Edward K. Cheng, Professor of Law at Vanderbilt Law School

Judges, attorneys, and academics commonly use case law surveys to ascertain the law and to pre­dict or make decisions. In some contexts, however, certain legal outcomes may be more likely to be published (and thus observed) than others, potentially distorting impressions from case surveys. Professor Cheng proposes a method for detecting and correcting legal publication bias based on ideas from multiple systems estimation, a technique traditionally used for estimating hidden populations. The paper applies the method to a simulated dataset of admissibility decisions to confirm its efficacy, then to a newly collected dataset on false confession experts, where the model estimates that the observed 16 percent admissibility rate may be in reality closer to 28 percent. The paper thus identifies and draws attention to the potential for legal publication bias, and offers a practical statistical tool for detecting and correcting it.

Ed Cheng is a professor of law at Vander­bilt Law School, where he holds the Tarkington Chair of Teaching Excellence. He earned degrees from Princeton University (in electrical engineering), the London School of Economics and Political Science (in information systems), and the Harvard Law School (where he was the articles, book reviews, and commentaries chair of the Harvard Law Review). He is currently pursuing a Ph.D. in statistics at Columbia University.  Professor Cheng teaches Evidence, Torts, and Statistical Concepts for Lawyers, and is a five‑time winner of the Hall‑Hartman Outstanding Professor Award for excellence in teaching.  His research focuses on scientific and expert evidence, and the interaction between law and statistics.  His articles have been published in the Yale Law Journal, Columbia Law Review, Stanford Law Review, and other leading law journals.