The DOI matches its Crossref record; 59 of the 60 references it cites were found in a registry or an index.
Bayesian Versus Orthodox Statistics: Which Side Are You On?
Open accessAbstract
Researchers are often confused about what can be inferred from significance tests. One problem occurs when people apply Bayesian intuitions to significance testing—two approaches that must be firmly separated. This article presents some common situations in which the approaches come to different conclusions; you can see where your intuitions initially lie. The situations include multiple testing, deciding when to stop running participants, and when a theory was thought of relative to finding out results. The interpretation of nonsignificant results has also been persistently problematic in a way that Bayesian inference can clarify. The Bayesian and orthodox approaches are placed in the context of different notions of rationality, and I accuse myself and others as having been irrational in the way we have been using statistics on a key notion of rationality. The reader is shown how to apply Bayesian inference in practice, using free online software, to allow more coherent inferences from data.
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“In terms of statistics, a Bayesian approach might be more appropriate than Fisherian statistics [56] as the Fisher approach is not appropriate with limited data [48].”
From Agile science: creating useful products for behavior change in the real world (2016) · cites this paper as [56]
Citations
Cites
Probability theory the logic of science
Jaynes, E. T., & Bretthorst, G. L. (2003) · Cited by 4,600
Probability Theory: The Logic of Science
Jaynes, E. T. (2003) · doi:10.1017/cbo9780511790423 · Cited by 4,045
Bayesian t tests for accepting and rejecting the null hypothesis
Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009) · Psychonomic Bulletin & Review · doi:10.3758/pbr.16.2.225 · Cited by 3,688 · cited 3×
“For a p value, if the null is true, any value in the interval 0 to 1 is equally likely no matter how much data you collect (Rouder et al., 2009).”
BackgroundMethodVannieuwenhoven, N. (2020) · McGill-Queen's University Press eBooks · doi:10.2307/j.ctvw1d7dg.9 · Cited by 3,370
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Bargh, J. A., Chen, M. I.-C., & Burrows, L. (1996) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.71.2.230 · Cited by 2,646
A practical solution to the pervasive problems ofp values
Wagenmakers, E.-J. (2007) · Psychonomic Bulletin & Review · doi:10.3758/bf03194105 · Cited by 2,490 · cited 1×
“For this reason, sooner or later, you are guaranteed to get a significant result if you run subjects long enough and stop when you get the p value you want (e.g., Wagenmakers, 2007).”
BackgroundHARKing: Hypothesizing after the results are known
Kerr, N. L. (1998) · Personality and Social Psychology Review · doi:10.1207/s15327957pspr0203_4 · Cited by 1,989
Research works printed but not linked: 2
- Blume J. D., Handbook of the philosophy of statistics
- Jeffreys H., 1961, The theory of probability
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Sorted by citations
Using Bayes to get the most out of non-significant results
Dienes, Z. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00781 · Cited by 1,998
“…with orthodox statistics, unless pre-specified as possible by one’s stopping rule (Armitage et al., 1969); by contrast, with Bayes, one can always collect more participants until the data are sensitive enough, that is, B < 1/3 or B > 3; see e.g., Berger and Wolpert (1988), Dienes (2008, 2011).”
BackgroundInfluentialSelf-citationBayesian inference for psychology. Part II: Example applications with JASP
Wagenmakers, E., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Selker, R., Gronau, Q. F., Dropmann, D., boutin, B., et al. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1323-7 · Cited by 1,788
“…(i.e., Bayesian model averaging in regression, Clyde, 2016), the informative model comparison approach (e.g., Gu, Mulder, Decovi´c, & Hoijtink, 2014; Gu, 2016; Mulder, 2014, 2016), and a more flexible and subjective prior specification approach (e.g., Dienes, 2011, 2014, 2016; Gronau et al., 2017).”
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
“Dienes (2008, 2011) provided another analytical solution, with a corresponding online calculator.”
BackgroundMethod(2013) · Cited by 1,351
Metadata
- Authors
- Dienes, Z.
- Affiliations
- University of Sussex (GB)
- Corresponding author
- Dienes, Zoltan
- ORCID
- Dienes, Zoltan
- Venue
- Perspectives on Psychological Science
- Publisher
- SAGE Publications
- Published
- 2011-05
- Volume
- 6
- Issue
- 3
- Pages
- 274-290
- ISSN
- 1745-6916 (ISSN-L)
- 1745-6924
- OpenAlex
- W2055754299
- Cited by
- 938
- References
- 60
- Citations by year
- 2026: 25 · 2025: 35 · 2024: 39 · 2023: 46 · 2022: 64 · 2021: 99 · 2020: 87 · 2019: 84 · 2018: 98 · 2017: 82 · 2016: 76 · 2015: 68 · 2014: 48 · 2013: 42 · 2012: 33
- FWCI
- 6.30
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Locations
- Perspectives on Psychological Science (publishedVersion)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by)
- 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 › Probability and Statistical Research
- Keywords
- Bayesian inference, significance testing, null hypothesis significance testing, multiple comparisons, stopping rules
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