Why calibrating LR-systems is best practice. A reaction to "The evaluation of evidence for microspectrophotometry...

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.