The cut-off on an ordinal test is often determined by its ‘diagnostic accuracy’, measured solely by its Sensitivity and Specificity in relation to a reference standard. This involves selecting the cut-off that maximises their combination, as calculated by measures, such as Youden’s J statistic, that treat False Positives and False Negatives as equally important. Prevalence-adjusted Predictive Values Positive and Negative at each possible cut-off are often calculated, but the set of complementary False Alarm Rates and False Reassurance Rates are left implicit. The False Alarms per False Reassurance Number, which we refer to as the FARN, represents the error rate trade-off embedded at each possible cut-off. The routine display of the full set of FARNs in a dataset would make transparent the preference-sensitivity of cut-off selection and virtually mandate the exploration of the quantitative relative disutility of a FA and a FR essential to establishing the cut-off appropriate in the given decision making context. We link to a prototype generic online calculator that instantly reveals the FARNs implicit in a research dataset. The GAD-7 test for the detection of Generalised Anxiety Disorder provides our empirical illustration. Focusing on the ‘accuracy’ of ordinal diagnostic or screening tests threatens the pursuit of therapeutic optimality.