Bayes Rules: A Bayesian-Intuit Approach to Legal Evidence

The law too often avoids or misuses statistical evidence. This problem is partially explained by the absence of a shared normative framework for working with such evidence. There is considerable disagreement within the legal community about how statistical evidence relates to legal inquiry. It is proposed that the first step to addressing the problem is to accept Bayesianism as a normative framework that leads to outcomes that largely align with legal intuitions. It is only once this has been accepted that we can proceed to encourage education about common conceptual errors involving statistical evidence as well as techniques to limit their occurrence. Objections to using Bayesianism in the legal context are addressed. It is argued that the objection based on the irrelevance of statistical evidence is fundamentally incoherent in its failure to identify most evidence as statistical. Second, objections to the incompleteness of a Bayesian approach in accounting for non-truth-related values do place legitimate limits on the use of Bayesianism in the law but in no way undermine its normative usefulness. Lastly, many criticisms of the role of Bayesianism in the law rest on misunderstandings of the meaning and manipulation of statistical evidence and are best addressed by presenting statistical evidence in ways that encourage correct understanding. Once it is accepted that, put in its proper place, a Bayesian approach to understanding statistical evidence can align with most fundamental legal intuitions, a less fearful approach to the use of statistical evidence in the law can emerge.Ph