This paper discusses the importance of sample size and statistical power in factor analysis (EFA, CFA) and structural equation modeling (SEM). It emphasizes the need for adequate sample size to ensure accurate and reliable results. Statistical power is the probability of detecting an actual effect, and it is influenced by factors such as sample size, effect size, and significance level. The paper reviews existing guidelines for determining sample size in EFA, CFA, and SEM, highlighting that larger samples generally provide more accurate results. However, the literature provides conflicting advice on the required sample size. For EFA, smaller samples may be sufficient if the data is strong, characterized by high communalities and strong loadings. In CFA and SEM, sample size affects the precision of parameter estimates and model fit indices. The paper also discusses Monte Carlo simulation methods for determining sample size and power, as well as alternative approaches like the Bayesian method. It concludes that while there are no universally accepted sample size rules, power analysis is essential for ensuring adequate statistical power in research. The paper also addresses what to do when sample size is insufficient, suggesting strategies such as using indicators with good psychometric properties and item parceling. Overall, the paper underscores the importance of considering sample size and power in EFA, CFA, and SEM to ensure valid and reliable results.This paper discusses the importance of sample size and statistical power in factor analysis (EFA, CFA) and structural equation modeling (SEM). It emphasizes the need for adequate sample size to ensure accurate and reliable results. Statistical power is the probability of detecting an actual effect, and it is influenced by factors such as sample size, effect size, and significance level. The paper reviews existing guidelines for determining sample size in EFA, CFA, and SEM, highlighting that larger samples generally provide more accurate results. However, the literature provides conflicting advice on the required sample size. For EFA, smaller samples may be sufficient if the data is strong, characterized by high communalities and strong loadings. In CFA and SEM, sample size affects the precision of parameter estimates and model fit indices. The paper also discusses Monte Carlo simulation methods for determining sample size and power, as well as alternative approaches like the Bayesian method. It concludes that while there are no universally accepted sample size rules, power analysis is essential for ensuring adequate statistical power in research. The paper also addresses what to do when sample size is insufficient, suggesting strategies such as using indicators with good psychometric properties and item parceling. Overall, the paper underscores the importance of considering sample size and power in EFA, CFA, and SEM to ensure valid and reliable results.