title: Regression analysis in biological research: sample size and statistical power.
abstract: Regression analysis is often used to demonstrate associations among variables believed to be biologically related. Failure to demonstrate a "significant" relationship may be due to two factors: 1) the variables are truly unrelated, or 2) a relationship exists but goes undetected due to inadequate statistical power. Investigators must consider the second possibility since failure to detect a statistically significant relationship is often taken as evidence for no biological relationship. These issues are addressed in the context of the interrelationship between four features common to all statistical methods: the size of effect or relationship worth detecting, the Type I (alpha) error, the sample size, and the Type II (beta) error. An example derived from published data relating morphological characteristics of muscle fiber type and isokinetic strength performance illustrates the practical significance of this dilemma.
authors.affiliation: Northern California Occupational Health Center, San Francisco General Medical Center, University of California 94110.
authors.forename: D S
authors.initials: DS
authors.lastname: Sharp
authors.forename: P M
authors.initials: PM
authors.lastname: Gahlinger
mesh_terms: D012044:Regression Analysis; D012106:Research