Type I Error in Single-Case Experimental Designs

Abstract Type I errors, or false positives, occur when a researcher erroneously concludes that the independent variable caused a change in the dependent variable. Type I errors can result in resources being misused in both research and practice. Although Type I errors are most often associated with group design paradigms, researchers using single-case experimental designs are also susceptible to making Type I errors. In this article, we describe the research on Type I errors in single-case experimental designs across analytic methods, including visual analysis, machine learning, effect estimation, and randomization approaches. We also suggest ways that each analytic method can be used to mitigate Type I errors and highlight aspects of Type I error in single-case experimental designs that are understudied and in need of additional research.