Are low LRs reliable?

John S. Buckleton, Simone N. Pugh, Jo Anne Bright, Duncan A. Taylor, James M. Curran, Maarten Kruijver, Peter Gill, Bruce Budowle, Kevin Cheng

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


To answer the question “Are low likelihood ratios reliable?” requires both a definition of reliable and then a test of whether low likelihood ratios (LRs) 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 LRs are reliable.

Original languageEnglish
Article number102350
JournalForensic Science International: Genetics
StatePublished - Nov 2020


  • Forensic DNA
  • Likelihood ratio
  • Probabilistic genotyping
  • STRmix

Cite this