The economist who wants to work on legal questions needs to know something about law, but he does not need a J.D. Analysis of jury selection, from which some identifiable group of the population allegedly has been excluded, has historically established the statistical approach to discrimination in selection of all kinds. The leading article by Michael Finkelstein, to which analysts still today pay homage, was published in 1966, providing the basis for the Supreme Court's 1977 finding of jury discrimination in Castaneda.
In a recent article Havrilesky (1993) argues against applying the hedonic damages concept to wrongful death and injury cases. The purpose of this paper is to critique his arguments. An examination of each of the seven points shows that none are appropriate. This analysis follows the same order and is under the same headings as Havrilesky's analysis. The conclusion section is added to summarize the paper.
This might come as a shock: Employees in large corporations sometimes mistakenly believe that they have been discriminated against. Admittedly, discrimination does occur, both in society and in the workplace. And as most attorneys know, many discrimination cases concern claims of either adverse treatment or adverse impact.
Effective evidence-based managelnent requires analyzing data from a broad array of sources and conducting carefully designed pretest-posttest comparisons. However, our experience suggests that few businesses take that process to the next level by building merged datasets that can be used for rigorous pretest-posttest comparisons and meaningful statistical analyses.
No contemporary guide exists for using statistics to prove causality in court. We outline a new theory explaining comprehension of causal graphs, and claim four hallmarks of causality are critical: Association, Prediction, Exclusion of Alternative Explanations, and Dose Dependence.