Are low LRs reliable?. (November 2020)

• Record Type: Journal Article
• Authors: Buckleton, John S.; Pugh, Simone N.; Bright, Jo-Anne; Taylor, Duncan A.; Curran, James M.; Kruijver, Maarten; Gill, Peter; Budowle, Bruce; Cheng, Kevin
• Is Part Of: Forensic science international. Volume 49(2020)
• Publication Date: 2020-11
• Subjects: Forensic DNA -- Probabilistic genotyping -- STRmix -- Likelihood ratio; Forensic genetics -- Periodicals; Génétique légale -- Périodiques; Forensic genetics; Electronic journals; Periodicals; 614.1
• DOI: 10.1016/j.fsigen.2020.102350
• Languages: English
• ISSNs: 1872-4973

Abstract

Highlights: DNA mixtures with at least one low LR are examined. Large scale tests of false donors are undertaken. The rate of inclusion of false donors is less than predicted by Turing's rule. Abstract: To answer the question "Are low likelihood ratios reliable?" requires both a definition of reliable and then a test of whether low likelihood ratios ( LR s) meet that definition. We offer, from a purely statistical standpoint, that reliability can be determined by assessing whether the rate of inclusionary support for non-donors over many cases is not larger than expected from the LR value. Thus, it is not the magnitude of the LR alone that determines reliability. Turing's rule is used to inform the expected rate of non-donor inclusionary support, where the rate of non-donor inclusionary support is at most the reciprocal of the LR, i.e. Pr( LR > x | Ha ) ≤1/ x . There are parallel concerns about whether the value of the evidence can be communicated. We do not discuss that in depth here although it is an important consideration to be addressed with training. In this paper, we use a mixture of real and simulated data to show that the rate of non-donor inclusionary support for these data is significantly lower than the upper bound given by Turing's rule. We take this as strong evidence that low LR s are reliable.