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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
(2005) · Springer eBooks · doi:10.1007/0-387-27255-0_9 · Cited by 20,333
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
Nelder, J. A., & Wedderburn, R. W. M. (1972) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344614 · Cited by 7,703
Gelman, A. (2006) · Bayesian Analysis · doi:10.1214/06-ba117a · Cited by 4,050
Bayesian Data Analysis, 2nd edn
Brix, A. (2005) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2004.00347_4.x · Cited by 2,588
A practical solution to the pervasive problems ofp values
Wagenmakers, E.-J. (2007) · Psychonomic Bulletin & Review · doi:10.3758/bf03194105 · Cited by 2,490
The Adaptive Character of Thought
Massaro, D. W., Friedman, D., & Anderson, J. R. (1991) · The American Journal of Psychology · doi:10.2307/1423253 · Cited by 2,174
Bayesian statistical inference for psychological research.
Edwards, W., Lindman, H., & Savage, L. J. (1963) · Psychological Review · doi:10.1037/h0044139 · Cited by 1,734
Prior distributions for variance parameters in hierarchical modelsPreprint
Gelman, A. (2004) · RePEc: Research Papers in Economics · Cited by 1,558
Generalized Linear Models, 2nd Edn.
Cheek, P. J., McCullagh, P., & Nelder, J. A. (1990) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.2307/2347392 · Cited by 1,528
Designing Experiments and Analysing Data: A Model Comparison Perspective.
Freeman, G. H., Maxwell, S. E., & Delaney, H. D. (1991) · Biometrics · doi:10.2307/2532173 · Cited by 1,229
Bayesian Modeling Using WinBUGS
Ntzoufras, I. (2008) · Wiley eBooks · doi:10.1002/9780470434567 · Cited by 1,009
Designing Experiments and Analyzing Data: A Model Comparison Perspective, Third Edition
Maxwell, S. E., & Delaney, H. D. (1999) · Medical Entomology and Zoology · Cited by 975
Bayesian brain : probabilistic approaches to neural coding
賢治, 銅., 信, 石., Pouget, A., & Rao, R. P. N. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/9780262042383.001.0001 · Cited by 823
The case for objective Bayesian analysis
Berger, J. (2006) · Bayesian Analysis · doi:10.1214/06-ba115 · Cited by 778
Bayesian Analysis for the Social Sciences
Jackman, S. D. (2009) · Wiley series in probability and statistics · doi:10.1002/9780470686621 · Cited by 774
A Language and Program for Complex Bayesian Modelling
Gilks, W., Thomas, A., & Spiegelhalter, D. (1994) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348941 · Cited by 760
Thomas, A., O'Hara, R., Ligges, U., & Sturtz, S. (2006) · Cited by 713
Probabilistic models of cognition: Conceptual foundations
Chater, N., Tenenbaum, J. B., & Yuille, A. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.007 · Cited by 573
Doing Bayesian Data Analysis: A Tutorial with R and BUGS
Barry, J. (2011) · Europe’s Journal of Psychology · doi:10.5964/ejop.v7i4.163 · Cited by 563
Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816772.006 · Cited by 529
Bayesian Methods for Data Analysis
Carlin, B., & Louis, T. (2008) · Chapman & Hall/CRC eBooks · doi:10.1201/b14884 · Cited by 505
Statistical Analysis and the Illusion of Objectivity
Berger, J., & Berry, D. (1988) · Infectious Diseases Newsletter · doi:10.1016/0278-2316(88)90057-6 · Cited by 448
Designing Experiments and Analyzing Data
Maxwell, S. E., & Delaney, H. D. (2003) · Routledge eBooks · doi:10.4324/9781410609243 · Cited by 395
Kruschke, J. K. (2010) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.72 · Cited by 343
Self-citationSample size determination : a review
Adcock, C. (1997) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.1111/1467-9884.00082 · Cited by 313
The Cambridge handbook of computational psychology
(2008) · Choice Reviews Online · doi:10.5860/choice.46-2369 · Cited by 309
The Cambridge Handbook of Computational Psychology
(2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816772 · Cited by 268
Wang, F., & Gelfand, A. (2002) · Statistical Science · doi:10.1214/ss/1030550861 · Cited by 249
Three case studies in the Bayesian analysis of cognitive models
Lee, M. D. (2008) · Psychonomic Bulletin & Review · doi:10.3758/pbr.15.1.1 · Cited by 177
Bayes factors: Prior sensitivity and model generalizability
Liu, C. C., & Aitkin, M. A. (2008) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2008.03.002 · Cited by 168
Bayesian approaches to associative learning: From passive to active learning
Kruschke, J. (2008) · Learning & Behavior · doi:10.3758/lb.36.3.210 · Cited by 160
Self-citationSample Size Calculations for Binomial Proportions Via Highest Posterior Density Intervals
Joseph, L., Wolfson, D., & du Berger, R. (1995) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348439 · Cited by 159
Modeling individual differences using Dirichlet processes
Navarro, D., Griffiths, T. L., Mark Steyvers, B., & Lee, M. D. (2006) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2005.11.006 · Cited by 150
Inference for a Bernoulli Process (a Bayesian View)
Lindley, D. V., & Phillips, L. D. (1976) · The American Statistician · doi:10.1080/00031305.1976.10479154 · Cited by 149
Dimensional Relevance Shifts in Category Learning
Kruschke, J. (1996) · Connection science · doi:10.1080/095400996116893 · Cited by 138
Self-citationBayesian statistical inference in psychology: Comment on Trafimow (2003).
Lee, M., & Wagenmakers, E. (2005) · Psychological Review · doi:10.1037/0033-295x.112.3.662 · Cited by 128
Modeling individual differences in cognition
Lee, M. D., & Webb, M. R. (2005) · Psychonomic Bulletin & Review · doi:10.3758/bf03196751 · Cited by 116
Bayesian sample size calculations for hypothesis testing
Weiss, R. (1997) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.1111/1467-9884.00075 · Cited by 107
Using Historical Data for Bayesian Sample Size Determination
Santis, F. D. (2006) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2006.00438.x · Cited by 91
What is the probability of replicating a statistically significant effect?
Miller, J. O. (2009) · Psychonomic Bulletin & Review · doi:10.3758/pbr.16.4.617 · Cited by 88
A hierarchical process-dissociation model.
Rouder, J. N., Lu, J., Morey, R. D., Sun, D., & Speckman, P. L. (2008) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.137.2.370 · Cited by 88
A Model of Knower‐Level Behavior in Number Concept Development
Lee, M., & Sarnecka, B. W. (2010) · Cognitive Science · doi:10.1111/j.1551-6709.2009.01063.x · Cited by 80
The Future of Statistics: A Bayesian 21st Century
Lindley, D. V. (1975) · Advances in Applied Probability · doi:10.2307/1426315 · Cited by 72
Acquired distinctiveness is controlled by stimulus relevance not correlation with reward.
George, D. N., & Pearce, J. M. (1999) · Journal of Experimental Psychology Animal Behavior Processes · doi:10.1037/0097-7403.25.3.363 · Cited by 71
Inference for a Bernoulli Process (A Bayesian View)
Lindley, D. V., & Phillips, L. D. (1976) · The American Statistician · doi:10.2307/2683855 · Cited by 67
Statistical evidence and sample size determination for Bayesian hypothesis testing
Santis, F. D. (2004) · Journal of Statistical Planning and Inference · doi:10.1016/s0378-3758(03)00198-8 · Cited by 55
Chater, N., & Oaksford, M. (2008) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199216093.003.0001 · Cited by 53
Designing Experiments and Analyzing Data: a Model Comparison Perspective, 2nd edn
Bailey, R. A. (2005) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2005.00368_9.x · Cited by 47
Oswald, C. J. P., Yee, B. K., Rawlins, J. N. P., Bannerman, D. M., Good, M. A., & Honey, R. C. (2001) · Behavioral Neuroscience · doi:10.1037/0735-7044.115.4.841 · Cited by 40
Some comments on Bayesian sample size determination
Joseph, L., Wolfson, D., & du Berger, R. (1995) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348442 · Cited by 37
BayesSDT: Software for Bayesian inference with signal detection theory
Lee, M. (2008) · Behavior Research Methods · doi:10.3758/brm.40.2.450 · Cited by 36
BayesGCM: Software for Bayesian inference with the generalized context model
Vanpaemel, W. (2009) · Behavior Research Methods · doi:10.3758/brm.41.4.1111 · Cited by 17
Cited by
Sorted by citations
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
