Statistical analyses play an important role in employment discrimination cases, as the Supreme Court has long recognized. Regression analysis can help a plaintiff establish a claim of discrimination under Title VII of the Civil Rights Act of 1964 by showing that, even when controlling for relevant characteristics, individuals of a certain class were treated differently than other employees or applicants. It can also help a defendant rebut such a claim by showing that differential treatment was due to characteristics other than being a member of a protected class. Yet, too often, opposing experts present invalid rebuttal evidence that the jury or judge overweighs. Opposing experts routinely criticize three aspects of the regression: the regression’s explanatory variables, its sample size, and its statistical significance. Even though these factors affect the reliability of the regression results only in very limited circumstances, the judge or jury is often persuaded by them and find for the defendant. As a result, valid regression analyses do not perform the critical work that they should in employment discrimination cases. Our own statistical analyses of seventy-eight Title VII employment discrimination cases finds that regression analyses do not substantially increase the plaintiff’s likelihood of prevailing at trial and that if the court recognizes any of these common critiques, the plaintiff is much less likely to prevail. The severe consequences of such critiques make it very important for the court and opposing experts to recognize when these critiques are without merit. We propose that courts adopt a peer-review system in which court-appointed economists, compensated by each party as a percentage of the total payment to econometric expert witnesses, review econometric evidence before the reports are submitted to the judge or jury.



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