Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

Across the social sciences, researchers have overwhelmingly used the classical statistical paradigm to draw conclusions from data, often focusing heavily on a single number: p. Recent years, however, have witnessed a surge of interest in an alternative statistical paradigm: Bayesian inference, in which probabilities are attached to parameters and models. We feel it is informative to provide statistical conclusions that go beyond a single number, and -regardless of one's statistical preference-it can be prudent to report the results from both the classical and the Bayesian paradigm. In order to promote a more inclusive and insightful approach to statistical inference we show how the opensource software program JASP (https://jasp-stats.org) provides a set of comprehensive Bayesian reanalyses from just a few commonly-reported summary statistics such as t and N . These Bayesian reanalyses allow researchers -and also editors, reviewers, readers, and reporters-to quantify evidence on a continuous scale, assess the robustness of that evidence to changes in the prior distribution, and gauge which posterior parameter ranges are more credible than others by examining the posterior distribution of the effect size. The procedure is illustrated using the seminal Festinger and Carlsmith (1959) study on cognitive dissonance.

Booming in business and a staple analysis in medical trials, the A/B test assesses the effect of an intervention or treatment by comparing its success rate with that of a control condition. Across many practical applications, it is desirable that (1) evidence can be obtained in favor of the null hypothesis that the treatment is ineffective; (2) evidence can be monitored as the data accumulate; (3) expert prior knowledge can be taken into account. Most existing approaches do not fulfill these desiderata. Here we describe a Bayesian A/B procedure based on Kass and Vaidyanathan (1992) that allows one to monitor the evidence for the hypotheses that the treatment has either a positive effect, a negative effect, or, crucially, no effect. Furthermore, this approach enables one to incorporate expert knowledge about the relative prior plausibility of the rival hypotheses and about the expected size of the effect, given that it is non-zero. To facilitate the wider adoption of this Bayesian procedure we developed the abtest package in R. We illustrate the package options and the associated statistical results with a fictitious business example and a real data medical example.

Despite an ongoing stream of lamentations, many empirical disciplines still treat the p value as the sole arbiter to separate the scientific wheat from the chaff. The continued reign of the p value 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 t test, 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.