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 Differences Between Explanatory and Response Variables

What You Should Know About Econometrics

Linear Regression Analysis

What Are Residuals?

Correlation Analysis in Research

What It Means When a Variable Is Spurious

How Intervening Variables Work in Sociology

What Is a Scatterplot?

Paired Data in Statistics

The Importance of Exclusion Restrictions in Instrumental Variables

Structural Equation Modeling

Unbiased and Biased Estimators

The Significance of Negative Slope

The Difference Between Extrapolation and Interpolation

Definition of a Hypothesis