The value of the evidence in forensic science is increasingly expressed by a Likelihood Ratio (LR), following a Bayesian framework. The LR aims at providing the information given by the evidence to the decision process in a trial. Although theoretical aspects of statistical models are essential to compute the LR, in real forensic situations there exist many other factors (including e.g. data sparsity, data variability and dataset shift) that might degrade the performance of the LR. This means that the computed LR values might be misleading, ultimately causing a loss in the accuracy of the decisions made by the fact finder. Therefore, it is essential to measure the performance of LR methods in forensic situations, with the further objective of validating LR methods for its use in casework. In this talk, we will present several popular performances measures for LR values. We will provide examples where these measures are used to compare different methods in the context of trace evidence. Finally, we will present a recently-proposed guideline for the validation of LR methods in forensic science, that relies upon the use of performance measures of LR methods.
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