@article{Wilford_Gonzales_Khairalla_2024, title={When Pleas Precede Evidence: Using Bayesian Analyses to Establish the Importance of a Reasonable Standard for Evidence Prior to Plea Offers}, volume={5}, url={https://wclawr.org/index.php/wclr/article/view/114}, DOI={10.29173/wclawr114}, abstractNote={<p>In most U.S. jurisdictions, prosecutors are not required to clearly establish a <em>reasonable basis</em> for guilt prior to offering defendants plea deals. We apply Bayesian analyses, which are uniquely suited to illuminate the impact of prior probability of guilt on the informativeness of a particular outcome (i.e., a guilty plea), to demonstrate the risks of plea offers that precede evidence. Our primary prediction was that lower prior probabilities of guilt would coincide with a significantly higher risk for false guilty pleas. We incorporated data from Wilford, Sutherland et al. (2021) into a Bayesian analysis allowing us to model the expected diagnosticity of plea acceptance across the full range of prior probability of guilt. Our analysis indicated that, as predicted, when plea offers are accepted at lower prior probabilities of guilt, the probability that a plea is actually false is significantly higher than when prior probabilities of guilt are higher. In other words, there is a trade-off between prior probability of guilt and information gain. For instance, in our analysis, when prior probability of guilt was 50%, posterior probability of guilt (after a plea) was 77.8%; when prior probability of guilt was 80%, posterior probability of guilt was 93.3%. Our results clearly indicate the importance of ensuring that there is a <em>reasonable basis</em> for guilt before a plea deal is extended. In the absence of shared discovery, no such reasonable basis can be established. Further, these results illustrate the additional insights gained from applying a Bayesian approach to plea-decision contexts.</p>}, number={1}, journal={The Wrongful Conviction Law Review}, author={Wilford, Miko M. and Gonzales, Joseph E. and Khairalla, Annmarie}, year={2024}, month={May}, pages={126–145} }