Cited by 742Statistical Methods and Bayesian InferenceStatistical Distribution Estimation and ApplicationsDecision-Making and Behavioral Economics

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Rejecting or Accepting Parameter Values in Bayesian Estimation

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Kruschke, John K.·2018·Advances in Methods and Practices in Psychological Science

DOI 10.1177/2515245918771304

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Abstract

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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“This “region of practical equivalence” (ROPE) from a Bayesian perspective [19] corresponds to the range of posterior estimates generated from randomly selected token allocations for each bin between 0 and 20.”
Background
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Nonhuman primates learn adjacent dependencies but fail to learn nonadjacent dependencies in a statistical learning task with a salient cue
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“We used a conservative ROPE in which only an estimated change greater than what would represent a Cohen's d of .9 (±.45 the standard deviation of discriminability or RT) was taken to represent the presence of an effect (Kruschke, 2018; McCartney & Rosenthal, 2000).”
Method
The sooner the better: clinical and neural correlates of impulsive choice in Tourette disorder
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Repetition effects in action planning reflect effector- but not hemisphere-specific coding
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Growth Response of Reef-Building Corals to Ocean Acidification Is Mediated by Interplay of Taxon-Specific Physiological Parameters
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With Bayesian estimation one can get all that Bayes factors offer, and more
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“See also Kruschke (2018); a less detailed but related account on this is also given by Williams, Bååth, and Philipp (2017).”
BackgroundInfluential
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“The values of the ROPEs are chosen according to recommendations given in [45].”
Method
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“Bayesian parameter estimation with 95% highest density intervals (HDIs) not encompassing zero were considered evidence for an e ﬀ ect, conditional on the assumption that the e ﬀ ect exists (Kruschke, 2018).”
Valenced Priming with Acquired Affective Concepts in Music
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“…within a region of practical equivalence (ROPE), defined as parameter values that are sufficiently close to 0 to be considered equivalent to the null for practical purposes [49] and mathematically defined as a standardized effect size of 0.1 (ie, half of a small effect as defined by Cohen [50]).”
BackgroundInfluential
On the relationship between complexity of primary occupation and L2 varietal behavior in adult migrants in Austria
Wirtz, M. A. (2024) · Linguistics Vanguard · doi:10.1515/lingvan-2023-0069 · Cited by 4
A Tale of Two Suggestions: Action and Diagnosis Recommendations for Responding to Robot Failure
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“ROPE is set based on recommendations by Kruschke [27]. tions), CDX captures participants’ likelihood of following those suggestions and is a measure of operator compliance with decision support [28].”
Method
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“The concept of practically equal to is borrowed from the concept underlying the region of practical equivalence (ROPE), indifference zone, and region of equivalence (e.g., Carlin & Louis, 2008; Kruschke, 2018).”
Background
Response bias reveals the role of interhemispheric inhibitory networks in movement preparation and executionPreprint
Puri, R., & Hinder, M. R. (2021) · doi:10.31234/osf.io/na96e · Cited by 4
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Chakroun, K., Wiehler, A., Wagner, B., Mathar, D., Ganzer, F., van Eimeren, T., Sommer, T., & Peters, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.29.509499 · Cited by 4
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Morehouse, K. N., Rouduri, V., Cunningham, W., & Charlesworth, T. E. S. (2023) · Research Square · doi:10.21203/rs.3.rs-2922677/v1 · Cited by 4
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“No decision was made if the 89% HDI partially overlapped ROPE (Kruschke 2018, Puri et al. 2023, Opie et al. 2024).”
A Bayesian approach for evaluating equivalence over multiple groups, and comparison with frequentist tost
Weusten, J. J. A. M., Kim, J. Y., Giacoletti, K. E. D., Vázquez, J., & Santos, P. D. L. (2024) · Journal of Applied Statistics · doi:10.1080/02664763.2023.2297150 · Cited by 3
“The literature provides examples in which Bayesian approaches are employed in comparing multiple means [6,16], for equivalence testing of two group means [10,11,21], and for omnibus non-inferiority testing for ANOVA and linear regression analyses [3].”
Targeting Daily Positive Events to Improve Emotional and Functional Well-Being in Adults With Fibromyalgia: Insights From the LARKSPUR Randomized Controlled Trial
Ong, A. D., Wilcox, K. T., Reid, M. C., Wethington, E., Cintron, D. W., Addington, E. L., Goktas, S., & Moskowitz, J. T. (2024) · Journal of Medical Internet Research · doi:10.2196/54678 · Cited by 5
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“Second, a Bayesian analysis delivers the probability of a hypothesis, given the data, in contrast to the probability of the data given the null hypothesis (Kruschke, 2018).”
Influential
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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
DOI: 10.1177/2515245918771304
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
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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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Rejecting or Accepting Parameter Values in Bayesian Estimation

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