Informed Bayesian t-Tests. Issue 2 (2nd April 2020) - Gronau, Quentin F.; Ly, Alexander; Wagenmakers, Eric-Jan
Abstract: Across the empirical sciences, few statistical procedures rival the popularity of the frequentist t -test. In contrast, the Bayesian versions of the t -test have languished in obscurity. In recent years, however, the theoretical and practical advantages of the Bayesian t -test have become increasingly apparent and various Bayesian t -tests have been proposed, both objective ones (based on general desiderata) and subjective ones (based on expert knowledge). Here, we propose a flexible t -prior for standardized effect size that allows computation of the Bayes factor by evaluating a single numerical integral. This specification contains previous objective and subjective t -test Bayes factors as special cases. Furthermore, we propose two measures for informed prior distributions that quantify the departure from the objective Bayes factor desiderata of predictive matching and information consistency. We illustrate the use of informed prior distributions based on an expert prior elicitation effort. Supplementary materials for this article are available online.