Conducting Meta-Analyses Based on p Values: Reservations and Recommendations for Applying p -Uniform and p -Curve
Open accessAbstract
Because of overwhelming evidence of publication bias in psychology, techniques to correct meta-analytic estimates for such bias are greatly needed. The methodology on which the p-uniform and p-curve methods are based has great promise for providing accurate meta-analytic estimates in the presence of publication bias. However, in this article, we show that in some situations, p-curve behaves erratically, whereas p-uniform may yield implausible estimates of negative effect size. Moreover, we show that (and explain why) p-curve and p-uniform result in overestimation of effect size under moderate-to-large heterogeneity and may yield unpredictable bias when researchers employ p-hacking. We offer hands-on recommendations on applying and interpreting results of meta-analyses in general and p-uniform and p-curve in particular. Both methods as well as traditional methods are applied to a meta-analysis on the effect of weight on judgments of importance. We offer guidance for applying p-uniform or p-curve using R and a user-friendly web application for applying p-uniform.
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With moderate heterogeneity (I2 = 42.01%), the true effect size estimates are expected to be robust.
“With a moderate ( < 50%) level of heterogeneity in included comparisons ( I 2 = 42.01%), we expect these estimates to be robust (van Aert etal., 2016).”
From When Problem Solving Followed by Instruction Works: Evidence for Productive Failure (2021) · cites this paper as van Aert et al., 2016
In the section “If Heterogeneity Is Moderate to Large, p-Curve and p-Uniform Overestimate Effect Size” (page unavailable), the paper reports that effect-size estimates are biased at its moderate-heterogeneity condition (I² ≈ 0.5). It does not assess I² = 42.01%, so that exact threshold is not tested; its finding does not support the cited expectation that moderate heterogeneity makes the estimates robust. (LLM, conf=0.84)
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- Authors
- van Aert, R. C. M., Wicherts, J. M., & van Assen, M. A. L. M.
- Affiliations
- Department of Methodology and Statistics, Tilburg University
- Tilburg University (NL)
- ORCID
- Wicherts, Jelte M.
- Venue
- Perspectives on Psychological Science
- Publisher
- SAGE Publications
- Published
- 2016-09
- Volume
- 11
- Issue
- 5
- Pages
- 713-729
- ISSN
- 1745-6916 (ISSN-L)
- 1745-6924
- PMC
- PMC5117126
- OpenAlex
- W2526603989
- Cited by
- 219
- References
- 54
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 24 · 2023: 22 · 2022: 28 · 2021: 22 · 2020: 25 · 2019: 23 · 2018: 20 · 2017: 27 · 2016: 3
- FWCI
- 22.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Perspectives on Psychological Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- Research portal (Tilburg University) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Keywords
- p-curve, publication bias, meta-analysis, effect size estimation, p-hacking, heterogeneity, effect size overestimation, R package, web application
- MeSH terms
- Data Interpretation, Statistical, Humans, Psychology, Software, Meta-Analysis as Topic, Models, Statistical, Publication Bias, Internet
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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