The power and pitfalls of underpowered studies
Open accessAbstract
This article reflects on the potential value and many pitfalls of underpowered studies to help authors and readers consider whether and how they contribute meaningfully to the published literature. A basic introduction to power and sample size calculations is provided. Several problems that can arise in analysis and publication of underpowered studies are described. In addition, features of underpowered studies that may provide value are proposed, including when the hypothesis test of interest is a limited part of the story, the data is rich enough to showcase interesting features of the population of interest, when the rarity or ubiquity of events is an important finding, and when the study is preregistered to reduce the impact of publication bias. Several reporting guidelines for underpowered studies are also suggested.
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Bias mostly occurs unintentionally; for example, when a scientist underpowers a study or is not aware of sexist assumptions that underpin their interpretive framework.
“Bias mostly occurs unintentionally (i.e., subconsciously); for example, when a scientist underpowers a study or is not aware of sexist assumptions that underpin their interpretive framework [48–52].”
From Autonomous artificial intelligence, scientific research, and human values (2026) · cites this paper as [52]
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- Authors
- Carnahan, R. M., & Brown, G. D.
- Affiliations
- University of Iowa (US)
- Corresponding author
- Carnahan, Ryan M.
- Venue
- Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy
- Publisher
- Wiley
- Published
- 2024-09
- Volume
- 44
- Issue
- 9
- Pages
- 698-700
- ISSN
- 0277-0008 (ISSN-L)
- 1875-9114
- OpenAlex
- W4402207341
- Cited by
- 27
- References
- 11
- Citations by year
- 2026: 16 · 2025: 9 · 2024: 2
- FWCI
- 9.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- underpowered studies, statistical power, sample size determination, publication bias, preregistration, reporting guidelines, rare events, hypothesis testing
- MeSH terms
- Data Interpretation, Statistical, Humans, Research Design, Publication Bias, Sample Size
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