At least in academic circles, statisticians working in forensic inference and statistics widely support probabilistic approaches, to give the strength of the ev

At least in academic circles, statisticians working in forensic inference and statistics widely support probabilistic approaches, to give the strength of the evidence in terms of likelihood ratios (or Bayes factors) with respect to prosecution and defense hypothesis. For example, what is the probability of observing the features of a given trace had it come from the suspect versus had it come from another individual in the relevant population. The concept of "matching" is also slightly more complicated in reality. Basically you have to account for uncertainty in measurements and other factors. For example, often you won't get a perfect print from a crime scene but a smudged partial mark, which you then compare to an inked fingerprint. Studies about the validity and reliability of fingermark-to-fingerprint comparison that are based on fingerprint databases of inked fingerprints most likely overstate the accuracy of the method as compared to testing under conditions of real casework. For some additional background on problems with fingerprints in forensics see: National Institute of Standards and Technology, National Institute of Justice, February 2012 report: "Latent Print Examination and Human Factors: Improving the Practice through a Systems Approach" (http://www.nist.gov/manuscript-publication-search.cfm?pub_id=910745 http://www.nist.gov/manuscript-publication-search.cfm?pub_id...) Edit: See this paper for a recent example of academic work in forensic statistics (I'm not affiliated with the work/authors etc.) : http://arxiv.org/abs/1503.08234v1 http://arxiv.org/abs/1503.08234v1 Edit2: fixed link