“…Nonparametric statistics have been developed for describing the difference between baseline and treatment conditions (i.e., effect sizes) for single‐subject design research, but have not been used to aid in the interpretation of functional analysis outcomes. For example, the percentage of nonoverlapping data (PND; Scruggs, Mastropieri, & Casto, ) is a conservative approach (Carr, ) that identifies the number of points during treatment that do not overlap with the highest baseline point and divides that sum of nonoverlapping points by the total number of treatment points to provide an effect size between 0 and 100%. Effect sizes calculated using simple statistics like PND have been found to be useful in treatment efficacy reviews (e.g., Campbell, ; Carr, Severtson, & Lepper, ; Heyvaert et al, ); however, it is unknown if PND effect sizes between the test and control conditions of a functional analysis would correspond to, or be more or less sensitive to, a structured criteria or visual analysis of functional analysis data.…”