Forensic strength of evidence statements should preferably be likelihood ratios calculated using relevant data, quantitative measurements, and statistical models

TL;DR

This paper advocates for using likelihood ratios derived from relevant data, measurements, and statistical models to express forensic evidence strength, emphasizing transparency, testability, and robustness over expert opinion alone.

Contribution

It argues that likelihood ratios based on statistical models are superior to expert opinion for forensic evidence presentation, enhancing transparency and reliability.

Findings

Likelihood ratios improve transparency in forensic evidence.

Statistical models offer robustness against cognitive biases.

Likelihood ratios facilitate testing and validation of evidence strength.

Abstract

Lennard (2013) [Fingerprint identification: how far have we come? Aus J Forensic Sci. doi:10.1080/00450618.2012.752037] proposes that the numeric output of statistical models should not be presented in court (except "if necessary" / "if required"). Instead he argues in favour of an "expert opinion" which may be informed by a statistical model but which is not itself the output of a statistical model. We argue that his proposed procedure lacks the transparency, the ease of testing of validity and reliability, and the relative robustness to cognitive bias that are the strengths of a likelihood-ratio approach based on relevant data, quantitative measurements, and statistical models, and that the latter is therefore preferable.