Abstract

The 2016 Supreme Court decision in Tyson Foods, Inc. v. Bouaphakeo revived the use of “representative” or sampling evidence in class actions. Federal courts are now more receptive to class plaintiffs’ efforts to prove classwide liability and, occasionally, aggregate damages, with sampling evidence. However, federal courts still routinely deny motions for class certification because they find that calculations of class members’ individual damages defeat the predominance prerequisite of Rule 23(b)(3). As a result, meritorious classwide claims founder. In this paper, we combine legal and statistical analyses and propose a novel solution to this dilemma that adheres to the Tysondecision while satisfying Daubert, the standards of Federal Rule of Evidence 702, and the prerequisites for Rule 23(b)(3) classes. We develop a method and derive a threshold to determine whether class damages claims are sufficiently homogeneous to justify the admissibility of sampling evidence to prove individual damages. Relying on Daubert and its progeny, as well as other well-recognized authority, we argue that accuracy is an appropriate standard for evidentiary reliability. Then, using generally accepted statistical methods and standards, we show that, when judgment variability exceeds claim variability (terms we define), sampling evidence improves accuracy and evidentiary reliability and is, therefore, admissible in Rule 23(b)(3) class certification proceedings. We also recommend several procedures to evaluate whether damages claims of a putative class satisfy the derived threshold. We conclude that our proposed method to prove individual damages achieves the Supreme Court’s stated goals of Rule 23(b)(3) class actions, “economies of time, effort, and expense” and the promotion of “uniformity of decision as to persons similarly situated, without sacrificing procedural fairness or bringing about other undesirable results.”

Publication Title

Boston College Law Review

Publication Date

2018

Document Type

Article



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Law Commons

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