2002
DOI: 10.1186/cc1521
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Abstract: 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.

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Cited by 505 publications
(214 citation statements)
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References 7 publications
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“…Using our variability measurements (coefficient of variation = 20%) and a 2α = 0.05, an n = 9 gave us a power of 90% to detect a 30% difference in intimal area after carotid injury [21]. The data are expressed as the mean ± SEM.…”
Section: Methodsmentioning
confidence: 99%
“…Using our variability measurements (coefficient of variation = 20%) and a 2α = 0.05, an n = 9 gave us a power of 90% to detect a 30% difference in intimal area after carotid injury [21]. The data are expressed as the mean ± SEM.…”
Section: Methodsmentioning
confidence: 99%
“…Was estimated using Withley et al formula to compare proportions in case control study [14]. The power was set at 80%.…”
Section: Sample Sizementioning
confidence: 99%
“…The sample size was calculated using Whitley's formula: N = 2 Cp, power/d 2 where N = sample size, Cp, power = constant defined by the values of p (threshold of significance) chosen available in the statistical tables [10]. For a value of α = 0.05 and a power of 95%, cp, power = 13.…”
Section: Sample Sizementioning
confidence: 99%