Dissociations observed in single-case studies play an important role in building and testing theory in neuropsychology; therefore the criteria used to identify their presence should be subjected to empirical scrutiny. Extending work on classical dissociations, Monte Carlo simulation is used to examine the Type I error rate for two methods of detecting strong dissociations. When a Type I error was defined as misclassifying a healthy control, error rates were low for both methods. When Type I errors were defined as misclassifying patients with strictly equivalent deficits on two tasks, error rates for strong dissociations were high for the "conventional" criteria and were very high when cases misclassified as exhibiting either form of dissociation (strong or classical) were combined (maximum = 55.1%). The power to detect strong and classical dissociations was generally low-to-moderate, but was moderate-to-high in most scenarios when power was defined as the ability to detect either form of dissociation. In most scenarios patients with strong dissociations were more likely to be classified as exhibiting classical dissociations. The results question the practical utility of the distinction between strong and classical dissociations regardless of the criteria employed to test for their presence.