LAUSR

Significance Bayes factors represent an informative alternative to P-values for reporting outcomes of hypothesis tests. They provide direct measures of the relative support that data provide to competing hypotheses and are able to quantify support for true null hypotheses. However, their use has been limited by several factors, including the requirement to specify alternative hypotheses and difficulties encountered in their calculation. Bayes factor functions (BFFs) overcome these difficulties by defining Bayes factors from classical test statistics and using standardized effect sizes to define alternative hypotheses. BFFs provide clear summaries of the outcome from a single experiment, eliminate arbitrary significance thresholds, and are ideal for combining evidence from replicated studies.