Central limit theorem

In probability theory, the central limit theorem (CLT) states that, under appropriate conditions, the distribution of a normalized version of the sample mean converges to a standard normal distribution. This holds even if the original variables themselves are not normally distributed. There are several versions of the CLT, each applying in the context of different conditions.

The theorem is a key concept in probability theory because it implies that probabilistic and statistical methods that work for normal distributions can be applicable to many problems involving other types of distributions.

This theorem has seen many changes during the formal development of probability theory. Previous versions of the theorem date back to 1811, but in its modern form it was only precisely stated in the 1920s.

In statistics, the CLT can be stated as: let

X

1

,

X

2

,

…

,

X

n

{\displaystyle X_{1},X_{2},\dots ,X_{n}}

denote a statistical sample of size

n

{\displaystyle n}

from a population with expected value (average)

μ

{\displaystyle \mu }

and finite positive variance

σ

2

{\displaystyle \sigma ^{2}}

, and let

X

¯

n

{\displaystyle {\bar {X}}_{n}}

denote the sample mean (which is itself a random variable). Then the limit as

n

→

∞

{\displaystyle n\to \infty }

of the distribution of

(

X

¯

n

−

μ

)

n

{\displaystyle ({\bar {X}}_{n}-\mu ){\sqrt {n}}}

is a normal distribution with mean

0

{\displaystyle 0}

and variance

σ

2

{\displaystyle \sigma ^{2}}

.

In other words, suppose that a large sample of observations is obtained, each observation being randomly produced in a way that does not depend on the values of the other observations, and the average (arithmetic mean) of the observed values is computed. If this procedure is performed many times, resulting in a collection of observed averages, the central limit theorem says that if the sample size is large enough, the probability distribution of these averages will closely approximate a normal distribution.