Detection and Correction of Legal Publication Bias

30 Pages Posted: 1 Nov 2016 Last revised: 3 Nov 2016

Date Written: October 31, 2016

Abstract

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. In this paper, I propose a method for detecting and correcting legal publication bias based on ideas from multiple systems estimation (MSE), a technique traditionally used for estimating hidden populations. I apply 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% admissibility rate may be in reality closer to 28%. The article thus identifies and draws attention to the potential for legal publication bias, and offers a practical statistical tool for detecting and correcting it.

Keywords: publication bias, false confessions, evidence, multiple systems estimation

Suggested Citation

Cheng, Edward K., Detection and Correction of Legal Publication Bias (October 31, 2016). Available at SSRN: https://ssrn.com/abstract=2861685 or http://dx.doi.org/10.2139/ssrn.2861685

Edward K. Cheng (Contact Author)

Vanderbilt Law School ( email )

131 21st Avenue South
Nashville, TN 37203-1181
United States
615-875-7630 (Phone)

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