Forensic Science and How Statistics Can Help It: Evidence, Hypothesis Testing, and Graphical Models

TL;DR

This paper discusses how statistical methods, including graphical models, can improve forensic science by providing clearer evidence evaluation and addressing challenges in court applications to reduce wrongful convictions.

Contribution

It introduces the application of graphical models like Bayesian networks and chain event graphs to enhance forensic evidence interpretation and discusses the importance of making statistical analysis accessible in legal contexts.

Findings

Graphical models facilitate concurrent examination of diverse evidence types.

Likelihood ratio approach is commonly used but often misapplied.

Statistical interpretation needs to be accessible to non-statisticians.

Abstract

The persistent issue of wrongful convictions in the United States emphasizes the need for scrutiny and improvement of the criminal justice system. While statistical methods for the evaluation of forensic evidence, including glass, fingerprints, and DNA, have significantly contributed to solving intricate crimes, there is a notable lack of national-level standards to ensure the appropriate application of statistics in forensic investigations. We discuss the obstacles in the application of statistics in court, and emphasize the importance of making statistical interpretation accessible to non-statisticians, especially those who make decisions about potentially innocent individuals. We investigate the use and misuse of statistical methods in crime investigations, in particular the likelihood ratio approach. We further describe the use of graphical models, where hypotheses and evidence can be represented as nodes connected by arrows signifying association or causality. We emphasize the advantages of special graph structures, such as object-oriented Bayesian networks and chain event graphs, which allow for the concurrent examination of evidence of various nature.