Sample Size Justification
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Abstract
An important step when designing an empirical study is to justify the sample size that will be collected. The key aim of a sample size justification for such studies is to explain how the collected data is expected to provide valuable information given the inferential goals of the researcher. In this overview article six approaches are discussed to justify the sample size in a quantitative empirical study: 1) collecting data from (almost) the entire population, 2) choosing a sample size based on resource constraints, 3) performing an a-priori power analysis, 4) planning for a desired accuracy, 5) using heuristics, or 6) explicitly acknowledging the absence of a justification. An important question to consider when justifying sample sizes is which effect sizes are deemed interesting, and the extent to which the data that is collected informs inferences about these effect sizes. Depending on the sample size justification chosen, researchers could consider 1) what the smallest effect size of interest is, 2) which minimal effect size will be statistically significant, 3) which effect sizes they expect (and what they base these expectations on), 4) which effect sizes would be rejected based on a confidence interval around the effect size, 5) which ranges of effects a study has sufficient power to detect based on a sensitivity power analysis, and 6) which effect sizes are expected in a specific research area. Researchers can use the guidelines presented in this article, for example by using the interactive form in the accompanying online Shiny app, to improve their sample size justification, and hopefully, align the informational value of a study with their inferential goals.
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Final sample size was determined by the time allocated to the first author to collect the data.
“Final sample size was determined by the time allocated to the first author to collect the data (Lakens, 2022).”
From The degree to which the cultural ideal is internalized predicts judgments of male and female physical attractiveness (2022) · cites this paper as Lakens, 2022
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- Authors
- Lakens, D.
- Affiliations
- Eindhoven University of Technology (NL)
- Corresponding author
- Lakens, Daniël
- ORCID
- Lakens, Daniël
- Venue
- Collabra Psychology
- Publisher
- University of California Press
- Published
- 2022
- Volume
- 8
- Issue
- 1
- ISSN
- 2474-7394 (ISSN-L)
- OpenAlex
- W4220832516
- Cited by
- 1,962
- References
- 108
- Citations by year
- 2026: 497 · 2025: 501 · 2024: 402 · 2023: 283 · 2022: 191 · 2021: 77 · 2020: 6 · 2019: 1 · 2017: 1 · 2016: 1 · 2014: 1
- FWCI
- 201.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Collabra Psychology (publishedVersion, cc-by)
- TU/e Research Portal (publishedVersion, other-oa)
- PsyArXiv (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- TU/e Research Portal (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VIDI Grant 452-17-013, 452-17-013, Grant 452-17-013)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- sample size justification, a priori power analysis, confidence intervals, resource constraints, research design, statistical power
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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.
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