The Central Limit Theorem (CLT) is one of the most important concepts in statistics and quantitative analysis because it provides a foundation for making inferences about a population from sample data. It explains how the distribution of sample means behaves when repeated random samples are drawn from a population. The theorem is particularly useful in managerial applications because managers often make decisions based on sample information rather than complete population data. The Central Limit Theorem states that, if sufficiently large random samples are drawn from a population with a finite mean and finite variance, the distribution of the sample means will tend to be approximately normal, regardless of the original distribution of the population. In other words, even if the population is skewed or does not follow a normal distribution, the sampling distribution of the sample mean becomes approximately normal as the sample size increases. Suppose a population has a mean μ and standard deviation σ. If a random sample of size