The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective

Kruschke, John K.; Liddell, Torrin M.

doi:10.3758/s13423-016-1221-4

Cited by 1,288Statistical Methods and Bayesian InferenceMeta-analysis and systematic reviewsProbability and Statistical Research

The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective

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Kruschke, John K., Liddell, Torrin M.·2018·Psychonomic Bulletin & Review

DOI 10.3758/s13423-016-1221-4

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Abstract

In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.

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A Bayesian approach was selected because it estimates the full probability distribution of treatment effects rather than relying on binary significance testing, allowing more informative interpretation of effect sizes and uncertainty, and is well suited to small or imbalanced evidence networks.
“A Bayesian approach was selected because it estimates the full probability distribution of treatment effects rather than relying on binary significance testing, allowing more informative interpretation of effect sizes and associated uncertainty [40, 41].”
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“To test for equivalence, the effect of the interface (HIM vs. DIM) was assessed via Bayesian parameter estimation using the Highest Density Interval (HDI) plus a Region of Practical Equivalence (ROPE) decision rule (Kruschke, 2011; Kruschke and Liddell, 2018; Kruschke and Meredith, 2018).”
Method
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The Bayesian New Statistics (Public)
Kruschke, J. K., & Liddell, T. M. (2017) · Open Science Framework · doi:10.17605/osf.io/wntsa
Self-citation
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Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications
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New methods for meta-analysis under a fixed effects framework, with frequentist and Bayesian estimation.
Islas, D., & Penelope, C. (2015)

Authors: Kruschke, J. K., & Liddell, T. M.
Affiliations: Indiana University Bloomington (US)
Corresponding author: Kruschke, John K.
ORCID: Kruschke, John K.
Venue: Psychonomic Bulletin & Review
Publisher: Springer Science and Business Media LLC
Published: 2017-02-07
Volume: 25
Issue: 1
Pages: 178-206
ISSN: 1069-9384 (ISSN-L)
1531-5320
DOI: 10.3758/s13423-016-1221-4
OpenAlex: W3124625789
Cited by: 1,288
References: 104
Citations by year: 2026: 137 · 2025: 171 · 2024: 156 · 2023: 133 · 2022: 153 · 2021: 186 · 2020: 133 · 2019: 98 · 2018: 88 · 2017: 32 · 2015: 1
FWCI: 66.75
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springer.com/tdm (unspecified)
Locations: Psychonomic Bulletin & Review (publishedVersion)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
OSF Preprints (OSF Preprints) (submittedVersion)
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: new statistics, Bayesian analysis, hypothesis testing, confidence intervals, credible intervals, meta-analysis, power analysis, randomized controlled trials
MeSH terms: Bayes Theorem, Humans, Research Design, Statistics as Topic, Meta-Analysis as Topic, Uncertainty

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The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective

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