A fundamental feature of how we make decisions is that our responses are variable in the choices we make and the time it takes to make them. This makes it impossible to determine, for a single trial of an experiment, the quality of the evidence on which a decision is based. Even for stimuli from a single experimental condition, it is likely that stimulus and encoding differences lead to differences in the quality of evidence. In the research reported here, with a simple “face”/”car” perceptual discrimination task, we obtained late (decision-related) and early (stimulus-related) single-trial EEG component amplitudes that discriminated between faces and cars within and across conditions. We used the values of these amplitudes to sort the response time and choice within each experimental condition into more-face-like and less-face-like groups and then fit the diffusion model for simple decision making (a well-established model in cognitive psychology) to the data in each group separately. The results show that dividing the data on a trial-by-trial basis by using the late-component amplitude produces differences in the estimates of evidence used in the decision process. However, dividing the data on the basis of the early EEG component amplitude or the times of the peak amplitudes of either component did not index the information used in the decision process. The results we present show that a single-trial EEG neurophysiological measure for nominally identical stimuli can be used to sort behavioral response times and choices into those that index the quality of decision-relevant evidence.