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.
