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This paper reviews the development of the law governing the admissibility of statistical studies. It analyzes the leading cases on scientific evidence and suggests that both the "reliability" and the "general acceptance" standards raise two major difficulties - the "boundary problem" of identifying the type of evidence that warrants careful screening and the "usurpation problem" of keeping the trial judge from closing the gate on evidence that should be left for the jury to assess.

The paper proposes partial solutions to these problems, and it applies them to statistical and econometric proof, particularly in the context of a recent antitrust case. It concludes that Daubert-like screening of complex statistical analyses is a salutary development, but that the task requires the elaboration of standards that attend to the distinction between a general methodology and a specific conclusion. Screening statistical proof demands some sophistication in evaluating the choice of a research design or statistical model, the variables included in a particular model, the procedures taken to verify the usefulness of the model for the data at hand, and the inferences or estimates that follow from the statistical analysis. The factors enumerated in Daubert work reasonably well with some of these aspects of the expert's work, but these factors are less well adapted to others. If the "intellectual rigor" standard of Kumho Tire is used to fill the gap, it must be applied with some caution lest it become a subterfuge for excluding expert testimony that is less than ideal but still within the range of reasonable scientific debate.


This paper was originally published at 87 Va. L. Rev. 1933.