The central limit theorem: The means of large, random samples are approximately normal

The central limit theorem is a fundamental theorem of probability and statistics. The theorem states that the distribution of , which is the mean of a random sample from a population with finite variance, is approximately normally distributed when the sample size is large, regardless of the shape of the population's distribution. Many common statistical procedures require data to be approximately normal, but the central limit theorem lets you to apply these useful procedures to populations that are strongly nonnormal. How large the sample size must be depends on the shape of the original distribution. If the population's distribution is symmetric, a sample size of 5 could yield a good approximation; if the population's distribution is strongly asymmetric, a larger sample size 50 or more is necessary. The following graphs show examples of how the distribution affects the sample size that you need.

Uniform distribution
Sample means

A population that follows a uniform distribution is symmetric but strongly nonnormal, as the first histogram demonstrates. However, the distribution of 1000 sample means (n=5) from this population is approximately normal because of the central limit theorem, as the second histogram demonstrates. This histogram of sample means includes a superimposed normal curve to illustrate its normality.

Exponential distribution
Sample means

A population that follows an exponential distribution is asymmetric and nonnormal, as the first histogram demonstrates. However, the distribution of sample means from 1000 samples of size 50 from this population is approximately normal because of the central limit theorem, as the second histogram demonstrates. This histogram of sample means includes a superimposed normal curve to illustrate its normality.

By using this site you agree to the use of cookies for analytics and personalized content.  Read our policy