This review article discusses the importance of appropriate sample size calculation and power analysis in research and highlights the use of G*Power software for these purposes. It explains the basic concepts of sample size calculation, including null and alternative hypotheses, effect size, power, alpha, type I and II errors, and the process of sample estimation. The article provides a step-by-step guide on how to use G*Power software for sample size and power calculations for various statistical methods, including t-tests, ANOVA, correlation, and chi-square tests. It also includes five statistical examples demonstrating the application of G*Power for sample size and power analysis. The article emphasizes the importance of considering the drop-out rate when calculating sample size and highlights the benefits of using G*Power, which is free, user-friendly, and supports a wide range of statistical tests. The review concludes that appropriate sample size calculation and power analysis are critical for ensuring the validity and reliability of research findings.This review article discusses the importance of appropriate sample size calculation and power analysis in research and highlights the use of G*Power software for these purposes. It explains the basic concepts of sample size calculation, including null and alternative hypotheses, effect size, power, alpha, type I and II errors, and the process of sample estimation. The article provides a step-by-step guide on how to use G*Power software for sample size and power calculations for various statistical methods, including t-tests, ANOVA, correlation, and chi-square tests. It also includes five statistical examples demonstrating the application of G*Power for sample size and power analysis. The article emphasizes the importance of considering the drop-out rate when calculating sample size and highlights the benefits of using G*Power, which is free, user-friendly, and supports a wide range of statistical tests. The review concludes that appropriate sample size calculation and power analysis are critical for ensuring the validity and reliability of research findings.