The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
What to believe: Bayesian methods for data analysis
Abstract
Although Bayesian models of mind have attracted great interest from cognitive scientists, Bayesian methods for data analysis have not. This article reviews several advantages of Bayesian data analysis over traditional null-hypothesis significance testing. Bayesian methods provide tremendous flexibility for data analytic models and yield rich information about parameters that can be used cumulatively across progressive experiments. Because Bayesian statistical methods can be applied to any data, regardless of the type of cognitive model (Bayesian or otherwise) that motivated the data collection, Bayesian methods for data analysis will continue to be appropriate even if Bayesian models of mind lose their appeal.
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“The Bayesian framework was chosen specifically for its ability to represent parameter uncertainty directly through posterior distributions, offering interpretable estimates without relying on sampling distributions from null hypotheses, as required in frequentist approaches [79, 80, 81].”
From Interpersonal Synchronization of Autonomic Physiology via Mere Visual Contact (2026) · cites this paper as [80]
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Cites
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Prior distributions for variance parameters in hierarchical modelsPreprint
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Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications
Wagenmakers, E., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., Selker, R., Gronau, Q. F., Šmíra, M., Epskamp, S., et al. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1343-3 · Cited by 1,767
Bayesian estimation supersedes the t-test
Kruschke, J. K. (2013) · Journal of Experimental Psychology: General · doi:10.1037/a0029146 · Cited by 1,530
Self-citationMakowski, D., Ben-Shachar, M., & Lüdecke, D. (2019) · Journal of Open Source Software · doi:10.21105/joss.01541 · Cited by 1,483
Increasing Transparency Through a Multiverse Analysis
Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016) · Perspectives on Psychological Science · doi:10.1177/1745691616658637 · Cited by 1,512
(2013) · Cited by 1,351
Metadata
- Authors
- Kruschke, J. K.
- Affiliations
- Indiana University Bloomington (US)
- Corresponding author
- Kruschke, John K.
- ORCID
- Kruschke, John K.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2010-07
- Volume
- 14
- Issue
- 7
- Pages
- 293-300
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W1964645108
- Semantic Scholar
- 7d132ec7b7f94fe1397f6c1e8e5bb4fa42806ad1
- Cited by
- 342
- References
- 54
- Citations by year
- 2026: 17 · 2025: 21 · 2024: 26 · 2023: 17 · 2022: 20 · 2021: 25 · 2020: 26 · 2019: 24 · 2018: 16 · 2017: 24 · 2016: 22 · 2015: 25 · 2014: 20 · 2013: 21 · 2012: 20
- FWCI
- 2.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Trends in Cognitive Sciences (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- Bayesian analysis, null hypothesis significance testing, cumulative evidence, cognitive models, statistical inference
- MeSH terms
- Bayes Theorem, Humans, Research Design, Statistics as Topic, Reproducibility of Results
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