Abstract
Background: Statistical methods are a cornerstone of research in clinical psychology and are used in clinical trials and reviews to determine the best available evidence. The most widespread statistical framework, frequentist statistics, is often misunderstood and misused. Even when properly applied, this framework can lead to erroneous conclusions and unnecessarily prolonged trials. The implications for clinical psychology are difficulties in interpreting best available evidence and unnecessarily costly and burdensome research. An alternative framework, Bayesian statistics, is proposed as a solution to several issues with current practice. Methods: Statistical tests of primary outcome measures were extracted from 272 studies, which were cited in 11 recent reviews in the Evidence-based updates series in the Journal of Clinical Child and Adolescent Psychology. The extracted tests were examined regarding relevant features and re-analyzed using Bayes Factors. Results: When statistical tests were significant, the majority (98%) of re-analyzed tests agreed with such claims. When statistical tests were nonsignificant almost half (43%) of re-analyzed tests disagreed with such claims. Equally important for clinical research, an average of 13% fewer participants per study would have been required if the studies had used Bayes Factors. Conclusions: Bayes Factors offer benefits for research in clinical psychology through intuitive interpretations, and less costly trials.