The JASP guidelines for conducting and reporting a Bayesian analysis
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THEORETICAL REVIEW
The JASP guidelines for conducting and reporting a Bayesian analysis 1 · Fabian Dablander1 · Koen Derks2 · Tim Draws1 · ¨ Johnny van Doorn1 · Don van den Bergh1 · Udo Bohm 3 1 1 ˇ Alexander Etz · Nathan J. Evans · Quentin F. Gronau · Julia M. Haaf1 · Max Hinne1 · Simon Kucharsky´ 1 · Alexander Ly1,4 · Maarten Marsman1 · Dora Matzke1 · Akash R. Komarlu Narendra Gupta1 · Alexandra Sarafoglou1 · Angelika Stefan1 · Jan G. Voelkel5 · Eric-Jan Wagenmakers1
© The Author(s) 2020
Abstract Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general. Keywords Bayesian inference · Scientific reporting · Statistical software In recent years, Bayesian inference has become increasingly popular, both in statistical science and in applied fields such as psychology, biology, and econometrics (e.g., Andrews & Baguley, 2013; Vandekerckhove, Rouder, & Kruschke, 2018). For the pragmatic researcher, the adoption of the Bayesian framework brings several advantages over the standard framework of frequentist null-hypothesis significance testing (NHST), including (1) the ability to obtain evidence in favor of the null hypothesis and discriminate between “absence of evidence” and “evidence of absence” (Dienes, 2014; Keysers, Gazzola, & Wagenmakers, 2020); (2) the ability to take into account prior knowledge to construct a more informative test
Johnny van Doorn
[email protected] 1
University of Amsterdam, Amsterdam, Netherlands
2
Nyenrode Business University, Breukelen, Netherlands
3
University of California, Irvine, California, USA
4
Centrum Wiskunde & Informatica, Amsterdam, Netherlands
5
Stanford University, Stanford, California, USA
(Gronau, Ly, & Wagenmakers, 2020; Lee & Vanpaemel, 2018); and (3) the ability to monitor the evidence as the data accumulate (Rouder, 2014). However, the relative novelty of conducting Bayesian analyses in applied fields means that there are no detailed reporting standards, and this in turn may frustrate the broader adoption and proper interpretation of the Bayesian framework. Several recent statistical guidelines include information on Bayesian inference, but these guidelines are either minimalist (Appelbaum et al., 2018; The BaSiS group, 2001), focus only on relatively complex statistical tests (Depaoli & Schoot, 2017), are too specific to a certain field (Spiegelhalter, Myles, Jones, & Abrams, 2000; Sung et al., 2005), or do not cover the full inferential process (Jarosz & Wiley, 2014).
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