THERE'S NO DOUBT that companies can benefit from workplace surveys and questionnaires. A GTE survey in the mid-1990s, for example} revealed that the performance of its different billing operations, as measured by the accuracy of bills sent out, was closely tied to the leadership style of the unit managers.
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.
We distinguish between reverse discrimination and over-correction, arguing that the former should be used only to describe cases where well-qualified non-minority applicants are unjustifiably denied positions in organizations run by and/or staffed by minorities. Similarly, we argue over-correction should be used to describe well-qualified non-minority applicants who are unjustifiably denied positions in organizations run by non-minorities.