A Practical Methodological Primer on Sample Size Determination Using G*Power for Biomedical Research

An appropriate sample size is essential to determine methodological steps that underpin the scientific validity, ethical integrity, and practical feasibility of biomedical and health research. Inadequate estimation may lead to underpowered studies or unnecessary resource use and participant exposure. This article provides a practical overview of statistical power analysis and sample size determination using the G*Power software (version 3.1.9.7), which is a freely available tool for researchers. The Core statistical concepts, including hypothesis formulation, effect size estimation, significance level (α), statistical power (1-β), and their dynamic interrelationship, are outlined. The article further describes the G*Power software ecosystem, its graphical user interface, supported statistical test families, and the distinct modes of power analysis. Step-by-step methods with realistic research scenarios demonstrate sample size calculation for commonly used statistical tests, including independent and paired t-tests, z-tests for proportions, one-way ANOVA, correlation analysis, and multiple linear regression. The article also highlights common pitfalls, limitations of G*Power, and advanced considerations in sample size estimation. This article aims to provide researchers with a structured framework for accurate sample size estimation, thereby enhancing study quality, reproducibility, and ethical responsibility in biomedical research.