Combatting bayesian criticism: a bayesian-inspired critical checklist for judicial reasoning

• Pages: 199-223
• Date: 01 July 2026
• Published date: 01 July 2026
• Author: Leya Lisa Hampson
• Subject Matter: Derecho Procesal

Quaestio facti. Revista Internacional sobre Razonamiento Probatorio / International Journal on Evidential Legal Reasoning

Año 2026 11 pp. 199-223 DOI: 10.33115/udg_bib/qf.i11.23303

Quaestio facti. Revista Internacional sobre Razonamiento Probatorio

Quaestio facti. International Journal on Evidential Legal Reasoning

Sección: Conjeturas y refutaciones

2026 l 11 pp. 199-223

Madrid, 2026

DOI: 10.33115/udg_bib/qf.i11.23303

Marcial Pons Ediciones Jurídicas y Sociales

© Leya Lisa Hampson

ISSN: 2604-6202

Recibido: 08/06/2026 | Aceptado: 15/06/2026 | Publicado online: 30/06/2026

Editado bajo licencia Reconocimiento 4.0 Internacional de Creative Commons

COMBATTING BAYESIAN CRITICISM:

A BAYESIAN-INSPIRED CRITICAL CHECKLIST

FOR JUDICIAL REASONING

Leya Lisa Hampson 1

ABSTRACT: is paper develops a Bayesian-inspired checklist of critical questions designed to sup-

port 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 improve and struc-

ture their reasoning and avoid probabilistic fallacies. is intended to assist judges in structuring and

critically reecting on their reasoning, while helping to identify and avoid probabilistic fallacies.

e framework addresses common reasoning errors discussed in the legal literature (see Dahlman,

2023), including base-rate neglect, inversion fallacies, false dichotomies, dependence neglect, con-

vergence neglect, and link-skipping. Organised according to key stages of probabilistic reasoning,

the checklist is designed as a exible aid to judicial deliberation and self-evaluation rather than a

formal decision-making model. It aims to promote transparency in reasoning, encourage explicit

reection on assumptions and evidential relationships, and support the critical evaluation of expert

evidence. Future research will focus on empirical testing and further renement of the framework.

KEYWORDS: Bayesian reasoning, Legal aid, Probabilistic fallacies

SUMMARY:

1. INTRODUCTION: 1.1. e Problem: Probabilistic Reasoning in Judicial

Contexts; 1.2. Suggested solutions: e Bayesian Method.— 2. METHOD: FROM

1 Department of Transboundary Legal Studies, Faculty of Law, University of Groningen

200 LEYA LISA HAMPSON

Quaestio facti. Revista Internacional sobre Razonamiento Probatorio / International Journal on Evidential Legal Reasoning

Año 2026 11 pp. 199-223 DOI: 10.33115/udg_bib/qf.i11.23303

THEORY TO STRUCTURED CHECKLIST.— 3. CHECKLIST DEVELOP-

MENT: 3.1. Framing the Hypotheses and Evidence; 3.2. Prior Probabilities and

Base Rate Neglect: 3.2.1. Motive and Opportunity; 3.3. False dichotomies and com-

peting hypotheses; 3.4. Evidence Evaluation: Inversion Fallacies; 3.5. Evidence Eval-

uation: Convergent Evidence, Dependencies and their Combination; 3.6. Indirect

Reasoning and Chains of Inference; 3.7. Instructions for application in practice.—

4. CONCLUSION.— REFERENCES.— APPENDIX A: PROPOSED CHECK-

LIST .— APPENDIX B: PROPOSED INFORMATION SHEET

1. INTRODUCTION

e central question addressed by Mackor (2026) is whether the Bayesian model

can provide meaningful guidance for the judicial evaluation of evidence, in particular

through the modelling of complete criminal cases. While this approach oers a formal-

ly coherent framework for reasoning under uncertainty, Mackor highlights the practi-

cal and conceptual concerns associated with its application in judicial contexts. ese

include discussions on the feasibility of constructing full Bayesian networks in real

cases, as well as the ability of judges to apply such models reliably and independently.

In light of these limitations, Mackor identies a number of directions for future

research. Among these is the proposal to translate the insights of Bayesian reasoning

into a structured checklist of “critical questions” that legal practitioners could use

when analysing evidence to help them recognise and avoid probabilistic fallacies.

e present paper takes up this proposal directly. Rather than focusing on the

construction of complete Bayesian models, it seeks to operationalise the suggested

list of critical questions by developing a structured checklist for use in legal, in par-

ticular judicial, reasoning. In doing so, it builds on the insights of Bayesian analysis,

while deliberately avoiding the need for numerical quantication or the construc-

tion of full Bayesian networks. e aim is to provide judges with a practical tool for

identifying and avoiding probabilistic fallacies in their own reasoning, as well as for

critically assessing reasoning presented in expert reports. In this way, the paper oers

a concrete response to the research agenda outlined by Mackor, by developing and

structuring the proposed checklist into an instrument that can be implemented in

judicial (and broader legal practice). In its current stage, the research remains a the-

oretical, oering an initial version of the checklist in the literature. e framework

will undergo further renement through feedback and iterative development before

being subjected to rigorous empirical evaluation in future studies.

1.1. e Problem: Probabilistic Reasoning in Judicial Contexts

Judicial decision-making involves reasoning under uncertainty. Judges are re-

quired to evaluate competing hypotheses in light of incomplete and often complex