A tutorial on testing hypotheses using the Bayes factor

Herbert Hoijtink*, Joris Mulder, Caspar van Lissa, Xin Gu

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)
357 Downloads (Pure)

Abstract

Learning about hypothesis evaluation using the Bayes factor could enhance psychological research. In contrast to null-hypothesis significance testing it renders the evidence in favor of each of the hypotheses under consideration (it can be used to quantify support for the null-hypothesis) instead of a dichotomous reject/do-not-reject decision; it can straightforwardly be used for the evaluation of multiple hypotheses without having to bother about the proper manner to account for multiple testing; and it allows continuous reevaluation of hypotheses after additional data have been collected (Bayesian updating). This tutorial addresses researchers considering to evaluate their hypotheses by means of the Bayes factor. The focus is completely applied and each topic discussed is illustrated using Bayes factors for the evaluation of hypotheses in the context of an ANOVA model, obtained using the R package bain. Readers can execute all the analyses presented while reading this tutorial if they download bain and the R-codes used. It will be elaborated in a completely nontechnical manner: what the Bayes factor is, how it can be obtained, how Bayes factors should be interpreted, and what can be done with Bayes factors. After reading this tutorial and executing the associated code, researchers will be able to use their own data for the evaluation of hypotheses by means of the Bayes factor, not only in the context of ANOVA models, but also in the context of other statistical models.
Original languageEnglish
Pages (from-to)539-556
JournalPsychological Methods
Volume24
Issue number5
DOIs
Publication statusPublished - 2019

Keywords

  • Bayes Factor
  • Bayesian error probabilities
  • CONSTRAINED HYPOTHESES
  • INEQUALITY
  • MODEL SELECTION
  • NEYMAN
  • P-VALUES
  • PREVALENCE
  • REPLICATION
  • bain
  • informative hypotheses
  • posterior probabilities

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