2018
DOI: 10.1016/j.jesp.2017.09.004
|View full text |Cite
|
Sign up to set email alerts
|

When power analyses based on pilot data are biased: Inaccurate effect size estimators and follow-up bias

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
192
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
6
2
1

Relationship

0
9

Authors

Journals

citations
Cited by 273 publications
(216 citation statements)
references
References 28 publications
0
192
0
Order By: Relevance
“…As a corollary, the effect‐size estimates obtained in our exploratory study might not provide a reliable basis for calculating the sample size of our confirmatory study (cf. Albers & Lakens, ). This implies that our confirmatory study might not have been adequately powered to detect, for example, the correlation between cWCST and PEBT performance.…”
Section: Discussionmentioning
confidence: 99%
“…As a corollary, the effect‐size estimates obtained in our exploratory study might not provide a reliable basis for calculating the sample size of our confirmatory study (cf. Albers & Lakens, ). This implies that our confirmatory study might not have been adequately powered to detect, for example, the correlation between cWCST and PEBT performance.…”
Section: Discussionmentioning
confidence: 99%
“…During the entire analysis, ω 2 was preferred as the effect size for the factorial and repeated-measures ANOVAs over partial eta square, which has been reported to be an upwardly biased estimate of the true population effect size. 40,41 The degrees of freedom were corrected using either the Greenhouse-Geisser or Huynh-Feldt estimates based on Mauchly's test of sphericity and the Greenhouse-Geisser epsilon (ε). 41 3 | RESULTS…”
Section: Discussionmentioning
confidence: 99%
“…207 The targeted effect size should be biologically plausible, and based on the degree of benefit that patients, consumers, or practitioners would consider sufficiently beneficial to be worthwhile, given other considerations such as cost, inconvenience, side effects, and potential for serious adverse effects 213 (Table 5). Effect sizes should not be based on estimates from the literature as a whole, which almost always leads to biased estimates, 214 inasmuch as the published literature contains a biased selection of all empirical research that is performed. Meta-analytic evaluations of the literature have consistently found that studies yielding null or mixed results are substantially less likely to appear in the published scientific record 215,216 than those studies with strong statistical support for the tested hypothesis.…”
Section: Sample and Effect Sizementioning
confidence: 99%
“…Use best current meta-analytic approaches to assess bias in the literature, and to correct for bias in reported effect sizes 214. Base sample sizes on the smallest effect size of interest, in the context of existing options and consumer burden.…”
mentioning
confidence: 99%