Central-limit theorem

A theorem that holds that if simple random samples of size n are drawn from a parent population with mean μ and variance σ2, then when n is large, the sample mean x will be approximately normally distributed with mean equal to μ and variance equal to σ2/n. The approximation will become more and more accurate as n becomes larger. It means that regardless of the shape of the parent population, distribution of the sample means will be normal if the sample is large enough.