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Four reasons to prefer Bayesian analyses over significance testing
Version 2 2023-06-13, 15:17
Version 1 2023-06-09, 05:20
journal contribution
posted on 2023-06-13, 15:17 authored by Zoltan DienesZoltan Dienes, Neil McLatchieInference using significance testing and Bayes factors is compared and contrasted in five case studies based on real research. The first study illustrates that the methods will often agree, both in motivating researchers to conclude that H1 is supported better than H0, and the other way round, that H0 is better supported than H1. The next four, however, show that the methods will also often disagree. In these cases, the aim of the paper will be to motivate the sensible evidential conclusion, and then see which approach matches those intuitions. Specifically, it is shown that a high-powered non-significant result is consistent with no evidence for H0 over H1 worth mentioning, which a Bayes factor can show, and, conversely, that a low-powered non-significant result is consistent with substantial evidence for H0 over H1, again indicated by Bayesian analyses. The fourth study illustrates that a high-powered significant result may not amount to any evidence for H1 over H0, matching the Bayesian conclusion. Finally, the fifth study illustrates that different theories can be evidentially supported to different degrees by the same data; a fact that P-values cannot reflect but Bayes factors can. It is argued that appropriate conclusions match the Bayesian inferences, but not those based on significance testing, where they disagree.
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Publication status
- Published
File Version
- Published version
Journal
Psychonomic Bulletin and ReviewISSN
1069-9384Publisher
Springer VerlagExternal DOI
Issue
1Volume
25Page range
207-218Department affiliated with
- Psychology Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2017-02-28First Open Access (FOA) Date
2017-04-03First Compliant Deposit (FCD) Date
2017-02-28Usage metrics
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