Why calibrating LR-systems is best practice. A reaction to “The evaluation of evidence for microspectrophotometry data using functional data analysis”, in FSI 305

In their paper “The evaluation of evidence for microspectrophotometry data using functional data analysis”, in FSI 305, Aitken et al. present a likelihood-ratio (LR) system for their data. We show the values generated by this system cannot be interpreted as LRs: they are ill-calibrated and should be interpreted as discriminating scores. We demonstrate how to transform the scores to well-calibrated LRs using a post-hoc calibrating step. Also, we address criticisms of calibration posited by Aitken et al. We conclude by noting that ill-calibrated LR-values are misleadingly small or large. Therefore calibration should be measured and, if necessary, corrected for. The corrected LR-values (instead of the discriminating scores) can be used to update the prior odds in Bayes rule.