Statistics review 4: Sample size calculations
By Elise Whitley, J. Ball. Critical Care, 2002
660 citations recorded by the source corpus.
The present review introduces the notion of statistical power and the hazard of under-powered studies. The problem of how to calculate an ideal sample size is also discussed within the context of factors that affect power, and specific methods for the calculation of sample size are presented for two common scenarios, along with extensions to the simplest case.
Subfields: Cardiology and Cardiovascular Medicine, Epidemiology, Surgery
View Paper: https://www.semanticscholar.org/paper/d0fd1f20314ce61cab8a7c863eb81562779af66a
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In Cardiology and Cardiovascular Medicine
In Epidemiology
In Surgery
Citations in the corpus
- Sample size estimation and power analysis for clinical research studies (2012, methodology): “[16] Many apparently null studies may be under-powered rather than genuinely demonstrating no difference between groups, absence of evidence is not evidence of absence.[9]”
- Supporting thinking on sample sizes for thematic analyses: a quantitative tool (2015, background): “Such calculations are context-dependent and part of a subjective process (Schulz & Grimes, 2005; Spiegelhalter & Freedman, 1986; Whitley & Ball, 2002), even if reviewers sometimes expect more precision in them than is feasible (Bacchetti, 2002).”
- RAAIS: Rapid Appraisal of Agricultural Innovation Systems (Part I). A diagnostic tool for integrated analysis of complex problems and innovation capacity (2015, methodology): “A (efficient) sampling method that allows for optimal allocation of resources can be used to determine the sample size (e.g. Whitley and Ball, 2002).”