Replicability and randomization test logic in behavior analysis

Randomization tests are a class of nonparametric statistics that determine the significance of treatment effects. Unlike parametric statistics, randomization tests do not assume a random sample, or make any of the distributional assumptions that often preclude statistical inferences about single-case data. A feature that randomization tests share with parametric statistics, however, is the derivation of a p-value. P-values are notoriously misinterpreted and are partly responsible for the putative replication crisis. Behavior analysts might question the utility of adding such a controversial index of statistical significance to their methods, so it is the aim of this paper to describe the randomization test logic and its potentially beneficial consequences. In doing so, this paper will: (1) address the replication crisis as a behavior analyst views it, (2) differentiate the problematic p-values of parametric statistics from the, arguably, more useful p-values of randomization tests, and (3) review the logic of randomization tests and their unique fit within the behavior analytic tradition of studying behavioral processes that cut across species.