A lurking variable is a variable that is not included in a statistical analysis but can still affect the outcome of that analysis. It is also known as a confounding variable, confounding factor, extraneous determinant, or confounder. Lurking variables can create problems by making the correlation between a pair of variables appear stronger than it actually is or by hiding the genuine effect between two variables. They are extraneous variables that may have an important, significant effect on the variables of interest, but may make the relationship between dependent variables and independent variables seem other than it actually is.

Lurking variables can cause omitted variable bias only when they correlate with both the dependent variable and an independent variable and are not included in the model. They can falsely identify a strong relationship between variables or hide the true relationship. For research results to be valid, lurking variables must be identified and then either eliminated, held constant, or included in the study. They can be identified with regression analysis: plot the residuals, and if you see a trend (either linear or non-linear), this is evidence that a particular variable is affecting the response variable.