This paper reviews the importance of sample size and statistical power in Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). It emphasizes that adequate statistical power is crucial for observing true relationships in datasets. The paper discusses the four parameters related to power analysis: Alpha, Beta, statistical power, and Effect size, which are essential for determining the appropriate sample size. Scale development, EFA, and SEM are noted as large-sample methods, where sample size significantly affects precision and replicability. However, existing literature provides limited and sometimes conflicting guidance on this issue. The paper highlights that in EFA, stronger data can require a smaller sample for accurate analysis, while in CFA and SEM, parameter estimates, chi-square tests, and goodness-of-fit indices are sensitive to sample size, affecting statistical power and precision. The paper reviews various methods for power analysis, including traditional rules of thumb, Monte Carlo simulation, and model-based approaches, and provides suggestions for small samples in factor analysis. It concludes with recommendations for researchers to ensure adequate but not excessive sample sizes in their studies.This paper reviews the importance of sample size and statistical power in Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). It emphasizes that adequate statistical power is crucial for observing true relationships in datasets. The paper discusses the four parameters related to power analysis: Alpha, Beta, statistical power, and Effect size, which are essential for determining the appropriate sample size. Scale development, EFA, and SEM are noted as large-sample methods, where sample size significantly affects precision and replicability. However, existing literature provides limited and sometimes conflicting guidance on this issue. The paper highlights that in EFA, stronger data can require a smaller sample for accurate analysis, while in CFA and SEM, parameter estimates, chi-square tests, and goodness-of-fit indices are sensitive to sample size, affecting statistical power and precision. The paper reviews various methods for power analysis, including traditional rules of thumb, Monte Carlo simulation, and model-based approaches, and provides suggestions for small samples in factor analysis. It concludes with recommendations for researchers to ensure adequate but not excessive sample sizes in their studies.