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.