This article by Hyun Kang from the Department of Anesthesiology and Pain Medicine at Chung-Ang University College of Medicine in Seoul, Korea, discusses the importance of appropriate sample size calculation and power analysis in research. The complexity and difficulty of these calculations, along with the lack of statistical expertise and the high cost of commercial software, make it challenging for researchers to perform these analyses effectively. To address these issues, the article introduces G*Power software (version 3.1.9.7), which is user-friendly and free. G*Power supports various statistical methods (F, t, χ², Z, and exact tests) and is recommended for sample size and power calculations. The article outlines the basic concepts of sample size calculation and power analysis, including the null and alternative hypotheses, effect size, power, alpha, type I error, and type II error. It provides a step-by-step guide on how to use G*Power, covering the process of establishing research goals, choosing appropriate statistical tests, selecting a power analysis method, inputting required variables, and calculating the sample size or power. Five statistical examples are provided to illustrate the application of G*Power: 1. **Two-sample t-test**: Compares the means of two independent samples. 2. **Dependent t-test**: Compares the means of two dependent samples. 3. **One-way ANOVA**: Compares the means of three or more samples. 4. **Correlation (Pearson r)**: Measures the relationship between two continuous variables. 5. **Two independent proportions (chi-square test)**: Compares the proportions of two independent samples. Each example includes both a priori and post-hoc calculations, demonstrating how to determine the required sample size or achieve the desired power. The article also addresses the consideration of drop-out rates when calculating sample sizes. In conclusion, the article emphasizes the importance of appropriate sample size calculation and power analysis in research, highlighting the benefits of using G*Power for these purposes.This article by Hyun Kang from the Department of Anesthesiology and Pain Medicine at Chung-Ang University College of Medicine in Seoul, Korea, discusses the importance of appropriate sample size calculation and power analysis in research. The complexity and difficulty of these calculations, along with the lack of statistical expertise and the high cost of commercial software, make it challenging for researchers to perform these analyses effectively. To address these issues, the article introduces G*Power software (version 3.1.9.7), which is user-friendly and free. G*Power supports various statistical methods (F, t, χ², Z, and exact tests) and is recommended for sample size and power calculations. The article outlines the basic concepts of sample size calculation and power analysis, including the null and alternative hypotheses, effect size, power, alpha, type I error, and type II error. It provides a step-by-step guide on how to use G*Power, covering the process of establishing research goals, choosing appropriate statistical tests, selecting a power analysis method, inputting required variables, and calculating the sample size or power. Five statistical examples are provided to illustrate the application of G*Power: 1. **Two-sample t-test**: Compares the means of two independent samples. 2. **Dependent t-test**: Compares the means of two dependent samples. 3. **One-way ANOVA**: Compares the means of three or more samples. 4. **Correlation (Pearson r)**: Measures the relationship between two continuous variables. 5. **Two independent proportions (chi-square test)**: Compares the proportions of two independent samples. Each example includes both a priori and post-hoc calculations, demonstrating how to determine the required sample size or achieve the desired power. The article also addresses the consideration of drop-out rates when calculating sample sizes. In conclusion, the article emphasizes the importance of appropriate sample size calculation and power analysis in research, highlighting the benefits of using G*Power for these purposes.