When 10 Trials are Better than 1000: An Evidentiary Perspective on Trial Sampling

Authors

Edward K. Cheng

Document Type

Article

Publication Title

University of Pennsylvania Law Review

Publication Date

2012

ISSN

0041-9907

Page Number

955

Keywords

torts, statistics, trial sampling, shrinkage, averaging, variability

Disciplines

Evidence | Law | Statistics and Probability | Torts

Abstract

In many mass tort cases, separately trying all individual claims is impractical, and thus a number of trial courts and commentators have explored the use of statistical sampling as a way of efficiently processing claims. Most discussions on the topic, however, implicitly assume that sampling is a “second best” solution: individual trials are preferred for accuracy, and sampling only justified under extraordinary circumstances. This Essay explores whether this assumption is really true. While intuitively one might think that individual trials would be more accurate at estimating liability than extrapolating from a subset of cases, the Essay offers three ways in which the “second best” assumption can be wrong. Under the right conditions, sampling can actually produce more accurate outcomes than individualized adjudication. Specifically, sampling’s advantages in averaging (reducing variability), shrinkage (borrowing strength across cases), and information gathering (through nonrandom sampling), can result in some instances in which ten trials are better than a thousand.

Recommended Citation

Edward K. Cheng, When 10 Trials are Better than 1000: An Evidentiary Perspective on Trial Sampling, 160 University of Pennsylvania Law Review. 955 (2012)
Available at: https://scholarship.law.vanderbilt.edu/faculty-publications/147