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Four reasons to prefer Bayesian analyses over significance testing

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posted on 2023-06-13, 15:17 authored by Zoltan DienesZoltan Dienes, Neil McLatchie
Inference 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.

History

Publication status

  • Published

File Version

  • Published version

Journal

Psychonomic Bulletin and Review

ISSN

1069-9384

Publisher

Springer Verlag

Issue

1

Volume

25

Page range

207-218

Department affiliated with

  • Psychology Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2017-02-28

First Open Access (FOA) Date

2017-04-03

First Compliant Deposit (FCD) Date

2017-02-28

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