Evidence , Probability , and the Burden of Proof

This Article analyzes the probabilistic and epistemological underpinnings of the burden of proof doctrine. We show that this doctrine is best understood as instructing factfinders to determine which of the parties’ conflicting stories makes most sense in terms of coherence, consilience, causality, and evidential coverage. By applying this method, factfinders should try—and will often succeed—to establish the truth, rather than a statistical surrogate of the truth, while securing the appropriate allocation of the risk of error. Descriptively, we argue that this understanding of the doctrine—the “relative plausibility theory”—corresponds to our courts’ practice. Prescriptively, we argue that the relative-plausibility method is operationally superior to factfinding that relies on mathematical probability. This method aligns with people’s natural reasoning and common sense, avoids paradoxes engendered by mathematical probability, and seamlessly integrates with the rules of substantive law that guide individuals’ primary conduct and determine liabilities and entitlements. We substantiate this claim by juxtaposing the extant doctrine against two recent contributions to evidence theory: Professor Louis Kaplow’s proposal that the burden of proof should be modified to track the statistical distributions of harms and benefits associated with relevant primary activities; and Professor Edward Cheng’s model that calls on factfinders to make their decisions by using numbers instead of words. Specifically, we demonstrate that both models suffer from serious conceptual problems and are not feasible operationally. The extant burden of proof doctrine, we conclude, works well and requires no far-reaching reforms.