Using Bayes factor hypothesis testing in neuroscience to establish evidence of absence
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Most neuroscientists would agree that for brain research to progress, we have to know which experimental manipulations have no effect as much as we must identify those that do have an effect. The dominant statistical approaches used in neuroscience rely on P values and can establish the latter but not the former. This makes non-significant findings difficult to interpret: do they support the null hypothesis or are they simply not informative? Here we show how Bayesian hypothesis testing can be used in neuroscience studies to establish both whether there is evidence of absence and whether there is absence of evidence. Through simple tutorial-style examples of Bayesian t-tests and ANOVA using the open-source project JASP, this article aims to empower neuroscientists to use this approach to provide compelling and rigorous evidence for the absence of an effect.
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Non-significant p-values are not sufficient evidence to claim the absence of an effect, as they may also reflect insufficient power to detect a small non-zero effect.
“Although this is common practice, non-significant p-values are not sufficient evidence to claim the absence of an effect, as they may also reflect insufficient power to detect a small non-zero effect (Keysers et al. 2020).”
From Evidence for unconscious priming using a Bayesian single-subject approach (2026) · cites this paper as Keysers et al. 2020
Bayesian inference provides the ability to obtain evidence in favor of the null hypothesis and to discriminate between absence of evidence and evidence of absence.
“including (1) the ability to obtain evidence in favor of the null hypothesis and discriminate between “absence of evidence” and “evidence of absence” (Dienes, 2014; Keysers, Gazzola, & Wagenmakers, 2020);”
From The JASP guidelines for conducting and reporting a Bayesian analysis (2021) · cites this paper as Keysers, Gazzola, & Wagenmakers, 2020
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- Authors
- Keysers, C., Gazzola, V., & Wagenmakers, E.-J.
- Affiliations
- Netherlands Institute for Neuroscience (NL)
- University of Amsterdam (NL)
- Corresponding author
- Keysers, Christian
- Venue
- Nature Neuroscience
- Published
- 2020-06-29
- Volume
- 23
- Issue
- 7
- Pages
- 788-799
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC7610527
- OpenAlex
- W3037007306
- Cited by
- 654
- References
- 41
- Citations by year
- 2026: 69 · 2025: 92 · 2024: 127 · 2023: 118 · 2022: 118 · 2021: 98 · 2020: 29 · 2019: 1
- FWCI
- 14.74
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-10-06)
- Open access
- green
- pure.knaw.nl
- Licence
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- KNAW research portal (Royal Academy of Art and Sciences (KNAW)) (publishedVersion, other-oa)
- PubMed Central (acceptedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
- KNAW Research Portal (The Royal Netherlands Academy of Arts and Sciences) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (453-15-009, 452-14-015)
- H2020 European Research Council (758703)
- 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 › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- Bayes factor, evidence of absence, absence of evidence, Bayesian hypothesis testing, null hypothesis, p-values, Bayesian t-test, JASP
- MeSH terms
- Animals, Bayes Theorem, Brain, Humans, Neurosciences, Research Design
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