Detection and Correction of Case-Publication Bias

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Case-publication bias, the possibility that certain legal outcomes may be more likely to be published or observed than others, carries significant implications for both legal actors and researchers. In this article, I propose a method for detecting and correcting case-publication bias based on ideas from multiple-systems estimation, a technique traditionally used for estimating hidden populations. I apply the method to a simulated data set of admissibility decisions to confirm its efficacy, then to a newly collected data set on false-confession expert testimony, where the model estimates that the observed 16 percent admissibility rate may be in reality closer to 28 percent. The article thus draws attention to the problem of case-publication bias and offers a practical statistical tool for detecting and correcting it.

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