Four reasons to prefer Bayesian analyses over significance testing

Dienes, Zoltan and McLatchie, Neil (2018) Four reasons to prefer Bayesian analyses over significance testing. Psychonomic Bulletin and Review, 25 (1). pp. 207-218. ISSN 1069-9384

[img] PDF - Accepted Version
Download (243kB)
[img] PDF - Published Version
Available under License Creative Commons Attribution.

Download (675kB)


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.

Item Type: Article
Schools and Departments: School of Psychology > Psychology
Depositing User: Ellena Adams
Date Deposited: 28 Feb 2017 14:17
Last Modified: 22 Jul 2019 16:16

View download statistics for this item

📧 Request an update