A resampling-based method of inference--permutation tests--is often used when distributional assumptions are questionable or unmet. Not only are these methods useful for obvious departures from parametric assumptions (e.g., normality) and small sample sizes, but they are also more robust than their parametric counterparts in the presences of outliers and missing data, problems that are often found in clinical child and adolescent psychology research. These methods are increasingly found in statistical software programs, making their use more feasible. In this article, we use an application-based approach to provide a brief tutorial on permutation testing. We present some historical perspectives, describe how the tests are formulated, and provide examples of common and specific situations under which the methods are most useful. Finally, we demonstrate the utility of these methods to clinical and adolescent psychology by examining four recent articles employing these methods. (Contains 6 tables.)