Central Limit Theorem

The central limit theorem states that the sampling distribution of the mean approaches Normality as the sample size increases, regardless of the probability distribution of the population from which the sample is drawn. If the population distribution is fairly Normally-distributed, this approach to Normality will happen at a fairly small sample size (10-20). The more the population departs from a Normal distribution, the larger the sample required.