Cited by 754Statistical Methods and Bayesian InferenceMeta-analysis and systematic reviewsBayesian Modeling and Causal Inference

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A tutorial on a practical Bayesian alternative to null-hypothesis significance testing

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Masson, Michael E. J.·2011·Behavior Research Methods

DOI 10.3758/s13428-010-0049-5

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Abstract

Null-hypothesis significance testing remains the standard inferential tool in cognitive science despite its serious disadvantages. Primary among these is the fact that the resulting probability value does not tell the researcher what he or she usually wants to know: How probable is a hypothesis, given the obtained data? Inspired by developments presented by Wagenmakers (Psychonomic Bulletin & Review, 14, 779-804, 2007), I provide a tutorial on a Bayesian model selection approach that requires only a simple transformation of sum-of-squares values generated by the standard analysis of variance. This approach generates a graded level of evidence regarding which model (e.g., effect absent [null hypothesis] vs. effect present [alternative hypothesis]) is more strongly supported by the data. This method also obviates admonitions never to speak of accepting the null hypothesis. An Excel worksheet for computing the Bayesian analysis is provided as supplemental material.

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Authors: Masson, M. E. J.
Affiliations: University of Victoria (CA)
Corresponding author: Masson, Michael E. J.
ORCID: Masson, Michael E. J.
Venue: Behavior Research Methods
Publisher: Springer Science and Business Media LLC
Published: 2011-02-08
Volume: 43
Issue: 3
Pages: 679-690
ISSN: 1554-351X (ISSN-L)
1554-3528
DOI: 10.3758/s13428-010-0049-5
OpenAlex: W2151069696
Cited by: 752
References: 44
Citations by year: 2026: 7 · 2025: 21 · 2024: 29 · 2023: 54 · 2022: 39 · 2021: 62 · 2020: 61 · 2019: 87 · 2018: 84 · 2017: 63 · 2016: 66 · 2015: 55 · 2014: 53 · 2013: 45 · 2012: 22
FWCI: 34.66
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springer.com/tdm (tdm)
Locations: Behavior Research Methods (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords: Bayesian model selection, null hypothesis significance testing, Bayes factor, analysis of variance, sums of squares, evidence rating, null hypothesis, effect size
MeSH terms: Bayes Theorem, Data Interpretation, Statistical, Probability, Research Design, Models, Statistical

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