Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist

Sarah Depaoli*, Rens van de Schoot

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research. Although it is very attractive to use Bayesian statistics, our personal experience has led us to believe that naively applying Bayesian methods can be dangerous for at least 3 main reasons: the potential influence of priors, misinterpretation of Bayesian features and results, and improper reporting of Bayesian results. To deal with these 3 points of potential danger, we have developed a succinct checklist: the WAMBS-checklist (When to worry and how to Avoid the Misuse of Bayesian Statistics). The purpose of the questionnaire is to describe 10 main points that should be thoroughly checked when applying Bayesian analysis. We provide an account of "when to worry" for each of these issues related to: (a) issues to check before estimating the model, (b) issues to check after estimating the model but before interpreting results,

Original languageEnglish
Pages (from-to)240-261
JournalPsychological Methods
Issue number2
Publication statusPublished - 2017


  • Bayesian checklist
  • Bayesian estimation
  • Convergence
  • Prior
  • Sensitivity analysis


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