Omitted variables bias (or sometimes omitted variable bias) is a standard expression for the bias that appears in an estimate of a parameter if the regression run does not have the appropriate form and data for other parameters. For example, many regressions that have wage or income as the dependent variable suffer from omitted variables bias because there is often no practical way to add in a worker's innate ability or motivation as an explanatory variable. As a result, the estimated coefficients on variables such as education as likely to be biased because of the correlation between educational attainment and unobserved ability. If the correlation between education and unobserved ability is positive, omitted variables bias will occur in an upward direction. Conversely, if the correlation between an explanatory variable and an unobserved relevant variable is negative, omitted variables bias will occur in a downward direction.

Definition and Use of Instrumental Variables in Econometrics

The Significance of Negative Slope

Correlation Analysis: Comparing Variables

The Differences Between Explanatory and Response Variables

What Is a Hypothesis?

Scientific Terms and Definitions You Should Know

Linear Regression and Multiple Linear Regression Analysis

What Is Ecological Correlation?

What Is Biased Language?

How Status Quo Bias Affects Your Decisions

How Intervening Variables Work in Sociology

What Are Residuals?

How to Calculate the Correlation Coefficient

Definition and Examples of Independent and Dependent Variables

How Are Extrapolation and Interpolation Different?

How to Use Secondary Data in Social Science Research