Estimating the Accuracy of Jury Verdicts


  • The author thanks Juha Alho, Ron Allen, Kenworthey Bilz, Shari Seidman Diamond, Ted Eisenberg, Steve Fienberg, Joseph Gastwirth, Jack Heinz, Wenxin Jiang, Chuck Manski, Dorothy Roberts, and Sandy Zabell for helpful comments. He is deeply indebted to Shelby Haberman for suggestions and patient help with modeling. Responsibility for errors rests with the author. This research was supported by the Institute for Policy Research, Northwestern University.

*Department of Statistics, Northwestern University, 2006 Sheridan Rd., Evanston, IL 60208; email: Bruce D. Spencer is Professor, Department of Statistics, and Faculty Fellow, Institute for Policy Research, Northwestern University.


Average accuracy of jury verdicts for a set of cases can be studied empirically and systematically even when the correct verdict cannot be known. The key is to obtain a second rating of the verdict, for example, the judge's, as in the recent study of criminal cases in the United States by the National Center for State Courts (NCSC). That study, like the famous Kalven-Zeisel study, showed only modest judge-jury agreement. Simple estimates of jury accuracy can be developed from the judge-jury agreement rate; the judge's verdict is not taken as the gold standard. Although the estimates of accuracy are subject to error, under plausible conditions they tend to overestimate the average accuracy of jury verdicts. The jury verdict was estimated to be accurate in no more than 87 percent of the NCSC cases (which, however, should not be regarded as a representative sample with respect to jury accuracy). More refined estimates, including false conviction and false acquittal rates, are developed with models using stronger assumptions. For example, the conditional probability that the jury incorrectly convicts given that the defendant truly was not guilty (a “Type I error”) was estimated at 0.25, with an estimated standard error (s.e.) of 0.07, the conditional probability that a jury incorrectly acquits given that the defendant truly was guilty (“Type II error”) was estimated at 0.14 (s.e. 0.03), and the difference was estimated at 0.12 (s.e. 0.08). The estimated number of defendants in the NCSC cases who truly are not guilty but are convicted does seem to be smaller than the number who truly are guilty but are acquitted. The conditional probability of a wrongful conviction, given that the defendant was convicted, is estimated at 0.10 (s.e. 0.03).