Abstract
The use of Bayes factors is becoming increasingly common in psychological sciences. Thus, it is important that researchers understand the logic behind the Bayes factor in order to correctly interpret it, and the strengths of weaknesses of the Bayesian approach. As education for psychological scientists focuses on frequentist statistics, resources are needed for researchers and students who want to learn more about this alternative approach. The aim of the current article is to provide such an overview to a psychological researcher. We cover the general logic behind Bayesian statistics, explain how the Bayes factor is calculated, how to set the priors in popular software packages to reflect the prior beliefs of the researcher, and finally provide a set of recommendations and caveats for interpreting Bayes factors.
Original language | English |
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Journal | Psychological Methods |
Early online date | 15 Nov 2021 |
DOIs | |
Publication status | E-pub ahead of print - 15 Nov 2021 |
Keywords
- 501001 General psychology
- 501001 Allgemeine Psychologie
- 101018 Statistics
- 101018 Statistik
- 509013 Social statistics
- 509013 Sozialstatistik
- B-hacking
- Bayes' theorem
- INCENTIVES
- JASP
- PSYCHOLOGY
- T TESTS
- p-hacking
- prior distributions
- Bayes’ theorem