The central limit theorem says that the sampling distribution of the mean will always be normally distributed, as long as the sample size is large enough. The distribution of the sample means is an example of a sampling distribution. You repeat this process many times, and end up with a large number of means, one for each sample.Now you draw another random sample of the same size, and again calculate the mean.Suppose that you draw a random sample from a population and calculate a statistic for the sample, such as the mean.Imagining an experiment may help you to understand sampling distributions: The central limit theorem relies on the concept of a sampling distribution, which is the probability distribution of a statistic for a large number of samples taken from a population. Frequently asked questions about the central limit theorem.Importance of the central limit theorem.Conditions of the central limit theorem.
