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• Theoretically, the test results depend on breakfast, so the test results are the dependent variable.
• The slope tells us how the dependent variable (\(y\)) changes for every one unit increase in the independent (\(x\)) variable, on average.
• In some ways, this experiment resembles the one with breakfast and test scores.
• An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects.

The factor under the experimenter’s control is the presence or absence of breakfast, so you know it is the independent variable. The experiment measures test scores of students who ate breakfast versus those who did not. Theoretically, the test results depend on breakfast, so the test results are the dependent variable.

This would have put both independent and dependent variables into a real life, practical context. Boyle was then able to devise his equation based on his observations of the independent and dependent variables. 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 statistics, the most often used word is ‘variable’ which refers to a characteristic that contains the value, which may vary from one entity to another. It is similar to the variables used in other disciplines like science and mathematics.

Example of an Independent and Dependent Variable

Observational and some quasi-experimental studies lack active interventions – their independent variables are not specifically imposed by the investigators. While these studies cannot tell us whether one variable causes changes, they can tell us how strong a relationship exists between variables. Some non-experimental studies also have independent variables, but they may not be determined or manipulated by the investigators. There can be multiple dependent variables for one independent variable. In a scientific experiment, the independent variables are controlled or changed whereas the dependent variables tend to be measured and tested.

• Generally, the independent variable goes on the x-axis (horizontal) and the dependent variable on the y-axis (vertical).
• The independent variable (\(x\)) is the number of hours Svetlana tutors each session.
• Based on your results, you note that the placebo and low-dose groups show little difference in blood pressure, while the high-dose group sees substantial improvements.
• Usually this type of confounding variable is avoided by randomly assigning subjects to groups, so not all of one kind of subject goes into one group.

Statology is a site that makes learning statistics easy by explaining topics in simple and straightforward ways. Scoring well on standardized tests is an important part of having a strong college application. You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment. If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples. You plot bars for each treatment group before and after the treatment to show the difference in blood pressure. Here is an overview of mixed costs, and creating a scattergraph to test our theory of the behavior of the costs.

So here the dependent variable is height, while the independent variable is the age, which is going to change on its own. Hence, here the height of the boy is shown on the y-axis, whereas x-axis indicates the age. The dependence of the former on the latter is being examined by the statistical models.

Understanding Independent and Dependent Variables

These terms are especially used in statistics, where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable. You measure the math skills of all participants using a standardized test and check whether they differ based on room temperature. An example is provided by the analysis of trend in sea level by Woodworth (1987).

A variable may be thought to alter the dependent or independent variables, but may not actually be the focus of the experiment. So that the variable will be kept constant or monitored to try to minimize its effect on the experiment. Such variables may be designated as either a “controlled variable”, “control variable”, or “fixed variable”. For another experiment, a scientist wants to determine whether one drug is more effective than another at controlling high blood pressure.

Independent vs Dependent Variable Key Takeaways

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. For each of the independent variables above, it’s clear that they can’t be changed by other variables in the experiment. Another way to think of independent variables, particularly in the context of functions, is that the independent variable is the input value of a function, commonly denoted as x. The independent variable is not affected by any other variable, hence its name.

Visualizing independent and dependent variables

A dependent variable is a consequence of an independent variable i.e. it is variable that measures the effect of the independent variable on the test units. When we create a graph, the independent variable will go on the x-axis and the dependent variable will go on the y-axis. A researcher changes the version of a study guide given to students to see how it affects exam scores. Changing (independent variable) affects the value of (dependent variable). The independent variable (\(x\)) is the number of hours Ethan works each visit. The dependent variable (\(y\)) is the amount, in dollars, Ethan earns for each visit.

An independent variable is the one that does not rely on anything else and hence can be manipulated, while the dependent shows the effect, of changes made to the independent variable. The independent variable (sometimes known what is credit card balance as the manipulated variable) is the variable whose change isn’t affected by any other variable in the experiment. There’s nothing you or anything else can do to speed up or slow down time or increase or decrease age.

For example, say you have ten sunflower seedlings, and you decide to give each a different amount of water each day to see if that affects their growth. The independent variable here would be the amount of water you give the plants, and the dependent variable is how tall the sunflowers grow. For each of the independent variables above, it’s clear that they can’t be changed by other variables in the experiment. An example of a dependent variable is how tall you are at different ages. The dependent variable (height) depends on the independent variable (age).

If you’re studying how different types of fertilizer affect how tall plants grow, the variables are type of fertilizer and plant height. Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.

Compare your paper to billions of pages and articles with Scribbr’s Turnitin-powered plagiarism checker. While one group gets the placebo, the other group gets the medication that is intended to have therapeutic value. Ideally, the medication should help patients with whatever it is intended to treat.

how to tell whether x and y are independent or not

In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ. The independent variable, x, is some value we choose, or manipulate, to determine the value of the dependent variable. There is no way for f(x) to affect x, but any change in x affects f(x). This is the relationship between dependent and independent variables.

Difference Between Independent and Dependent Variables

A variable is extraneous only when it can be assumed (or shown) to influence the dependent variable. This effect is called confounding or omitted variable bias; in these situations, design changes and/or controlling for a variable statistical control is necessary. In the context of a function, the independent variables are the inputs to the function and the dependent variables are the outputs of the function. If you write out the variables in a sentence that shows cause and effect, the independent variable causes the effect on the dependent variable.

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