  A group of approaches for calculating forensic likelihood ratios first
calculates scores which quantify the degree of difference or the degree of
similarity between pairs of samples, then converts those scores to likelihood
ratios. In order for a score-based approach to produce a forensically
interpretable likelihood ratio, however, in addition to accounting for the
similarity of the questioned sample with respect to the known sample, it must
also account for the typicality of the questioned sample with respect to the
relevant population. The present paper explores a number of score-based
approaches using different types of scores and different procedures for
converting scores to likelihood ratios. Monte Carlo simulations are used to
compare the output of these approaches to true likelihood-ratio values
calculated on the basis of the distribution specified for a simulated
population. The inadequacy of approaches based on similarity-only or
difference-only scores is illustrated, and the relative performance of
different approaches which take account of both similarity and typicality is
assessed.
