Calculation of likelihood ratios for gunshot residue evidence-statistical aspects

Micha MandelY, Nadav Levin, Elad Izraeli, Naomi Kaplan-Damary*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Recently published articles have proposed the use of likelihood ratios (LRs) in determining the evidential value of finding a given number of gunshot residue (GSR) particles on a suspect. LRs depend on the probabilistic models assumed for the defence proposition (the suspect was not involved in a shooting) and the prosecutor's proposition (the suspect was involved), and should be calculated based on data obtained in well designed experiments. However, statistical aspects of the analysis that select the appropriate model and provide uncertainty measures are rarely considered. In this article, data from Cardinetti et al. (2006, A proposal for statistical evaluation of the detection of gunshot residues on a suspect. Scanning, 28(3):142-147) are used to demonstrate the sensitivity of calculated LRs to the assumed model. It is shown that the Poisson model, considered by Cardinetti and others, is inappropriate and that a Negative Binomial model fits the data much better. The statistical error arising from the fact that models are estimated based on small sampled data is discussed, as well as the importance of accounting for this error. We conclude that only with a large database can statistical models be estimated accurately and LR's be treated as valid scientific measures.

Original languageAmerican English
Pages (from-to)107-125
Number of pages19
JournalLaw, Probability and Risk
Issue number2
StatePublished - Jun 2016

Bibliographical note

Funding Information:
This work was supported by the Israel Science Foundation [519/14 to M.M.].

Publisher Copyright:
© The Author [2016].


  • Confidence interval
  • GSR
  • Likelihood ratio
  • Negative Binomial
  • Poisson regression
  • Prediction interval


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