Extraneous variable

Extraneous variables are variables other than the independent variable that may bear any effect on the outcome being studied. Extraneous variables may aid a researcher with prediction and goodness of fit, but are not of substantive interest to the hypothesis under examination. For example, in a study examining the relationship between post-secondary education and lifetime earnings, some extraneous variables might be gender, ethnicity, social class, genetics, intelligence, age, and so forth.

A variable is extraneous only when it can be assumed (or shown) to influence the dependent variable. It introduces noise but does not systematically bias the results (i.e., the estimate of the relationship between the target independent variable and the outcome). For example, while social class might affect lifetime earnings, as long as it has no effect on education, then the effect of education on earnings can be identified without controlling for social class.

Extraneous variables are often classified into three types:

1. Subject variables, which are the characteristics of the individuals being studied that might affect their actions. These variables include age, gender, health status, mood, background, etc.
2. Experimental variables are characteristics of the persons conducting the experiment which might influence how a person behaves. Gender, the presence of racial discrimination, language, or other factors may qualify as such variables.
3. Situational variables are features of the environment in which the study or research was conducted, which have a bearing on the outcome of the experiment in a negative way. Included are the air temperature, level of activity, lighting, and the time of day.

Extraneous variables might also introduce confounding, wherein the relationship between an independent and dependent variable depends on the level of a third, extraneous variable. In these situations, design changes and/or statistical control is necessary.