BUCSS: Bias and Uncertainty Corrected Sample Size

Implements sample size planning for a future study from the summary statistics of a prior study, correcting the prior effect for publication bias and uncertainty so the planned study attains its target statistical power more reliably than methods that take a sample effect size at face value. Planners cover the one-sample, paired, independent, and Welch t tests; between-subjects analysis of variance and covariance; within-subjects and split-plot analysis of variance; a regression coefficient, the squared multiple correlation, and a subset of predictors; the Pearson correlation; a two-group multivariate comparison; and the chi square difference between nested models. The regression planners can treat the predictors as fixed or as sampled, the within-subjects and split-plot planners accept a sphericity correction, and each plan reports the effect size it is built on. A simulation function audits how often a plan attains its target power. The method is described in Anderson, Kelley, and Maxwell (2017) <doi:10.1177/0956797617723724> and extended to replication studies in Anderson and Kelley (2024) <doi:10.1037/met0000520>.

install.packages("BUCSS")

Try the BUCSS package in your browser