Lampinen (2016) suggested that proponents of ROC analysis may prefer that approach to the diagnosticity ratio because they are under the impression that it provides a theoretical measure of underlying discriminability (d′). In truth, we and others prefer ROC analysis for applied purposes because it provides an atheoretical measure of empirical discriminability (namely, partial area-under-the-curve, or pAUC). The issue of underlying theoretical discriminability only arises when theoreticians seek to explain why one eyewitness identification procedure yields a higher pAUC than another. Lampinen (2016) also argued that favoring the procedure that yields a higher pAUC can lead to an irrational decision outcome. However, his argument depends on needlessly restricting which points from two ROCs can be compared. As a general rule, the maximum-utility point will fall somewhere on the higher ROC, underscoring the need for ROC analysis. Thus, Lampinen's (2016) arguments against the usefulness of ROC analysis are unfounded.