The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty
Abstract
The sample size necessary to obtain a desired level of statistical power depends in part on the population value of the effect size, which is, by definition, unknown. A common approach to sample-size planning uses the sample effect size from a prior study as an estimate of the population value of the effect to be detected in the future study. Although this strategy is intuitively appealing, effect-size estimates, taken at face value, are typically not accurate estimates of the population effect size because of publication bias and uncertainty. We show that the use of this approach often results in underpowered studies, sometimes to an alarming degree. We present an alternative approach that adjusts sample effect sizes for bias and uncertainty, and we demonstrate its effectiveness for several experimental designs. Furthermore, we discuss an open-source R package, BUCSS, and user-friendly Web applications that we have made available to researchers so that they can easily implement our suggested methods.
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“Furthermore, sample sizes are often underpowered (4-6), at times leading to conclusions that do not hold up with larger-sized replications or metaanalyses (7).”
From Generic language in scientific communication (2019) · cites this paper as [4]
Citations
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Estimating the reproducibility of psychological science
Open Science Collaboration (2015) · Science · doi:10.1126/science.aac4716 · Cited by 8,515
Power failure: why small sample size undermines the reliability of neuroscience
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3475 · Cited by 7,641 · cited 1×
“However, even when researchers conduct a well-intentioned power analysis to determine the sample size of a replication study, the power of the original study limits their ability to determine an accurate sample size, sometimes severely (Anderson & Maxwell, 2016, 2017; Button et al., 2013).”
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Lakens, D. (2022) · Collabra Psychology · doi:10.1525/collabra.33267 · Cited by 1,962
Brysbaert, M. (2019) · Journal of Cognition · doi:10.5334/joc.72 · Cited by 996
Replicability, Robustness, and Reproducibility in Psychological Science
Nosek, B. A., Hardwicke, T. E., Moshontz, H., Allard, A., Corker, K. S., Dreber, A., Fidler, F., Hilgard, J., Kline Struhl, M., Nuijten, M. B., et al. (2022) · Annual Review of Psychology · doi:10.1146/annurev-psych-020821-114157 · Cited by 770
A Practical Primer To Power Analysis for Simple Experimental Designs
Perugini, M., Gallucci, M., & Costantini, G. (2018) · International Review of Social Psychology · doi:10.5334/irsp.181 · Cited by 482
Publication bias examined in meta-analyses from psychology and medicine: A meta-meta-analysis
van Aert, R. C. M., Wicherts, J. M., & van Assen, M. A. L. M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215052 · Cited by 365
Metadata
- Authors
- Anderson, S. F., Kelley, K., & Maxwell, S. E.
- Affiliations
- University of Notre Dame (US)
- Corresponding author
- Anderson, Samantha F.
- Venue
- Psychological Science
- Publisher
- SAGE Publications
- Published
- 2017-09-13
- Volume
- 28
- Issue
- 11
- Pages
- 1547-1562
- ISSN
- 0956-7976 (ISSN-L)
- 1467-9280
- OpenAlex
- W2756450828
- Cited by
- 406
- References
- 63
- Citations by year
- 2026: 31 · 2025: 39 · 2024: 46 · 2023: 55 · 2022: 52 · 2021: 53 · 2020: 44 · 2019: 47 · 2018: 34 · 2017: 3
- FWCI
- 31.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Science (publishedVersion)
- PubMed (publishedVersion)
- 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
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- sample size determination, statistical power, effect size estimation, publication bias, underpowered studies
- MeSH terms
- Data Interpretation, Statistical, Humans, Publication Bias, Sample Size, Uncertainty
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