Sample size determination and power analysis using the G*Power software.

Hyun Kang

doi:10.3352/jeehp.2021.18.17

Abstract

Appropriate sample size calculation and power analysis have become major issues in research and publication processes. However, the complexity and difficulty of calculating sample size and power require broad statistical knowledge, there is a shortage of personnel with programming skills, and commercial programs are often too expensive to use in practice. The review article aimed to explain the basic concepts of sample size calculation and power analysis; the process of sample estimation; and how to calculate sample size using G*Power software (latest ver. 3.1.9.7; Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany) with 5 statistical examples. The null and alternative hypothesis, effect size, power, alpha, type I error, and type II error should be described when calculating the sample size or power. G*Power is recommended for sample size and power calculations for various statistical methods (F, t, χ2, Z, and exact tests), because it is easy to use and free. The process of sample estimation consists of establishing research goals and hypotheses, choosing appropriate statistical tests, choosing one of 5 possible power analysis methods, inputting the required variables for analysis, and selecting the “calculate” button. The G*Power software supports sample size and power calculation for various statistical methods (F, t, χ2, z, and exact tests). This software is helpful for researchers to estimate the sample size and to conduct power analysis.

Highlights

Background/rationale If research can be conducted among the entire population of interest, the researchers would obtain more accurate findings
Research should provide an accurate estimate of the therapeutic effect, which may lead to evidence-based decisions or judgments
The International Committee of Medical Journal Editors recommends that authors describe statistical methods with sufficient detail to enable a knowledgeable reader with access to the original data to verify the reported results [12], and the same principle should be followed for the description of sample size calculation or power analysis

Summary

Introduction

Background/rationale If research can be conducted among the entire population of interest, the researchers would obtain more accurate findings. In most cases, conducting a study of the entire population is impractical, if not impossible, and would be inefficient. Research should provide an accurate estimate of the therapeutic effect, which may lead to evidence-based decisions or judgments. Studies with inappropriate sample sizes or powers do not provide accurate estimates and report inappropriate information on the treatment effect, making evidence-based decisions or judgments difficult. If the sample size is too large, too many variables—beyond those that researchers want to evaluate in the study—may become statistically significant. Some variables may show a statistically significant difference, even if the difference is not meaningful.

Objectives

Methods

Conclusion