# Confounding Variable Examples

Confounding Variable

A confounding variable is an outside influence that changes the effect of a dependent and independent variable. This extraneous influence is used to influence the outcome of an experimental design. Simply, a confounding variable is an extra variable entered into the equation that was not accounted for. Confounding variables can ruin an experiment and produce useless results. They suggest that there are correlations when there really are not. In an experiment, the independent variable generally has an effect on the dependent variable. For example, if you are researching whether a lack of exercise has an effect on weight gain, the lack of exercise is the independent variable and weight gain is the dependent variable. A confounding variable would be any other influence that has an effect on weight gain. Amount of food consumption is a confounding variable, a placebo is a confounding variable, or weather could be a confounding variable. Each may change the effect of the experiment design.

In order to reduce confounding variables, make sure all the confounding variables are identified in the study. Make a list of everything thought of, one by one, and consider whether those listed items might influence the outcome of the study. Understanding the confounding variables will result in more accurate results.

Examples of Confounding Variable:

1. A mother's education

Suppose a study is done to reveal whether bottle-feeding is related to an increase of diarrhea in infants. It would appear logical that the bottle-fed infants are more prone to diarrhea since water and bottles could easily get contaminated, or the milk could go bad. However, the facts are that bottle-fed infants are less likely to get diarrhea than breast-fed infants. Bottle feeding actually protects against illness. The confounding variable would be the extent of the mother's education on the matter. If you take the mother's education into account, you would learn that better educated mothers are more likely to bottle-feed infants.

2. Weather

Another example is the correlation between murder rate and the sale of ice-cream. As the murder rate raises so does the sale of ice-cream. One suggestion for this could be that murderers cause people to buy ice-cream. This is highly unlikely. A second suggestion is that purchasing ice-cream causes people to commit murder, also highly unlikely. Then there is a third variable which includes a confounding variable. It is distinctly possible that the weather causes the correlation. While the weather is icy cold, fewer people are out interacting with others and less likely to purchase ice-cream. Conversely, when it is hot outside, there is more social interaction and more ice-cream being purchased. In this example, the weather is the variable that confounds the relationship between ice-cream sales and murder.

3. Slanted wood

Another example is the relationship between the force applied to a ball and the distance the ball travels. The natural prediction would be that the ball given the most force would travel furthest. However, if the confounding variable is a downward slanted piece of wood to help propel the ball, the results would be dramatically different. The slanted wood is the confounding variable that changes the outcome of the experiment.