Employment Discrimination and Statistical Science

Abstract

Good practice of legal statistics depends on a foundation of good statistical science. Causal inference from statistical data depends both on understanding of substantive causal processes and adequate principles of statistical inference. The paper makes a case that Bayesian reasoning is needed for statistical studies of employment discrimination. A model based on Bayesian principles is developed in detail and is used to show that any statistical estimate of the effects of employment discrimination must be adjusted from sources of knowledge outside the statistician's data. Econometric analyses, which suggest otherwise, are analyzed and criticized.