Fair Project

By Happy Sharer

Introduction

A scientific hypothesis is a statement that attempts to explain a certain phenomenon or natural occurrence. It is used as the basis for conducting experiments and collecting data in order to prove or disprove its validity. Writing an effective hypothesis for a science fair project requires breaking down the process into several steps. This article outlines the key components of a hypothesis, provides tips for creating a successful hypothesis and explains how to use data to support a hypothesis.

Step-by-Step Guide to Writing an Effective Hypothesis

When writing a hypothesis for a science fair project, there are several steps to follow. The first step is to identify the problem. A clear problem statement will help guide the rest of the process. Once the problem has been identified, formulate a question that can be answered through experimentation. After the question has been formulated, it is time to brainstorm possible explanations for the phenomenon. These explanations will form the basis for the hypothesis. Finally, the hypothesis should be stated in a clear and concise manner.

Components of a Scientific Hypothesis

In order for a hypothesis to be considered valid, it must contain certain components. First, the hypothesis must include both independent and dependent variables. An independent variable is the factor that is being manipulated in the experiment, while a dependent variable is the factor that is being measured. Second, the hypothesis must include operational definitions for each of the variables. Operational definitions are specific descriptions of how each variable is defined and how it will be measured in the experiment.

Creating a Testable Hypothesis

Once the components of the hypothesis have been identified, it is important to make sure the hypothesis is testable. To do this, the hypothesis must be developed further so that it can be tested through experimentation. This involves making predictions about what will happen if the hypothesis is correct. Additionally, it is important to consider any potential confounding variables that may affect the results of the experiment. By taking these factors into account, the hypothesis can be made more testable and accurate.

Benefits of Writing a Clear Hypothesis

Writing a clear and specific hypothesis has a number of benefits. First, it allows for greater clarity when conducting the experiment. This enables the experimenter to focus on the relevant variables and collect the most accurate data possible. Additionally, a clear hypothesis makes it easier to analyze and interpret the data collected during the experiment. This can help to ensure that the results are reliable and accurate.

Using Data to Support Your Hypothesis

Once the experiment has been conducted, it is important to analyze the data collected to determine whether it supports the hypothesis. To do this, it is necessary to collect data that is relevant to the hypothesis. This data should be collected in a systematic manner and recorded accurately. Once the data has been collected, it can be analyzed using various statistical methods to determine whether the hypothesis is supported by the evidence.

Writing an effective hypothesis for a science fair project is a crucial step in the experiment process. It is important to identify the problem, formulate a question, brainstorm possible explanations, and state the hypothesis clearly. Additionally, the hypothesis must include both independent and dependent variables as well as operational definitions. It is also important to make sure the hypothesis is testable and to consider any potential confounding variables. Finally, the data collected during the experiment should be analyzed to determine whether it supports the hypothesis. Following these steps will help to ensure the success of any science fair project.

(Note: Is this article not meeting your expectations? Do you have knowledge or insights to share? Unlock new opportunities and expand your reach by joining our authors team. Click Registration to join us and share your expertise with our readers.)

Hi, I'm Happy Sharer and I love sharing interesting and useful knowledge with others. I have a passion for learning and enjoy explaining complex concepts in a simple way.

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Do a Science Fair Project!

How do you do a science fair project.

Ask a parent, teacher, or other adult to help you research the topic and find out how to do a science fair project about it.

Test, answer, or show?

Your science fair project may do one of three things:

Test an idea (or hypothesis.)

Answer a question.

Show how nature works.

Topic ideas:

Space topics:.

How do the constellations change in the night sky over different periods of time?

How does the number of stars visible in the sky change from place to place because of light pollution?

Learn about and demonstrate the ancient method of parallax to measure the distance to an object, such as stars and planets.

Study different types of stars and explain different ways they end their life cycles.

Earth topics:

How do the phases of the Moon correspond to the changing tides?

Demonstrate what causes the phases of the Moon?

How does the tilt of Earth’s axis create seasons throughout the year?

How do weather conditions (temperature, humidity) affect how fast a puddle evaporates?

How salty is the ocean?

Solar system topics:

How does the size of a meteorite relate to the size of the crater it makes when it hits Earth?

How does the phase of the Moon affect the number of stars visible in the sky?

Show how a planet’s distance from the Sun affects its temperature.

Sun topics:

Observe and record changes in the number and placement of sun spots over several days. DO NOT look directly at the Sun!

Make a sundial and explain how it works.

Show why the Moon and the Sun appear to be the same size in the sky.

How effective are automobile sunshades?

Study and explain the life space of the sun relative to other stars.

Pick a topic.

Try to find out what people already know about it.

State a hypothesis related to the topic. That is, make a cause-and-effect-statement that you can test using the scientific method .

Explain something.

Make a plan to observe something.

Design and carry out your research, keeping careful records of everything you do or see.

Create an exhibit or display to show and explain to others what you hoped to test (if you had a hypothesis) or what question you wanted to answer, what you did, what your data showed, and your conclusions.

Write a short report that also states the same things as the exhibit or display, and also gives the sources of your initial background research.

Practice describing your project and results, so you will be ready for visitors to your exhibit at the science fair.

Follow these steps to a successful science fair entry!

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Parents' Guide to Science Fair Project Vocabulary

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Benefits of Science Fair Projects for Kids

Science fair project terms, choosing a science fair project idea, tips for working with your child.

How can you help your child with her science fair project when you don't understand the many terms used? Read on for some definitions to bring you up to speed, along with thoughts on how working with your child on a science fair project can improve your relationship.

Science fairs are a great way to teach kids to investigate our world. From breakthroughs in our understanding of the biology of cancer to disease outbreaks such as the Zika virus to fears about the Yellowstone supervolcano, these topics are in the news daily.​ Schools have changed remarkably in recent years, and most of these projects require parental input. At the same time, the world has changed, and kids are often learning terms unfamiliar to their parents.

It's not just learning science that's at stake here. Relationships between children and parents are changing. First, we heard about the quality of time versus the quantity, but now that quality time is often threatened by anything with a screen. Doing a science project with your child—with your phones turned off or in another room—is a great opportunity to re-establish or improve your connection.

Even the times when we converse with each other, the topics have changed. The latest media hype or Hollywood antics have replaced some of the more in-depth topics of discussion. With a science project , you may discuss problems that are more meaningful than the last media scare or celebrity slip-up.

For example, how do doctors figure out how a drug works to treat cancer? What happens when you are stung by a mosquito, and why do some people receive more bites than others? How do we know the world isn't flat? How should you behave around a person with autism, and what is life like for that person. What happens to children who are bullied ?

To be an active parent in helping with the project, you'll likely be reading scientific publications. There's no need to panic.

After your child poses a question for her science fair project, she will be asked to generate a hypothesis. If she is experimenting, you will need to identify the dependent and independent variables. If these terms are already leaving you confused, don't fret. Here's a list of the science project terms and definitions you need to know as a parent.

Abstract : A brief summary of your child’s science fair project. An abstract should explain the project concisely, using about 200-250 words.

Analysis : The explanation of the data your child has gathered. The analysis will describe the results of the experiment, what those results proved, whether or not the hypothesis was correct (and why), and what your child learned.

Application : The real-world implications of what an experiment discovered. In other words, how that information can be used to make changes to how something is done.

Conclusion : The answer to the initial question posed by your child’s science fair project. The conclusion sums everything up.

Control : The component or variable of the experiment in which nothing changes or is changed.

Data : Data is information, specifically, the information gathered before, during, and after an experiment that is used to reach a conclusion.

Dependent Variable : The dependent variable is the component or piece of the experiment that changes based on the independent variable.

Display Board : The free-standing cardboard, typically trifold, on which your child will display information about his science fair project. The display board is how the general public will learn about his experiment.

Graph : A chart that visually displays the data of the experiment. It can be a numbered grid or a spreadsheet.

Hypothesis : The “educated guess” as to what will happen during a science experiment when certain variables are introduced or changed. It is a prediction of the answer to the question posed by the science fair project.

Independent Variable : The piece or component of the experiment that is changed while everything else stays the same. The independent variable tests the “what if’s” of the project.

Log : A scientific log is a written account of what happened moment by moment (or day by day, depending on the project) throughout the project/experiment.

Procedure :   The step-by-step directions of how to experiment. The procedure should be clear enough that anyone who reads it can replicate the experiment.

Purpose (Problem) : The question the science project sets out to prove or test.

Science Project Proposal : A brief description of a proposed science fair project. The proposal should include the problem, the hypothesis, and the procedure. It sometimes will include an explanation of the independent and dependent variables and a material list as well.

Scientific Method : An organized manner of discovering something, the scientific method must be followed to make a project valid. The scientific method has six steps: Observation, Question, Hypothesis, Experimentation, Analysis, and Conclusion.

If your child is still brainstorming an idea for their project, how can you help? You may best capture her interest if you look at topics that are being researched today. The field of immunotherapy, for example, can be fascinating as you look at how doctors are using our immune systems to fight cancer.

Or perhaps you can re-ask one of those challenging questions your child asked when younger. How far does space go? Looking at something such as this allows you to let your child know how special she is by recalling things she said long ago.

Another idea may be a question someone in your family has asked. Why do some people need allergy shots, and how do they work? What exactly is an allergy? Why do so many kids have peanut allergies these days, and should peanuts be banned from schools?

There are many ideas for science fair projects online. The key is to make the project something your child is interested in researching, rather than you.

If you think of the importance of communication with your child, you would think that parents would be required to take classes. For example, nurses are instructed on communication techniques because of the importance of patient-health professional interaction. Those in sales are taught a multitude of methods for understanding people.

And those in management? A glance online reveals seminars galore on how to communicate. Yet parents, as the primary influence in the life of a precious child, are taught little. Your science fair project, however, can give you a chance to practice!

You may want to begin by learning some of the mistakes parents make when talking to kids. Perhaps the most important mistake is to allow kids to finish what they are saying. Be comfortable with moments of silence. Let your child work through problems before giving her your answer.

Avoid focusing on the grade and instead focus on what your child can learn. Yet if your child is excited about going for an "A" go along with her goal. To be prepared ahead of time for frustrating moments, think about the traits and habits of good parents .

A Word From Verywell

We've shared the definitions of common science fair terms so you can help your child on her science fair project. The reason being is that working together on science fair projects is a great way for a parent and child to focus on a task as a team and practice communication skills.

If you view the project as an opportunity to improve communication with your child, you may feel a bit less frustrated when the project becomes—as many parents would agree—a much more significant undertaking than anticipated.

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Here's the Secret to *Really* Understanding Your Science Fair Results

How to Use Stats to Stand Out at the Science Fair

If you want to win your science fair, statistically analyzing your data is a great way to stand out from the competition, but when you get the result – say P = 0.04 – what does it actually mean ? You can do all the math from the first part of this post , but if you don’t truly understand the numbers statistical tests return, you still don’t really know what your experiment found.

For example: Can you reject the “ null hypothesis ” based on your result? What does that even mean? Is it possible your finding is due to chance? What does a correlation tell you about the relationship between two variables? These are the types of questions you’ll need to answer to get the interpretation of your science fair results right.

The Null Hypothesis

Whenever you do statistics, you’re pitting the “null hypothesis” against your “experimental hypothesis.” The null hypothesis is always basically the same: There is no relationship between the things you’re testing. In scientific experiments, you assume the null hypothesis is true until you have sufficient evidence to refute it. In other words, you don't assume you'll get a certain result from your experiments — you assume your hypothesis isn't true until the scientific results tell you otherwise.

Confused? Here's an example. Say you're doing a science project to find out if dogs are right- or left-handed. Your null hypothesis might be that dogs have no dominant paw. From there, your results will tell you whether your null hypothesis is true, or whether dogs seem to be right- or left-handed.

But how can you tell the difference between real results and what might happen by pure chance? Statistics, of course!

Determining what evidence is “sufficient” is the job of statistical tests, and because you’re testing the null hypothesis, it’s best to define exactly what it is for your experiment. You should really do this before you start your work, but even if you’ve focused on your experimental hypothesis (the relationship you suspect might actually exist) it’s easy to put together a null hypothesis after the fact.

P Values and Statistical Significance

If your experiment gives you sufficient cause to reject the null hypothesis, this is called a “statistically significant” result. But, as with most things in science, there is a very specific definition of what this actually means, and you should be clear about it when you’re looking at your science fair results. The definition comes down to the meaning of the P value you get from your statistical test.

The P value is often misinterpreted to mean “the probability that the result is due to chance,” and although this is close to the meaning it is not actually true . The P value instead tells you the chance that, if the null hypothesis was true, you would obtain your result due to random statistical noise. For example, if you were testing whether a coin was unevenly weighted (with a null hypothesis that it is a fair coin), a result of 45 heads to 55 tails would be fairly likely from flipping a fair coin due to general statistical variation, and this is what the P value quantifies.

The “significance level” is a cut-off value for P – anything below this is considered sufficiently unlikely for you to reject the null hypothesis. This is usually chosen as P = 0.05 (so there would only be a 5% chance that your results would be obtained in a world where the null hypothesis was true), but ultimately this is just a convention. In some circumstances, a significance level of P = 0.10 is perfectly fine, and in others, scientists “raise the bar” a little and set a more strict cut-off of P = 0.01. It’s usually best to just stick to P = 0.05, but understand that there's variation sometimes.

Interpreting Correlations

If you’re testing for a difference between two groups, understanding the meaning of statistical significance is enough, but if your test involves correlations between two variables (for example, the amount of light a plant receives and how tall it grows, or the number of previous attempts and your score at a game), things are a little bit different. Tests for correlations return values between −1 and +1, and understanding these and what either type of correlation implies for causality is essential to interpreting your results.

Firstly, the correlation score is easy to understand if you consider the extreme cases. Any positive correlation value means that both variables increase together , and a value of +1 is a perfect correlation, where the graph of one variable against another is straight line. In the same way, any minus correlation value means that when one variable increases, the other decreases, and a value of −1 is a perfect negative correlation. Finally, a value of 0 means there is no correlation at all. Of course, most results will be a decimal (like 0.65), with larger values (higher numbers, either positive or negative) meaning a stronger correlation.

However, a key caveat is that correlation does not imply causation . In other words, just because two things are correlated doesn’t mean that one causes the other, and you shouldn’t be tempted to draw such a conclusion in your writeup on the basis of a correlation alone. A good example is a correlation between yellow teeth and lung cancer: It isn’t that yellow teeth cause lung cancer; it’s that smoking causes both yellow teeth and lung cancer. In the same way, your results could be due to another factor you haven’t considered, so it’s always risky to make causal claims without very strong evidence beyond a simple correlation.

With these points in mind, whatever your science fair project, you should be able to do the statistics you need to and explain exactly what they show. You might not win, but what you’ve learned gives you the tools you need to really get the judges’ attention.

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About the Author

Lee Johnson is a freelance writer and science enthusiast, with a passion for distilling complex concepts into simple, digestible language. He's written about science for several websites including eHow UK and WiseGeek, mainly covering physics and astronomy. He was also a science blogger for Elements Behavioral Health's blog network for five years. He studied physics at the Open University and graduated in 2018.

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Weather Science Fair

What are some good ideas for science fair projects.

How do clouds and cloud formation relate to weather patterns?

• How does weather affect human emotion

Is weather related to local crop harvests?

Projects that test a hypothesis

Here are some suggestions for projects to test a hypothesis. We give one possible hypothesis for each project, but many others are also possible. After stating your hypothesis, design and carry out an experiment to test it.

How does the temperature change during the day?

One possible hypothesis: The temperature is lowest at midnight and highest at high noon.

What is the difference between the temperature in direct sun and in the shade? Is the difference always the same?

One possible hypothesis: The temperature in the shade is at least 10° F but no more than 20° F cooler than in the sun.

One possible hypothesis: The meteorologist on Channel 3 is more often right than the meteorologist on Channel 7.

How does weather affect human emotion?

One possible hypothesis: People are more often sad, depressed, or moody on cloudy days than on sunny days.

Does weather affect test scores? Should teachers give tests on rainy days so students perform better?

One possible hypothesis: Students do better on tests on rainy days than on sunny days.

You could test this hypothesis by giving a timed test such as a math addition or multiplication test to the same people on very different weather days. Do they score better or faster in good or bad weather? You could also look at any data on SAT scores, usually required for entrance to a university, to see if any research has been done correlating them to weather conditions.

Projects that review what we already know

Here are some suggestions for research projects to find out how much scientists already know about these things.

What are clouds made of? What are the different kinds of clouds and how are they different?

What causes the wind to blow? Are hurricanes and tornadoes just high winds?

How are tornados formed and what causes them?

What causes hail? Why are some hailstones larger than others?

Does solar activity such as sunspots or coronal mass ejections affect weather on Earth?

Can you outrun a typical tornado or hurricane as it moves across the earth? Should you try? Why or why not?

Is air pressure related to weather as some aneroid barometers suggest with the words that are printed on them?

Projects that find relationships in the data

These suggested projects will require you to make observations (which may require reading the data reports of others) and try to see patterns or relationships in the data.

What are your local rainfall patterns and how do they compare to other parts of the country?

What are the common wind patterns in your area and why?

How do yearly rainfall statistics in your area compare with corresponding numbers of traffic accidents or fatalities?

How do yearly rainfall statistics in your area compare with corresponding forest fires or brush fires?

Is weather related to illnesses? Are there more colds and flu when the weather is cold and damp?

Using an aneroid barometer and cloud types see how well you can predict the general weather using some simple observations: High thin cirrus clouds usually precede a storm the day before. Falling barometers often mean rain is on the way. High pressure often indicates clearing skies. Puffy cottonball looking cumulus clouds are often fair weather clouds. In certain times of the year patterns might exist such as the weather to the northwest often heads your way in the winter or in summer the weather in the southwest often heads your way. Did you predict the weather as well as the local forecasters?

Engineering design project

Design and build an automatic recording weather device. Test it over time.

Hypothesis Definition (Science)

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A hypothesis is an explanation that is proposed for a phenomenon. Formulating a hypothesis is a step of the scientific method .

Alternate Spellings: plural: hypotheses

Examples: Upon observing that a lake appears blue under a blue sky, you might propose the hypothesis that the lake is blue because it is reflecting the sky. One alternate hypothesis would be that the lake is blue because water is blue.

Hypothesis Versus Theory

Although in common usage the terms hypothesis and theory are used interchangeably, the two words mean something different from each other in science. Like a hypothesis, a theory is testable and may be used to make predictions. However, a theory has been tested using the scientific method many times. Testing a hypothesis may, over time, lead to the formulation of a theory.

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Experiment Definition in Science – What Is a Science Experiment?

In science, an experiment is simply a test of a hypothesis in the scientific method . It is a controlled examination of cause and effect. Here is a look at what a science experiment is (and is not), the key factors in an experiment, examples, and types of experiments.

Experiment Definition in Science

By definition, an experiment is a procedure that tests a hypothesis. A hypothesis, in turn, is a prediction of cause and effect or the predicted outcome of changing one factor of a situation. Both the hypothesis and experiment are components of the scientific method. The steps of the scientific method are:

• Make observations.
• Ask a question or identify a problem.
• State a hypothesis.
• Perform an experiment that tests the hypothesis.
• Based on the results of the experiment, either accept or reject the hypothesis.
• Draw conclusions and report the outcome of the experiment.

Key Parts of an Experiment

The two key parts of an experiment are the independent and dependent variables. The independent variable is the one factor that you control or change in an experiment. The dependent variable is the factor that you measure that responds to the independent variable. An experiment often includes other types of variables , but at its heart, it’s all about the relationship between the independent and dependent variable.

Examples of Experiments

Fertilizer and plant size.

For example, you think a certain fertilizer helps plants grow better. You’ve watched your plants grow and they seem to do better when they have the fertilizer compared to when they don’t. But, observations are only the beginning of science. So, you state a hypothesis: Adding fertilizer increases plant size. Note, you could have stated the hypothesis in different ways. Maybe you think the fertilizer increases plant mass or fruit production, for example. However you state the hypothesis, it includes both the independent and dependent variables. In this case, the independent variable is the presence or absence of fertilizer. The dependent variable is the response to the independent variable, which is the size of the plants.

Now that you have a hypothesis, the next step is designing an experiment that tests it. Experimental design is very important because the way you conduct an experiment influences its outcome. For example, if you use too small of an amount of fertilizer you may see no effect from the treatment. Or, if you dump an entire container of fertilizer on a plant you could kill it! So, recording the steps of the experiment help you judge the outcome of the experiment and aid others who come after you and examine your work. Other factors that might influence your results might include the species of plant and duration of the treatment. Record any conditions that might affect the outcome. Ideally, you want the only difference between your two groups of plants to be whether or not they receive fertilizer. Then, measure the height of the plants and see if there is a difference between the two groups.

Salt and Cookies

You don’t need a lab for an experiment. For example, consider a baking experiment. Let’s say you like the flavor of salt in your cookies, but you’re pretty sure the batch you made using extra salt fell a bit flat. If you double the amount of salt in a recipe, will it affect their size? Here, the independent variable is the amount of salt in the recipe and the dependent variable is cookie size.

Test this hypothesis with an experiment. Bake cookies using the normal recipe (your control group ) and bake some using twice the salt (the experimental group). Make sure it’s the exact same recipe. Bake the cookies at the same temperature and for the same time. Only change the amount of salt in the recipe. Then measure the height or diameter of the cookies and decide whether to accept or reject the hypothesis.

Examples of Things That Are Not Experiments

Based on the examples of experiments, you should see what is not an experiment:

• Making observations does not constitute an experiment. Initial observations often lead to an experiment, but are not a substitute for one.
• Making a model is not an experiment.
• Neither is making a poster.
• Just trying something to see what happens is not an experiment. You need a hypothesis or prediction about the outcome.
• Changing a lot of things at once isn’t an experiment. You only have one independent and one dependent variable. However, in an experiment, you might suspect the independent variable has an effect on a separate. So, you design a new experiment to test this.

Types of Experiments

There are three main types of experiments: controlled experiments, natural experiments, and field experiments,

• Controlled experiment : A controlled experiment compares two groups of samples that differ only in independent variable. For example, a drug trial compares the effect of a group taking a placebo (control group) against those getting the drug (the treatment group). Experiments in a lab or home generally are controlled experiments
• Natural experiment : Another name for a natural experiment is a quasi-experiment. In this type of experiment, the researcher does not directly control the independent variable, plus there may be other variables at play. Here, the goal is establishing a correlation between the independent and dependent variable. For example, in the formation of new elements a scientist hypothesizes that a certain collision between particles creates a new atom. But, other outcomes may be possible. Or, perhaps only decay products are observed that indicate the element, and not the new atom itself. Many fields of science rely on natural experiments, since controlled experiments aren’t always possible.
• Field experiment : While a controlled experiments takes place in a lab or other controlled setting, a field experiment occurs in a natural setting. Some phenomena cannot be readily studied in a lab or else the setting exerts an influence that affects the results. So, a field experiment may have higher validity. However, since the setting is not controlled, it is also subject to external factors and potential contamination. For example, if you study whether a certain plumage color affects bird mate selection, a field experiment in a natural environment eliminates the stressors of an artificial environment. Yet, other factors that could be controlled in a lab may influence results. For example, nutrition and health are controlled in a lab, but not in the field.
• Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
• Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments. Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Holland, Paul W. (December 1986). “Statistics and Causal Inference”.  Journal of the American Statistical Association . 81 (396): 945–960. doi: 10.2307/2289064
• Stohr-Hunt, Patricia (1996). “An Analysis of Frequency of Hands-on Experience and Science Achievement”. Journal of Research in Science Teaching . 33 (1): 101–109. doi: 10.1002/(SICI)1098-2736(199601)33:1<101::AID-TEA6>3.0.CO;2-Z