Sample Size Justification¶
Lakens, D. (2022). Sample Size Justification. Collabra: Psychology, 8(1).
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Statistical Power
- A power analysis exhibits: (a) a pre-specified test — the hypothesis test whose power is to be characterized — with its test statistic, null distribution, and significance level α; (b) an alternative hypothesis expressed as an effect-size value δ under which power is to be computed; © a probability model specifying the sampling distribution of the test statistic under H₁ (typically a non-central version of the null distribution, parameterized by the non-centrality parameter which depends on δ, n, and σ); (d) specification of n, α, δ, and σ — the four quantities that jointly determine power, with three fixed and one solved for; (e) a computational method — closed-form formula (t-test, F-test, chi-squared), tabulated value (Cohen's handbook), or simulation-based (complex designs); (f) design-specific adjustments — design-effect for cluster designs, allocation-ratio for unequal-n, multiplicity-adjustment for multiple primary outcomes; (g) sensitivity characterization — how power varies with effect-size assumptions (because the "true" δ is unknown, power at several plausible δ values is informative)
This sourcePsyArXiv Preprints. Lakens comprehensive guide to justified sample-size specification beyond traditional power analysis.
Supported in partVerified against the publisher's abstract
Lakens (2022) backs the sensitivity element — power computed across a range of effect-size assumptions — but not the seven-part taxonomy the claim enumerates.
“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 …”
- A power analysis exhibits: (a) a pre-specified test — the hypothesis test whose power is to be characterized — with its test statistic, null distribution, and significance level α; (b) an alternative hypothesis expressed as an effect-size value δ under which power is to be computed; © a probability model specifying the sampling distribution of the test statistic under H₁ (typically a non-central version of the null distribution, parameterized by the non-centrality parameter which depends on δ, n, and σ); (d) specification of n, α, δ, and σ — the four quantities that jointly determine power, with three fixed and one solved for; (e) a computational method — closed-form formula (t-test, F-test, chi-squared), tabulated value (Cohen's handbook), or simulation-based (complex designs); (f) design-specific adjustments — design-effect for cluster designs, allocation-ratio for unequal-n, multiplicity-adjustment for multiple primary outcomes; (g) sensitivity characterization — how power varies with effect-size assumptions (because the "true" δ is unknown, power at several plausible δ values is informative)
Mechanisms¶
- Minimum Detectable Effect Table
- It misleads when the meaningful threshold is set loosely: an MDE looks acceptable only relative to the smallest effect size of interest, and if no one has committed to that number, a large MDE can be waved through as "fine."
This sourceExplains that a minimum detectable effect is informative only relative to a prejustified smallest effect size of interest.
- It misleads when the meaningful threshold is set loosely: an MDE looks acceptable only relative to the smallest effect size of interest, and if no one has committed to that number, a large MDE can be waved through as "fine."
Verification¶
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