What to believe: Bayesian methods for data analysis

Kruschke, John K.

doi:10.1016/j.tics.2010.05.001

Cited by 342Statistical Methods and Bayesian InferenceBayesian Modeling and Causal InferenceDecision-Making and Behavioral Economics

What to believe: Bayesian methods for data analysis

Kruschke, John K.·2010·Trends in Cognitive Sciences

DOI 10.1016/j.tics.2010.05.001

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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 represents parameter uncertainty directly through posterior distributions, offering interpretable estimates without relying on sampling distributions from null hypotheses.
“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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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)
DOI: 10.1016/j.tics.2010.05.001
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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