Bayesian inference for psychology. Part II: Example applications with JASP

Abstract

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.

Incidental Haptic Sensations May Not Influence Social Judgments: A Purely Confirmatory Replication Attempt of Study 1 by Ackerman, Nocera, & Bargh (2010)

“…The analyses were executed in JASP (jasp-stats.org; JASP team, 2017; Wagenmakers et al, in press;2017). On the OSF, JASP output can be viewed without having JASP installed.…”

Introduction to Bayesian Inference for Psychology

“…Once the models were built, the researcher had only to "turn the crank" of probabilistic inference and posterior probabilities are obtained through standard mechanisms that rely on little other than the sum and product rules of probability. As this example illustrates, the practical computation of posterior probabilities will often rely on calculus or numerical integration methods; several papers in this special issue deal with computational software that is available (39,58,64,65).…”

“…Recent computational developments have made the computation of Bayes factors more tractable, especially for common scenarios (64,65). For uncommon or complex scenarios, one might resort to reporting a different model comparison metric that does not rely on the marginal likelihood, such as the various information criteria (AIC, BIC, DIC, WAIC) or leave-one-out cross validation (LOOCV; see 56,59,60).…”

“…While we maintain that the mathematical underpinnings serve understanding of these methods in important ways, we should also point out that recent developments regarding Bayesian statistical software packages (e.g., 39,58,64,65) have made it possible to perform many kinds of Bayesian analyses without the need to carry out any of the technical mathematical derivations. The mathematical basis we present here remains, of course, more general.…”

Involuntary attentional orienting to counterproductive exogenous cues

“…Choosing a narrower distribution (i.e., a lower value of r), would result in a high level of similarity between H 1 and H 0 , making the tests uninformative. Conversely, choosing an unreasonably wide distribution would result in the BF favouring the null too heavily, by placing too much weight on extreme effect size values (Etz et al, 2017;Rouder et al, 2009;Wagenmakers et al, 2017). In addition to calculating the BF, robustness checks and sequential analyses (combined with a robustness analysis) of the BF were carried out and visualized in plots.…”