This paper discusses the critical importance of determining the appropriate sample size and power analysis in clinical research studies. It emphasizes that an optimal sample size ensures adequate statistical power to detect significant differences, while an underpowered study may fail to provide statistically conclusive results. The paper covers various study designs, including single-group mean studies, survey-type studies, two-group studies based on means and proportions, correlation studies, and case-control studies for assessing categorical outcomes. Key factors affecting sample size, such as study design, outcome variable, alpha level, variance, minimum detectable difference, and power, are discussed. The paper provides detailed formulas and examples for calculating sample sizes in different scenarios, such as survey research, single-group mean studies, two-group studies, and studies with proportions or odds ratios. It also highlights the importance of considering practical constraints, such as budget and time limitations, and the need for retrospective power analysis when results are not statistically significant. The conclusion underscores that appropriately sized samples are essential for reliable and valid research findings, and that inadequate sample sizes can lead to false conclusions and waste resources.This paper discusses the critical importance of determining the appropriate sample size and power analysis in clinical research studies. It emphasizes that an optimal sample size ensures adequate statistical power to detect significant differences, while an underpowered study may fail to provide statistically conclusive results. The paper covers various study designs, including single-group mean studies, survey-type studies, two-group studies based on means and proportions, correlation studies, and case-control studies for assessing categorical outcomes. Key factors affecting sample size, such as study design, outcome variable, alpha level, variance, minimum detectable difference, and power, are discussed. The paper provides detailed formulas and examples for calculating sample sizes in different scenarios, such as survey research, single-group mean studies, two-group studies, and studies with proportions or odds ratios. It also highlights the importance of considering practical constraints, such as budget and time limitations, and the need for retrospective power analysis when results are not statistically significant. The conclusion underscores that appropriately sized samples are essential for reliable and valid research findings, and that inadequate sample sizes can lead to false conclusions and waste resources.