CoChat
Sign Up
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

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

Published version of a preprint

Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1525/collabra.33267

Cites

108 of 108 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Cited by

Sorted by citations

See all 1,969

Related

See all

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

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.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review