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Rather than starting from scratch in putting together a plan for answering your question, you want to be a savvy scientist using library and Internet research to help you find the best way to do things and ensure that you don't repeat mistakes from the past.

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The scientific method starts when you ask a question about something that you observe: How, What, When, Who, Which, Why, or Where?

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A hypothesis is an educated guess about how things work. It is an attempt to answer your question with an explanation that can be tested.

A good hypothesis allows you to then make a prediction:
"If _____[I do this] _____, then _____[this]_____ will happen."

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State both your hypothesis and the resulting prediction you will be testing. Predictions must be easy to measure.

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In the best experiments, the scientist must be able to measure the values for each variable. Weight or mass is an example of a variable that is very easy to measure. However, imagine trying to do an experiment where one of the variables is love. There is no such thing as a "love-meter." You might have a belief that someone is in love, but you cannot really be sure, and you would probably have friends that do not agree with you. So, love is not measurable in a scientific sense; therefore, it would be a poor variable to use in an experiment.

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For detailed help with this step, use these resources:

Background Research Plan

Finding Information

How to Write a Bibliography in APA and MLA styles With Examples

Research Paper

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Your Question

Laboratory Notebook

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For detailed help with this step, use these resources:

Variables

Variables for Beginners

Writing a Hypothesis for Your Science Fair Project

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For a science fair project some teachers require that the question be something you can measure, preferably with a number.

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Test Your Hypothesis by Doing an Experiment

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Test Your Hypothesis by Doing an Experiment

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Your experiment tests whether your prediction is accurate and thus your hypothesis is supported or not. It is important for your experiment to be a fair test. You conduct a fair test by making sure that you change only one factor at a time while keeping all other conditions the same.

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You should also repeat your experiments several times to make sure that the first results weren't just an accident.

Repeating a science experiment is an important step to verify that your results are consistent and not just an accident.

For a typical experiment, you should plan to repeat it at least three times (more is better).

If you are doing something like growing plants, then you should do the experiment on at least three plants in separate pots (that's the same as doing the experiment three times).

If you are doing an experiment that involves testing or surveying different groups, you won't need to repeat the experiment three times, but you will need to test or survey a sufficient number of participants to insure that your results are reliable. You will almost always need many more than three participants! See our Science Buddies resource, How Many Survey Participants Do I Need?

If your experiment involves machine learning, you won't need to repeat it three times. Machine learning models go through many rounds of training, called iterations, to learn from the data. These built-in iterations and tests are enough to check that your results are reliable but make sure to explain why this process works in your project.

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For detailed help with this step, use these resources:

Experimental Procedure

Materials List

Conducting an Experiment

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Example: Materials List & Experimental Procedure

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Example: Materials List & Experimental Procedure

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Materials List

CD player & a CD (low drain device)

Three identical flashlights (medium drain device)

Camera flash (high drain device)

AA size Duracell and Energizer batteries

AA size of a "heavy-duty" (non-alkaline) battery (I used Panasonic)

Voltmeter & a AA battery holder

Kitchen timer

Experimental Procedure

1. Number each battery so you can tell them apart.

2. Measure each battery's voltage by using the voltmeter.

3. Put the same battery into one of the devices and turn it on.

4. Let the device run for thirty minutes before measuring its voltage again. (Record the voltage in a table every time it is measured.)

5. Repeat step 4 until the battery is at 0.9 volts or until the device stops.

6. Do steps 1–5 again, three trials for each brand of battery in each experimental group.

7. For the camera flash push the flash button every 30 seconds and measure the voltage every 5 minutes.

8. For the flashlights rotate each battery brand so each one has a turn in each flashlight.

9. For the CD player repeat the same song at the same volume throughout the tests.

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Sample Size: How Many Survey Participants Do I Need?

To have confidence that your survey results are representative, it is critically important that you have a large number of randomly selected participants in each group you survey.

https://clincalc.com/stats/samplesize.aspx

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Determines the minimum number of subjects for adequate study power

ClinCalc.com » Statistics » Sample Size Calculator

Study Group Design

vs.
Two independent
study groups
vs.
One study group
vs. population

Two study groups will each receive different treatments.

Primary Endpoint

Dichotomous
(yes/no)

Continuous
(means)

The primary endpoint is binomial - only two possible outcomes.
Eg, mortality (dead/not dead), pregnant (pregnant/not)

Statistical Parameters

Anticipated Incidence

Known population | %
Study group | %

Anticipated Mean

Known population | ±
Study group |

Type I/II Error Rate

Alpha |
Power |

Press 'Calculate' to view calculation results.

About This Calculator

This calculator uses a number of different equations to determine the minimum number of subjects that need to be enrolled in a study in order to have sufficient statistical power to detect a treatment effect.¹

Before a study is conducted, investigators need to determine how many subjects should be included. By enrolling too few subjects, a study may not have enough statistical power to detect a difference (type II error). Enrolling too many patients can be unnecessarily costly or time-consuming.

Generally speaking, statistical power is determined by the following variables:

Baseline Incidence: If an outcome occurs infrequently, many more patients are needed in order to detect a difference.

Population Variance: The higher the variance (standard deviation), the more patients are needed to demonstrate a difference.

Treatment Effect Size: If the difference between two treatments is small, more patients will be required to detect a difference.

Alpha: The probability of a type-I error -- finding a difference when a difference does not exist. Most medical literature uses an alpha cut-off of 5% (0.05) -- indicating a 5% chance that a significant difference is actually due to chance and is not a true difference.

Beta: The probability of a type-II error -- not detecting a difference when one actually exists. Beta is directly related to study power (Power = 1 - β). Most medical literature uses a beta cut-off of 20% (0.2) -- indicating a 20% chance that a significant difference is missed.

Post-Hoc Power Analysis

To calculate the post-hoc statistical power of an existing trial, please visit the post-hoc power analysis calculator.

References and Additional Reading

1. Rosner B. Fundamentals of Biostatistics. 7th ed. Boston, MA: Brooks/Cole; 2011.

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Press 'Calculate' to view calculation results. This calculator uses a number of different equations to determine the minimum number of subjects that need to be enrolled in a study in order to have sufficient statistical power to detect a treatment effect. Before a study is conducted, investigators need to determine how many subjects should be included.

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Eleven short guides to basic statistical concepts, designed for journalists and others who did not major in math.

Mean: Let's get started.

Median: How to find out how the "average Joe" is doing.

Mode: Who's popular?

Percent: Looking for changes.

Per capita and Rates: When an increase is really a decrease, and other ways people can use numbers to trick you.

Standard Deviation and Normal Distribution: A quick look at the King of Stats.

Survey Sample Sizes and Margin of Error: How not to get suckered by polls and other research.

Regression Analysis: It's all about relationships.

Data Analysis: How to tell if these numbers are really worth writing about anyway.

Statistical Tests: How do I pick the correct statistical test?

Student's T: Is your sample relevant to the larger population it is supposed to represent?

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The independent variable is the one that is changed by the scientist. Why just one? Well, if you changed more than one variable it would be hard to figure out which change is causing what you observe. For example, what if our scientific question was: "How does the size of a dog affect how much food it eats?"; then, during your feeding experiments you changed both the size of the dog and the time of day the dogs were fed. The data might get a bit confusing— did the larger dog eat less food than the smaller dog because of his size or because it was the middle of the day and dogs prefer to eat more in the morning?

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The dependent variables are the things that the scientist focuses his or her observations on to see how they respond to the change made to the independent variable. In our dog example, the dependent variable is how much the dogs eat. This is what we are observing and measuring. It is called the "dependent" variable because we are trying to figure out whether its value depends on the value of the independent variable. If there is a direct link between the two types of variables (independent and dependent) then you may be uncovering a cause and effect relationship. The number of dependent variables in an experiment varies, but there can be more than one.

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Experiments also have controlled variables. Controlled variables are quantities that a scientist wants to remain constant, and she or he must observe them as carefully as the dependent variables. For example, in the dog experiment example, you would need to control how hungry the dogs are at the start of the experiment, the type of food you are feeding them, and whether the food was a type that they liked. Why? If you did not, then other explanations could be given for differences you observe in how much they eat. For instance, maybe the little dog eats more because it is hungrier that day, maybe the big dog does not like the dog food offered, or maybe all dogs will eat more wet dog food than dry dog food. So, you should keep all the other variables the same (you control them) so that you can see only the effect of the one variable (the independent variable) that you are trying to test. Similar to our example, most experiments have more than one controlled variable. Some people refer to controlled variables as "constant variables."

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What are Variables?

What are Variables?

In science, a variable is any factor, trait, or condition that can exist in differing amounts or types.

Scientists try to figure out how the natural world works.To do this they use experiments to search for cause and effect relationships. Cause and effect relationships explain why things happen and allow you to reliably predict the outcomes of an action. Scientists use the scientific method to design an experiment so that they can observe or measure if changes to one thing cause something else to vary in a repeatable way.

These factors that change in a scientific experiment are variables.

A properly designed experiment usually has three kinds of variables: independent, dependent, and controlled.

What is an Independent Variable?

The independent variable is the one that is changed by the scientist. Why just one? Well, if you changed more than one variable it would be hard to figure out which change is causing what you observe. For example, what if our scientific question was: "How does the size of a dog affect how much food it eats?"; then, during your feeding experiments you changed both the size of the dog and the time of day the dogs were fed. The data might get a bit confusing- did the larger dog eat less food than the smaller dog because of his size or because it was the middle of the day and dogs prefer to eat more in the morning?

Sometimes it is impossible to just change one variable, and in those cases, scientists rely on more-complicated mathematical analysis and additional experiments to try to figure out what is going on. Older students are invited to read more about that in our Experimental Design for Advanced Science Projects page. To be clear though, for a science fair, it is usually wise to have only one independent variable at a time. If you are new to doing science projects and want to know the effect of changing multiple variables, do multiple tests where you focus on one independent variable at a time.

What is a Dependent Variable?

The dependent variables are the things that the scientist focuses his or her observations on to see how they respond to the change made to the independent variable. In our dog example, the dependent variable is how much the dogs eat. This is what we are observing and measuring. It is called the "dependent" variable because we are trying to figure out whether its value depends on the value of the independent variable. If there is a direct link between the two types of variables (independent and dependent) then you may be uncovering a cause and effect relationship. The number of dependent variables in an experiment varies, but there can be more than one.

What is a Control Variable?

Experiments also have controlled variables. Controlled variables are quantities that a scientist wants to remain constant, and she or he must observe them as carefully as the dependent variables. For example, in the dog experiment example, you would need to control how hungry the dogs are at the start of the experiment, the type of food you are feeding them, and whether the food was a type that they liked. Why? If you did not, then other explanations could be given for differences you observe in how much they eat. For instance, maybe the little dog eats more because it is hungrier that day, maybe the big dog does not like the dog food offered, or maybe all dogs will eat more wet dog food than dry dog food. So, you should keep all the other variables the same (you control them) so that you can see only the effect of the one variable (the independent variable) that you are trying to test. Similar to our example, most experiments have more than one controlled variable. Some people refer to controlled variables as "constant variables."

In the best experiments, the scientist must be able to measure the values for each variable. Weight or mass is an example of a variable that is very easy to measure. However, imagine trying to do an experiment where one of the variables is love. There is no such thing as a "love-meter." You might have a belief that someone is in love, but you cannot really be sure, and you would probably have friends that do not agree with you. So, love is not measurable in a scientific sense; therefore, it would be a poor variable to use in an experiment.

Examples

Examples of Variables

Time as an Example of an Independent Variable

In some experiments, time is what causes the dependent variable to change. The scientist simply starts the process, then observes and records data at regular intervals.

The Independent Variable for Surveys and Tests of Different Groups

When a scientist performs a test or survey on different groups of people or things, those groups define the independent variable. For example:

Either/Or (Binary) Variables

Sometimes a variable simply represents an either/or (binary) condition. For example, something might be either present or not present during an experiment.

Sample

Sample

Here is a sample containing the variables and hypothesis.

Checklist

Science Fair Project Variables Checklist

Assign these with Google Classroom:

Explore Our Science Videos

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How to use dependent, independent, and controlled variables in your science experiments.

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Doing a Fair Test

It is important for an experiment to be a fair test. You conduct a fair test by making sure that you change one factor at a time while keeping all other conditions the same.

For example, let's imagine that we want to measure which is the fastest toy car to coast down a sloping ramp. If we gently release the first car, but give the second car a push start, did we do a fair test of which car was fastest? No! We gave the second car an unfair advantage by pushing it to start. That's not a fair test! The only thing that should change between the two tests is the car; we should start them down the ramp in exactly the same way.

Let's pretend we're doing an experiment to see if fertilizer makes a plant grow to be larger than a plant that doesn't receive fertilizer. We put seeds of the same kind in three pots with fertilizer and rich soil. But, we run out of soil so we put the seeds without fertilizer in three pots filled with sand. We put all six pots in the same location and water each one with the same amount of water every other day. The plants with soil and fertilizer grow to be much larger than the ones grown in sand without fertilizer. Is that a fair test of whether fertilizer makes a plant grow to be larger? No! We changed two things (type of soil and fertilizer) so we have no idea whether the plants with fertilizer grew to be larger because of the fertilizer or whether the other plants were stunted by being grown in sand. It wasn't a fair test! All of the plants should have been in the same kind of soil.

Conducting a fair test is one of the most important ingredients of doing good, scientifically valuable experiments. To insure that your experiment is a fair test, you must change only one factor at a time while keeping all other conditions the same.

Scientists call the changing factors in an experiment variables.

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Doing a Fair Test: Variables for Beginners

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