Relapse is the recovery of a previously suppressed response. Animal models have been useful in examin-ing the mechanisms underlying relapse (e.g., reinstatement, renewal, reacquisition, resurgence). How-ever, there are several challenges to analyzing relapse data using traditional approaches. For example,null hypothesis significance testing is commonly used to determine whether relapse has occurred. How-ever, this method requires several a priori assumptions about the data, as well as a large sample size forbetween-subjects comparisons or repeated testing for within-subjects comparisons. Monte Carlomethods may represent an improved analytic technique, because these methods require no priorassumptions, permit smaller sample sizes, and can be tailored to account for all of the data from anexperiment instead of some limited set. In the present study, we conducted reanalyses of three studiesof relapse (Berry, Sweeney, & Odum, 2014; Galizio et al., 2018; Odum & Shahan, 2004) using MonteCarlo techniques to determine if relapse occurred and if there were differences in rate of responsebased on relevant independent variables (such as group membership or schedule of reinforcement).These reanalyses supported the previousfindings. Finally, we provide general recommendations forusing Monte Carlo methods in studies of relapse.