This refers to any bias in a predictive model which is the result of the omission of variables that are relevant to the outcome. The omission of a relevant variable can, among other things, lead to erroneous conclusions about the relative influence of different variables on a certain outcome. A researcher trying to determine the factors that influence unemployment, for instance, would be committing an error if he were to ignore the effect of the minimum wage on the unemployment rate. The omitted variable bias is a common problem in the field of regression analysis.
