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A tutorial on testing hypotheses using the Bayes factor.

Herbert Hoijtink1, Joris Mulder1, Caspar van Lissa1

  • 1Department of Methodology and Statistics.

Psychological Methods
|February 12, 2019
PubMed
Summary
This summary is machine-generated.

The Bayes factor offers a superior method for hypothesis evaluation in psychological research, moving beyond traditional significance testing. This approach quantifies evidence for all hypotheses, including the null, and facilitates continuous data updating.

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Area of Science:

  • Psychological Research Methods
  • Statistical Inference

Background:

  • Traditional null-hypothesis significance testing (NHST) provides dichotomous reject/do-not-reject decisions.
  • NHST struggles with quantifying evidence for the null hypothesis and handling multiple comparisons.
  • Bayesian approaches offer a more nuanced framework for hypothesis evaluation.

Purpose of the Study:

  • To introduce researchers to hypothesis evaluation using the Bayes factor.
  • To provide a practical, non-technical guide for applying Bayes factors in psychological research.
  • To demonstrate the utility of Bayes factors with ANOVA models using the R package 'bain'.

Main Methods:

  • The tutorial focuses on the applied aspects of Bayes factor calculation and interpretation.
  • Illustrations use Bayes factors within an ANOVA framework, leveraging the R package 'bain'.
  • Readers are encouraged to replicate analyses using provided R-codes and the 'bain' package.

Main Results:

  • The Bayes factor quantifies evidence in favor of each hypothesis, including the null.
  • It simplifies the evaluation of multiple hypotheses without complex adjustments for multiple testing.
  • Bayes factors allow for continuous updating of evidence as new data become available.

Conclusions:

  • Researchers can effectively evaluate hypotheses using Bayes factors after completing this tutorial.
  • The methods demonstrated are applicable beyond ANOVA models to various statistical contexts.
  • Adoption of Bayes factors can enhance the rigor and interpretability of psychological research findings.