Experiments often produce a hit rate and a false alarm rate in each of two conditions. These response rates are summarized into a single-point sensitivity measure such as d', and t tests are conducted to test for experimental effects. Using large-scale Monte Carlo simulations, we evaluate the Type I error rates and power that result from four commonly used single-point measures: d', A', percent correct, and gamma. We also test a newly proposed measure called gammaC. For all measures, we consider several ways of handling cases in which false alarm rate = 0 or hit rate = 1. The results of our simulations indicate that power is similar for these measures but that the Type I error rates are often unacceptably high. Type I errors are minimized when the selected sensitivity measure is theoretically appropriate for the data.