The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Rejecting or Accepting Parameter Values in Bayesian Estimation
Open accessAbstract
This article explains a decision rule that uses Bayesian posterior distributions as the basis for accepting or rejecting null values of parameters. This decision rule focuses on the range of plausible values indicated by the highest density interval of the posterior distribution and the relation between this range and a region of practical equivalence (ROPE) around the null value. The article also discusses considerations for setting the limits of a ROPE and emphasizes that analogous considerations apply to setting the decision thresholds for p values and Bayes factors.
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“There is no uniquely correct ROPE; however, by convention, the ROPE range is often set at half the size of Cohen’s definition of small effect size (i.e. 0.2 [306];) resulting in ROPE values of ±0.1 and ±0.05 for standardized mean differences (e.g. Cohen’s d = group 1 mean – group 2 mean/pooled standard deviation) and standardized regression coefficients (i.e. sβ = coefficient from a regression on”
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Cites
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The ASA Statement on p -Values: Context, Process, and Purpose
Wasserstein, R. L., & Lazar, N. A. (2016) · The American Statistician · doi:10.1080/00031305.2016.1154108 · Cited by 6,306 · cited 1×
“Discrete decisions should be avoided if possible, because such decisions encourage people to ignore the magnitude of the parameter value and its uncertainty (e.g., Cumming, 2014; Kruschke & Liddell, 2018b; Wasserstein & Lazar, 2016, and many references cited therein).”
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Lakens, D. (2022) · Collabra Psychology · doi:10.1525/collabra.33267 · Cited by 1,962
Makowski, D., Ben-Shachar, M., & Lüdecke, D. (2019) · Journal of Open Source Software · doi:10.21105/joss.01541 · Cited by 1,483
“Such a Test for Practical Equivalence , implemented via equivalence_test() , is based on the “HDI+ROPE decision rule” (Kruschke, 2018) to check whether parameter values should be accepted or rejected against an explicitly formulated “null hypothesis” (i.e., a ROPE).”
BackgroundMethodBayesian Analysis Reporting Guidelines
Kruschke, J. K. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01177-7 · Cited by 525
Self-citationWhy Hypothesis Testers Should Spend Less Time Testing Hypotheses
Scheel, A. M., Tiokhin, L., Isager, P. M., & Lakens, D. (2021) · Perspectives on Psychological Science · doi:10.1177/1745691620966795 · Cited by 382
“This problem is not solved with mere practice—forcing researchers to specify what would falsify their hypotheses when they have no theoretical basis for doing so can lead to testing against arbitrary values (Kruschke, 2018) and runs the risk of replacing one mindless ritual with another.”
BackgroundImproving inferences about null effects with Bayes factors and equivalence tests.
Lakens, D., McLatchie, N., Isager, P. M., Scheel, A. M., & Dienes, Z. (2020) · The Journals of Gerontology: Series B · doi:10.1093/geronb/gby065 · Cited by 279
“The TOST approach is equivalent to examining whether a 90% CI (for alpha = 0.05) around the effect falls between ΔL and ΔU, and concluding equivalence if the 90% CI does not contain either equivalence bound (Westlake, 1972; for a related Bayesian procedure, see Kruschke, 2011, 2018).”
BackgroundMethodInfluential
Metadata
- Authors
- Kruschke, J. K.
- Affiliations
- Indiana University Bloomington (US)
- Corresponding author
- Kruschke, John K.
- ORCID
- Kruschke, John K.
- Venue
- Advances in Methods and Practices in Psychological Science
- Publisher
- SAGE Publications
- Published
- 2018-05-08
- Volume
- 1
- Issue
- 2
- Pages
- 270-280
- ISSN
- 2515-2459 (ISSN-L)
- 2515-2467
- OpenAlex
- W2801776818
- Cited by
- 734
- References
- 51
- Citations by year
- 2026: 99 · 2025: 128 · 2024: 128 · 2023: 98 · 2022: 85 · 2021: 93 · 2020: 67 · 2019: 25 · 2018: 10
- FWCI
- 41.08
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Advances in Methods and Practices in Psychological Science (publishedVersion)
- IUScholarWorks Open (Indiana University) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Funders
- U.S. Food and Drug Administration
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- Bayesian hypothesis testing, null hypothesis rejection, highest density interval, region of practical equivalence, posterior distribution, decision threshold, p-values, Bayes factor, parameter values
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