The Importance of A Priori Sample Size Estimation in Strength and Conditioning Research

TLDR

Statistical power is the probability of correctly rejecting a false null hypothesis, and it depends on significance level, effect size, and sample size—only the latter can be controlled by researchers, making its appropriate selection a critical yet often misunderstood design element in strength and conditioning studies. This tutorial aims to guide researchers in estimating sample size for common experimental designs in strength and conditioning, emphasizing effect size selection as the key determinant when power and significance level are fixed. The authors illustrate sample size estimation using the freely available G*Power 3.1.4 software, detailing procedures for various statistical tests relevant to strength and conditioning research.

Abstract

The statistical power, or sensitivity of an experiment, is defined as the probability of rejecting a false null hypothesis. Only 3 factors can affect statistical power: (a) the significance level (α), (b) the magnitude or size of the treatment effect (effect size), and (c) the sample size (n). Of these 3 factors, only the sample size can be manipulated by the investigator because the significance level is usually selected before the study, and the effect size is determined by the effectiveness of the treatment. Thus, selection of an appropriate sample size is one of the most important components of research design but is often misunderstood by beginning researchers. The purpose of this tutorial is to describe procedures for estimating sample size for a variety of different experimental designs that are common in strength and conditioning research. Emphasis is placed on selecting an appropriate effect size because this step fully determines sample size when power and the significance level are fixed. There are many different software packages that can be used for sample size estimation. However, I chose to describe the procedures for the G*Power software package (version 3.1.4) because this software is freely downloadable and capable of estimating sample size for many of the different statistical tests used in strength and conditioning research. Furthermore, G*Power provides a number of different auxiliary features that can be useful for researchers when designing studies. It is my hope that the procedures described in this article will be beneficial for researchers in the field of strength and conditioning.