This chapter comprehensively addresses the fundamental concepts of power analysis, its application types, and its implications for clinical research in health sciences. The validity and ethical integrity of research findings depend not only on the statistical methods used but also on the adequacy of the sample size. Power analysis is a crucial planning tool for estimating the minimum number of participants required to achieve statistical significance. Key elements of power analysis include effect size, significance level, statistical power, and the type of statistical test. The chapter explains various types of power analysis—namely a priori, post hoc (posterior), and sensitivity analyses—and illustrates their applications using independent and paired sample comparisons as well as ANOVA, particularly through the G*Power software. Additionally, a comparison of commonly used tools such as PASS, R, and G*Power is provided, highlighting their strengths and application domains. The clinical relevance of power analysis is emphasized, noting that it not only ensures statistical adequacy but also directly impacts patient safety and resource management. Common pitfalls such as arbitrarily selecting effect sizes or mismatching test types in power calculations are also discussed. Finally, the chapter underlines that power analysis should be seen not merely as a technical computation, but as an integral part of ethical, scientific, and applied research planning. Promoting awareness and adopting a shared understanding of power analysis across interdisciplinary research teams is essential for enhancing the quality and impact of health science studies.