The two main variables in a science experiment are the independent variable and the dependent variable. Here's the definition on independent variable and a look at how it's used:

### Key Takeaways: Independent Variable

- The independent variable is the factor that you purposely change or control in order to see what effect it has.
- The variable that responds to the change in the independent variable is called the dependent variable. It depends on the independent variable.
- The independent variable is graphed on the x-axis.

## Independent Variable Definition

An independent variable is defines as the variable that is changed or controlled in a scientific experiment. It represents the cause or reason for an outcome.

Independent variables are the variables that the experimenter changes to test their dependent variable. A change in the independent variable directly causes a change in the dependent variable. The effect on the dependent variable is measured and recorded.

**Common Misspellings: **independant variable

## Independent Variable Examples

- A scientist is testing the effect of light and dark on the behavior of moths by turning a light on and off. The independent variable is the amount of light and the moth's reaction is the dependent variable.
- In a study to determine the effect of temperature on plant pigmentation, the independent variable (cause) is the temperature, while the amount of pigment or color is the dependent variable (the effect).

## Graphing the Independent Variable

When graphing data for an experiment, the independent variable is plotted on the x-axis, while the dependent variable is recorded on the y-axis. An easy way to keep the two variables straight is to use the acronym DRY MIX, which stands for:

- Dependent variable that Responds to change goes on the Y axis
- Manipulated or Independent variable goes on the X axis

## Practice Identifying the Independent Variable

Students are often asked to identify the independent and dependent variable in an experiment. The difficulty is that the value of both of these variables can change. It's even possible for the dependent variable to remain unchanged in response to controlling the independent variable.

**Example**: You're asked to identify the independent and dependent variable in an experiment looking to see if there is a relationship between hours of sleep and student test scores.

There are two ways to identify the independent variable. The first is to write the hypothesis and see if it makes sense:

- Student test scores have no effect on the number of hours the students sleeps.
- The number of hours students sleep have no effect on their test scores.

Only one of these statements makes sense. This type of hypothesis is constructed to state the independent variable followed by the predicted impact on the dependent variable. So, the number of hours of sleep is the independent variable.

The other way to identify the independent variable is more intuitive. Remember, the independent variable is the one the experimenter controls to measures its effect on the dependent variable. A researcher can control the number of hours a student sleeps. On the other hand, the scientist has no control on the students' test scores.

The independent variable always changes in an experiment, even if there is just a control and an experimental group. The dependent variable may or may not change in response to the independent variable. In the example regarding sleep and student test scores, it's possible the data might show no change in test scores, no matter how much sleep students get (although this outcome seems unlikely). The point is that a researcher *knows* the values of the independent variable. The value of the dependent variable is *measured*.

## Sources

- Babbie, Earl R. (2009).
*The Practice of Social Research*(12th ed.). Wadsworth Publishing. ISBN 0-495-59841-0. - Dodge, Y. (2003).
*The Oxford Dictionary of Statistical Terms*. OUP. ISBN 0-19-920613-9. - Everitt, B. S. (2002).
*The Cambridge Dictionary of Statistics*(2nd ed.). Cambridge UP. ISBN 0-521-81099-X. - Gujarati, Damodar N.; Porter, Dawn C. (2009). "Terminology and Notation".
*Basic Econometrics*(5th international ed.). New York: McGraw-Hill. p. 21. ISBN 978-007-127625-2. - Shadish, William R.; Cook, Thomas D.; Campbell, Donald T. (2002).
*Experimental and quasi-experimental designs for generalized causal inference*. (Nachdr. ed.). Boston: Houghton Mifflin. ISBN 0-395-61556-9.