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Cited by 1,969Meta-analysis and systematic reviewsStatistical Methods in EpidemiologyStatistical Methods and Bayesian Inference

The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.

Sample Size Justification

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Lakens, Daniël·2022·Collabra Psychology
DOI 10.1525/collabra.33267

Published version of a preprint

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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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Metadata

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)
DOI
10.1525/collabra.33267
OpenAlex
W4220832516
Cited by
1,962
References
108
Citations by year
2026: 497 · 2025: 501 · 2024: 402 · 2023: 283 · 2022: 191
FWCI
201.66
Type
journal article
Language
en
Open access
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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