The EZ diffusion method: too EZ?

The diffusion model (Ratcliff, 1978) for fast two-choice decisions has been successful in a number of domains. Wagenmakers, van der Maas, and Grasman (2007) proposed a new method for fitting the model to data ("EZ") that is simpler than the standard chisquare method (Ratcliff & Tuerlinckx, 2002). For an experimental condition, EZ can estimate parameter values for the main components of processing using only correct response times (RTs), their variance, and accuracy, not error RTs or the shapes of RT distributions. Wagenmakers et al. suggested that EZ produces accurate parameter estimates in cases in which the chi-square method would fail-specifically, experimental conditions with small numbers of observations or with accuracy near ceiling. In this article, I counter these claims and discuss EZ's limitations. Unlike the chi-square method, EZ is extremely sensitive to outlier RTs and is usually less efficient in recovering parameter values, and it can lead to errors in interpretation when the data do not meet its assumptions, when the number of observations in an experimental condition is small, or when accuracy in an experimental condition is high. The conclusion is that EZ can be useful in the exploration of parameter spaces, but it should not be used for meaningful estimates of parameter values or for assessing whether or not a model fits data.