95 N.C. L. Rev. 607 (2017)
Statistical adjudication, the practice of using sampling and other statistical techniques to adjudicate large case aggregations, is highly controversial today. In all its forms, statistical adjudication decides cases on the basis of statistical extrapolation rather than case-specific facts. For example, a court adjudicating a large class action might try a random sample of cases, average the trial verdicts, and give the average to all the other cases in the aggregation. In Wal-Mart Stores, Inc. v. Dukes, the Supreme Court rejected a sampling proposal as inconsistent with the Rules Enabling Act, calling it “Trial by Formula.” In the wake of this decision, at least one commentator declared the death of statistical adjudication.
In an important decision last term, Tyson Foods, Inc. v. Bouaphakeo, the Court changed course and breathed new life into statistical adjudication. It upheld the use of sampling to establish liability and damages in a Fair Labor Standards Act case and indicated that sampling might be available in other cases as well. The Court’s opinion is far from clear, however, and offers little guidance to lower court judges trying to determine when and how to use the procedure in future cases.
This Article explores the impact of Tyson Foods on the future of statistical adjudication. Part I defines statistical adjudication and distinguishes it from statistical evidence. Part II shows that Tyson Foods is better understood as a case of statistical adjudication than simply a case of statistical evidence. Part III takes a closer look at the Court’s opinion in an effort to tease out factors and principles to guide the future use of statistical adjudication. Part IV explores reasons for the vague discomfort with the procedure, reasons that seem tied to nagging doubts about its legitimacy. Critics worry that statistical adjudication is too strange a fit with adjudication, too substantive to be legitimately implemented as procedure, and too mechanical to count as a proper form of adjudicative reasoning. Part IV argues that statistical adjudication is not as strange as it might seem, that its outcome effects do not make it too substantive, and that while it substitutes a mechanical decision algorithm for the usual reasoning process, it does so in a way that can be justified as legitimate. It is time that we recognize statistical adjudication for what it is: a useful procedural tool that, when carefully designed and selectively deployed, is capable of adjudicating large case aggregations fairly and efficiently.