Combatting bayesian criticism: a bayesian-inspired critical checklist for judicial reasoning
Abstract
This paper develops a Bayesian-inspired checklist of critical questions designed to support probabilistic reasoning in legal contexts. Building on Mackor’s (2026) proposal, rather than requiring formal Bayesian models or numerical probabilities, the framework translates key Bayesian principles into a practical sequence of guided questions aimed at helping judges structure and critically assess their reasoning while avoiding probabilistic fallacies. The framework addresses common reasoning errors discussed in the legal literature (see Dahlman, 2023), including base-rate neglect, inversion fallacies, false dichotomies, dependence neglect, convergence neglect, and link-skipping. Organised according to the key stages of probabilistic reasoning, the checklist is intended as a flexible aid to judicial deliberation and self-evaluation rather than as a formal decision-making model. It aims to promote transparency in judicial reasoning, encourage explicit reflection on underlying assumptions and evidential relationships, and support the critical evaluation of expert evidence. Future research will focus on empirical testing and further refinement of the framework.