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]
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“BFs between 1/3 and 3 suggest that the data are insensitive to distinguish the alternative hypothesis from the null (Dienes, 2011).”
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“So, considering the limitations of null hypothesis significance testing (Dienes, 2011), Bayesian analyses were conducted to examine the evidence in favour of the null hypothesis (H0: no differences in destination memory when personal facts were transmitted to distinctive faces compared to…”
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“The Bayes Factors in this article were calculated according to the procedure recommended by Dienes [22] using the R script implemented by Baguley and Kaye [24].”
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“We also calculated Bayes factors (BF) that provides direct measures of the relative support that data provide to the competing hypothesis (Van De Schoot et al., 2017; Dienes, 2011; Makowski et al., 2019).”
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“Specifically, following Dienes (2011), we used a normally-distributed prior of effect sizes, centered on the observed difference in the previous experiment, and with a standard deviation of half that observed difference.”
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“We thus cannot rule out the possibility that the n ull results in those studies stemmed from underpow-ered experiments (Dienes 2011, Kruschke 2018).”
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“Unlike frequentist statistical approaches, which depend heavily on sample size to yield reliable p-values and power, Bayesian methods assess the strength of evidence for competing hypotheses irrespective of sample size (Dienes, 2011; Wagenmakers et al., 2018).”
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“A BF 10 < 1/3 is interpreted as supporting H0 while BF 10 > 3 supports H1 (Dienes, 2011), corresponding to our preregistration.”
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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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