Central Limit Theorem: the tendency for the sample mean of a set of random variables to have a normal sampling distribution, regardless (with certain exceptions) of the shape of the underlying sampling distribution of the random variable. If n independent observations each have mean μ and variance σ2, then under broad assumptions their sample mean is an estimator of μ, and has an approximately normal distribution with mean μ, variance σ2/n, and standard deviation (also known as the standard error of the estimator).
