Statistical Evidence in Employment Discrimination Cases

Statistical methods in general, and multiple regression analysis in particular, are being used increasingly to provide evidence in employment discrimination cases. While the technical issues involved in using statistical methods to detect discrimination are straightforward, the conceptual issues are much less clearly understood. This article provides a framework to help clarify the conditions under which an estimated effect can be properly attributed to discrimination. Several interrelated issues have caused particular confusion, including the distinction between disparate impact and disparate treatment, the definition of test bias, the use of “reverse regression,†proxy variables for true productivity, and measurement error. A simple mathematical model is developed to analyze the precise nature of these issues. It is concluded that although employment discrimination cases involve all the usual problems involved in causal inference from observational data, certain aspects of the legal context may facilitate the valid application of statistical techniques.