In behavior analysis, visual inspection of graphic information is the standard by which data are evaluated. Efforts to supplement visual inspection using inferential statistical procedures to assess intervention effects (e.g., analysis of variance or time-series analysis) have met with opposition. However, when serial dependence is present in the data, the use of visual inspection by itself may prove to be problematic. Previously published reports demonstrate that autocorrelated data influence trained observers' ability to identify level treatment effects and trends that occur in the intervention phase of experiments. In this report, four recent studies are presented in which autoregressive equations were used to produce point-to-point functions to simulate experimental data. In each study, various parameters were manipulated to assess trained observers' responses to changes in point-to-point functions from the baseline condition to intervention. Level shifts over baseline behavior (treatment effect), as well as no change from baseline (no treatment effect or trend), were most readily identified by observers, but trends were rarely recognized. Furthermore, other factors previously thought to augment and improve observers' responses had no impact. Results are discussed in terms of the use of visual inspection and the training of behavior analysts.
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