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The “Full Bayesian Significance Test e-value”, henceforth FBST ev, has received increasing attention across a range of disciplines including psychology. We show that the FBST evleads to four problems: (1) the FBST evcannot quantify evidence in favor of a null hypothesis and therefore also cannot discriminate “evidence of absence” from “absence of evidence”; (2) the FBST evis susceptible to sampling to a foregone conclusion; (3) the FBST evviolates the principle of predictive irrelevance, such that it is affected by data that are equally likely to occur under the null hypothesis and the alternative hypothesis; (4) the FBST evsuffers from the Jeffreys-Lindley paradox in that it does not include a correction for selection. These problems also plague the frequentist p-value. We conclude that although the FBST evmay be an improvement over the p-value, it does not provide a reasonable measure of evidence against the null hypothesis.

Despite an ongoing stream of lamentations, many empirical disciplines still treat the pvalue as the sole arbiter to separate the scientific wheat from the chaff. The continued reign of the pvalue is arguably due in part to a perceived lack of workable alternatives. In order to be workable, any alternative methodology must be (1) relevant: it has to address the practitioners’ research question, which—for better or for worse—most often concerns the test of a hypothesis, and less often concerns the estimation of a parameter; (2) available: it must have a concrete implementation for practitioners’ statistical workhorses such as the ttest, regression, and ANOVA; and (3) easy to use: methods that demand practitioners switch to the theoreticians’ programming tools will face an uphill struggle for adoption. The above desiderata are fulfilled by Harold Jeffreys’s Bayes factor methodology as implemented in the open-source software JASP. We explain Jeffreys’s methodology and showcase its practical relevance with two examples.