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A group of approaches for calculating forensic likelihood ratios firstcalculates scores which quantify the degree of difference or the degree ofsimilarity between pairs of samples, then converts those scores to likelihoodratios. In order for a score-based approach to produce a forensicallyinterpretable likelihood ratio, however, in addition to accounting for thesimilarity of the questioned sample with respect to the known sample, it mustalso account for the typicality of the questioned sample with respect to therelevant population. The present paper explores a number of score-basedapproaches using different types of scores and different procedures forconverting scores to likelihood ratios. Monte Carlo simulations are used tocompare the output of these approaches to true likelihood-ratio valuescalculated on the basis of the distribution specified for a simulatedpopulation. The inadequacy of approaches based on similarity-only ordifference-only scores is illustrated, and the relative performance ofdifferent approaches which take account of both similarity and typicality isassessed.

Calculation of forensic likelihood ratios: Use of Monte Carlo  simulations to compare the output of score-based approaches with true  likelihood-ratio values