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In probability theory, the **central limit theorem** (**CLT**) states that, given certain conditions, the arithmetic mean of a sufficiently large number of iterates of independent random variables, each with a well-defined expected value and well-defined variance, will be approximately normally distributed.^{[1]} That is, suppose that a sample is obtained containing a large number of observations, each observation being randomly generated in a way that does not depend on the values of the other observations, and that the arithmetic average of the observed values is computed. If this procedure is performed many times, the central limit theorem says that the computed values of the average will be distributed according to the normal distribution (commonly known as a "bell curve").

The central limit theorem has a number of variants. In its common form, the random variables must be identically distributed. In variants, convergence of the mean to the normal distribution also occurs for non-identical distributions, given that they comply with certain conditions.

In more general probability theory, a **central limit theorem** is any of a set of weak-convergence theorems. They all express the fact that a sum of many independent and identically distributed (i.i.d.) random variables, or alternatively, random variables with specific types of dependence, will tend to be distributed according to one of a small set of *attractor distributions*. When the variance of the i.i.d. variables is finite, the attractor distribution is the normal distribution. In contrast, the sum of a number of i.i.d. random variables with power law tail distributions decreasing as |*x*|^{−α−1} where 0 < α < 2 (and therefore having infinite variance) will tend to an alpha-stable distribution with stability parameter (or index of stability) of α as the number of variables grows.^{[2]}

Let {*X*_{1}, ..., *X _{n}*} be a random sample of size

of these random variables. By the law of large numbers, the sample averages converge in probability and almost surely to the expected value µ as *n* → ∞. The classical central limit theorem describes the size and the distributional form of the stochastic fluctuations around the deterministic number µ during this convergence. More precisely, it states that as *n* gets larger, the distribution of the difference between the sample average *S _{n}* and its limit µ, when multiplied by the factor √

Lindeberg–Lévy CLT.Suppose {X_{1},X_{2}, ...} is a sequence of i.i.d. random variables with E[X] = µ and Var[_{i}X] = σ_{i}^{2}< ∞. Then asnapproaches infinity, the random variables √n(S− µ) converge in distribution to a normal_{n}N(0, σ^{2}):^{[3]}

In the case σ > 0, convergence in distribution means that the cumulative distribution functions of √*n*(*S _{n}* − µ) converge pointwise to the cdf of the N(0, σ

where Φ(*x*) is the standard normal cdf evaluated at *x*. Note that the convergence is uniform in *z* in the sense that

where sup denotes the least upper bound (or supremum) of the set.^{[4]}

The theorem is named after Russian mathematician Aleksandr Lyapunov. In this variant of the central limit theorem the random variables *X _{i}* have to be independent, but not necessarily identically distributed. The theorem also requires that random variables |

Lyapunov CLT.^{[5]}Suppose {X_{1},X_{2}, ...} is a sequence of independent random variables, each with finite expected value μ_{i}and variance σ 2i. DefineIf for some δ > 0, the

Lyapunov’s conditionis satisfied, then a sum of (

X− μ_{i}_{i})/sconverges in distribution to a standard normal random variable, as_{n}ngoes to infinity:

In practice it is usually easiest to check the Lyapunov’s condition for δ = 1. If a sequence of random variables satisfies Lyapunov’s condition, then it also satisfies Lindeberg’s condition. The converse implication, however, does not hold.

Main article: Lindeberg's condition

In the same setting and with the same notation as above, the Lyapunov condition can be replaced with the following weaker one (from Lindeberg in 1920).

Suppose that for every ε > 0

where **1**_{{…}} is the indicator function. Then the distribution of the standardized sums converges towards the standard normal distribution N(0,1).

Proofs that use characteristic functions can be extended to cases where each individual *X*_{1}, ..., *X _{n}* is an independent and identically distributed random vector in

Let

be the *i*-vector. The bold in **X**_{i} means that it is a random vector, not a random (univariate) variable. Then the sum of the random vectors will be

and the average will be

and therefore

- .

The multivariate central limit theorem states that

where the covariance matrix Σ is equal to

A useful generalization of a sequence of independent, identically distributed random variables is a mixing random process in discrete time; "mixing" means, roughly, that random variables temporally far apart from one another are nearly independent. Several kinds of mixing are used in ergodic theory and probability theory. See especially strong mixing (also called α-mixing) defined by α(*n*) → 0 where α(*n*) is so-called strong mixing coefficient.

A simplified formulation of the central limit theorem under strong mixing is:^{[7]}

**Theorem.** Suppose that *X*_{1}, *X*_{2}, … is stationary and α-mixing with α_{n} = *O*(*n*^{−5}) and that E(*X _{n}*) = 0 and E(

exists, and if σ ≠ 0 then converges in distribution to *N*(0, 1).

In fact,

where the series converges absolutely.

The assumption σ ≠ 0 cannot be omitted, since the asymptotic normality fails for *X _{n}* =

There is a stronger version of the theorem:^{[8]} the assumption E(*X _{n}*

Main article: Martingale central limit theorem

Theorem. Let a martingaleMsatisfy_{n}then converges in distribution to N(0,1) as

- in probability as
ntends to infinity,- for every ε > 0, as
ntends to infinity,n→ ∞.^{[9]}^{[10]}

*Caution:* The restricted expectation^{[clarification needed]} E(*X*; *A*) should not be confused with the conditional expectation E(*X*|*A*) = E(*X*; *A*)/**P**(*A*).

For a theorem of such fundamental importance to statistics and applied probability, the central limit theorem has a remarkably simple proof using characteristic functions. It is similar to the proof of a (weak) law of large numbers. For any random variable, *Y*, with zero mean and a unit variance (var(*Y*) = 1), the characteristic function of *Y* is, by Taylor's theorem,

where *o* (*t*^{2}) is "little o notation" for some function of *t* that goes to zero more rapidly than *t*^{2}.

Letting *Y _{i}* be (

By simple properties of characteristic functions, the characteristic function of the sum is:

so that, by the limit of the exponential function ( `e`^{x}= lim(1+x/n)^{n}) the characteristic function of *Z*_{n} is

But this limit is just the characteristic function of a standard normal distribution N(0, 1), and the central limit theorem follows from the Lévy continuity theorem, which confirms that the convergence of characteristic functions implies convergence in distribution.

The central limit theorem gives only an asymptotic distribution. As an approximation for a finite number of observations, it provides a reasonable approximation only when close to the peak of the normal distribution; it requires a very large number of observations to stretch into the tails.

If the third central moment E((*X*_{1} − μ)^{3}) exists and is finite, then the above convergence is uniform and the speed of convergence is at least on the order of 1/*n*^{1/2} (see Berry-Esseen theorem). Stein's method^{[11]} can be used not only to prove the central limit theorem, but also to provide bounds on the rates of convergence for selected metrics.^{[12]}

The convergence to the normal distribution is monotonic, in the sense that the entropy of *Z*_{n} increases monotonically to that of the normal distribution.^{[13]}

The central limit theorem applies in particular to sums of independent and identically distributed discrete random variables. A sum of discrete random variables is still a discrete random variable, so that we are confronted with a sequence of discrete random variables whose cumulative probability distribution function converges towards a cumulative probability distribution function corresponding to a continuous variable (namely that of the normal distribution). This means that if we build a histogram of the realisations of the sum of *n* independent identical discrete variables, the curve that joins the centers of the upper faces of the rectangles forming the histogram converges toward a Gaussian curve as *n* approaches infinity, this relation is known as de Moivre–Laplace theorem. The binomial distribution article details such an application of the central limit theorem in the simple case of a discrete variable taking only two possible values.

This section includes a list of references, but its sources remain unclear because it has insufficient inline citations. (April 2012) |

The law of large numbers as well as the central limit theorem are partial solutions to a general problem: "What is the limiting behaviour of *S*_{n} as *n* approaches infinity?"^{[citation needed]} In mathematical analysis, asymptotic series are one of the most popular tools employed to approach such questions.

Suppose we have an asymptotic expansion of *f*(*n*):

Dividing both parts by φ_{1}(*n*) and taking the limit will produce *a*_{1}, the coefficient of the highest-order term in the expansion, which represents the rate at which *f*(*n*) changes in its leading term.

Informally, one can say: "*f*(*n*) grows approximately as *a*_{1} φ_{1}(*n*)". Taking the difference between *f*(*n*) and its approximation and then dividing by the next term in the expansion, we arrive at a more refined statement about *f*(*n*):

Here one can say that the difference between the function and its approximation grows approximately as *a*_{2} φ_{2}(*n*). The idea is that dividing the function by appropriate normalizing functions, and looking at the limiting behavior of the result, can tell us much about the limiting behavior of the original function itself.

Informally, something along these lines is happening when the sum, *S _{n}*, of independent identically distributed random variables,

where ξ is distributed as N(0, σ^{2}). This provides values of the first two constants in the informal expansion

In the case where the *X*_{i}'s do not have finite mean or variance, convergence of the shifted and rescaled sum can also occur with different centering and scaling factors:

or informally

Distributions Ξ which can arise in this way are called *stable*.^{[15]} Clearly, the normal distribution is stable, but there are also other stable distributions, such as the Cauchy distribution, for which the mean or variance are not defined. The scaling factor *b _{n}* may be proportional to

The law of the iterated logarithm specifies what is happening "in between" the law of large numbers and the central limit theorem. Specifically it says that the normalizing function intermediate in size between n of the law of large numbers and √*n* of the central limit theorem provides a non-trivial limiting behavior.

The density of the sum of two or more independent variables is the convolution of their densities (if these densities exist). Thus the central limit theorem can be interpreted as a statement about the properties of density functions under convolution: the convolution of a number of density functions tends to the normal density as the number of density functions increases without bound. These theorems require stronger hypotheses than the forms of the central limit theorem given above. Theorems of this type are often called local limit theorems. See,^{[18]} Chapter 7 for a particular local limit theorem for sums of i.i.d. random variables.

Since the characteristic function of a convolution is the product of the characteristic functions of the densities involved, the central limit theorem has yet another restatement: the product of the characteristic functions of a number of density functions becomes close to the characteristic function of the normal density as the number of density functions increases without bound, under the conditions stated above. However, to state this more precisely, an appropriate scaling factor needs to be applied to the argument of the characteristic function.

An equivalent statement can be made about Fourier transforms, since the characteristic function is essentially a Fourier transform.

The logarithm of a product is simply the sum of the logarithms of the factors. Therefore when the logarithm of a product of random variables that take only positive values approaches a normal distribution, the product itself approaches a log-normal distribution. Many physical quantities (especially mass or length, which are a matter of scale and cannot be negative) are the products of different random factors, so they follow a log-normal distribution.

Whereas the central limit theorem for sums of random variables requires the condition of finite variance, the corresponding theorem for products requires the corresponding condition that the density function be square-integrable.^{[19]}

Asymptotic normality, that is, convergence to the normal distribution after appropriate shift and rescaling, is a phenomenon much more general than the classical framework treated above, namely, sums of independent random variables (or vectors). New frameworks are revealed from time to time; no single unifying framework is available for now.

Theorem.There exists a sequence ε_{n}↓ 0 for which the following holds. Letn≥ 1, and let random variablesX_{1}, …,Xhave a log-concave joint density_{n}fsuch thatf(x_{1}, …,x) =_{n}f(|x_{1}|, …, |x|) for all_{n}x_{1}, …,x, and E(_{n}X_{k}^{2}) = 1 for allk= 1, …,n. Then the distribution ofis ε

_{n}-close to N(0, 1) in the total variation distance.^{[20]}

These two ε_{n}-close distributions have densities (in fact, log-concave densities), thus, the total variance distance between them is the integral of the absolute value of the difference between the densities. Convergence in total variation is stronger than weak convergence.

An important example of a log-concave density is a function constant inside a given convex body and vanishing outside; it corresponds to the uniform distribution on the convex body, which explains the term "central limit theorem for convex bodies".

Another example: *f*(*x*_{1}, …, *x _{n}*) = const · exp( − (|

The condition *f*(*x*_{1}, …, *x _{n}*) =

Here is a Berry-Esseen type result.

**Theorem.** Let *X*_{1}, …, *X _{n}* satisfy the assumptions of the previous theorem, then

for all *a* < *b*; here *C* is a universal (absolute) constant. Moreover, for every *c*_{1}, …, *c _{n}* ∈

The distribution of need not be approximately normal (in fact, it can be uniform).^{[23]} However, the distribution of *c*_{1}*X*_{1} + … + *c _{n}X_{n}* is close to

Theorem(Salem - Zygmund). LetUbe a random variable distributed uniformly on (0, 2π), andX=_{k}rcos(_{k}n+_{k}Ua), where_{k}

n_{k}satisfy the lacunarity condition: there existsq> 1 such thatn_{k+1}≥qn_{k}for allk,r_{k}are such that

- 0 ≤
a_{k}< 2π.Then

^{[24]}^{[25]}converges in distribution to

N(0, 1/2).

TheoremLetA_{1}, ...,A_{n}be independent random points on the planeR^{2}each having the two-dimensional standard normal distribution. LetK_{n}be the convex hull of these points, andXthe area of_{n}K_{n}Then^{[26]}converges in distribution to

N(0, 1) asntends to infinity.

The same holds in all dimensions (2, 3, ...).

The polytope *K*_{n} is called Gaussian random polytope.

A similar result holds for the number of vertices (of the Gaussian polytope), the number of edges, and in fact, faces of all dimensions.^{[27]}

A linear function of a matrix *M* is a linear combination of its elements (with given coefficients), *M* ↦ tr(*AM*) where *A* is the matrix of the coefficients; see Trace_(linear_algebra)#Inner product.

A random orthogonal matrix is said to be distributed uniformly, if its distribution is the normalized Haar measure on the orthogonal group O(*n*, **R**); see Rotation matrix#Uniform random rotation matrices.

**Theorem.** Let *M* be a random orthogonal *n* × *n* matrix distributed uniformly, and *A* a fixed *n* × *n* matrix such that tr(*AA**) = *n*, and let *X* = tr(*AM*). Then^{[28]} the distribution of *X* is close to N(0, 1) in the total variation metric up to^{[clarification needed]} 2√3/(*n*−1).

**Theorem.** Let random variables *X*_{1}, *X*_{2}, … ∈ *L*_{2}(Ω) be such that *X _{n}* → 0 weakly in

A generalization of the classical central limit theorem to the context of Tsallis statistics has been described by Umarov, Tsallis and Steinberg^{[30]} in which the independence constraint for the i.i.d. variables is relaxed to an extent defined by the *q* parameter, with independence being recovered as q->1. In analogy to the classical central limit theorem, such random variables with fixed mean and variance tend towards the q-Gaussian distribution, which maximizes the Tsallis entropy under these constraints. Umarov, Tsallis, Gell-Mann and Steinberg have defined similar generalizations of all symmetric alpha-stable distributions, and have formulated a number of conjectures regarding their relevance to an even more general Central limit theorem.^{[31]}

A simple example of the central limit theorem is rolling a large number of identical, unbiased dice. The distribution of the sum (or average) of the rolled numbers will be well approximated by a normal distribution. Since real-world quantities are often the balanced sum of many unobserved random events, the central limit theorem also provides a partial explanation for the prevalence of the normal probability distribution. It also justifies the approximation of large-sample statistics to the normal distribution in controlled experiments.

Published literature contains a number of useful and interesting examples and applications relating to the central limit theorem.^{[32]} One source^{[33]} states the following examples:

- The probability distribution for total distance covered in a random walk (biased or unbiased) will tend toward a normal distribution.
- Flipping a large number of coins will result in a normal distribution for the total number of heads (or equivalently total number of tails).

From another viewpoint, the central limit theorem explains the common appearance of the "Bell Curve" in density estimates applied to real world data. In cases like electronic noise, examination grades, and so on, we can often regard a single measured value as the weighted average of a large number of small effects. Using generalisations of the central limit theorem, we can then see that this would often (though not always) produce a final distribution that is approximately normal.

In general, the more a measurement is like the sum of independent variables with equal influence on the result, the more normality it exhibits. This justifies the common use of this distribution to stand in for the effects of unobserved variables in models like the linear model.

Regression analysis and in particular ordinary least squares specifies that a dependent variable depends according to some function upon one or more independent variables, with an additive error term. Various types of statistical inference on the regression assume that the error term is normally distributed. This assumption can be justified by assuming that the error term is actually the sum of a large number of independent error terms; even if the individual error terms are not normally distributed, by the central limit theorem their sum can be assumed to be normally distributed.

Main article: Illustration of the central limit theorem

Given its importance to statistics, a number of papers and computer packages are available that demonstrate the convergence involved in the central limit theorem.^{[34]}

Tijms writes:^{[35]}

The central limit theorem has an interesting history. The first version of this theorem was postulated by the French-born mathematician Abraham de Moivre who, in a remarkable article published in 1733, used the normal distribution to approximate the distribution of the number of heads resulting from many tosses of a fair coin. This finding was far ahead of its time, and was nearly forgotten until the famous French mathematician Pierre-Simon Laplace rescued it from obscurity in his monumental work

Théorie Analytique des Probabilités, which was published in 1812. Laplace expanded De Moivre's finding by approximating the binomial distribution with the normal distribution. But as with De Moivre, Laplace's finding received little attention in his own time. It was not until the nineteenth century was at an end that the importance of the central limit theorem was discerned, when, in 1901, Russian mathematician Aleksandr Lyapunov defined it in general terms and proved precisely how it worked mathematically. Nowadays, the central limit theorem is considered to be the unofficial sovereign of probability theory.

Sir Francis Galton described the Central Limit Theorem as:^{[36]}

I know of scarcely anything so apt to impress the imagination as the wonderful form of cosmic order expressed by the "Law of Frequency of Error". The law would have been personified by the Greeks and deified, if they had known of it. It reigns with serenity and in complete self-effacement, amidst the wildest confusion. The huger the mob, and the greater the apparent anarchy, the more perfect is its sway. It is the supreme law of Unreason. Whenever a large sample of chaotic elements are taken in hand and marshaled in the order of their magnitude, an unsuspected and most beautiful form of regularity proves to have been latent all along.

The actual term "central limit theorem" (in German: "zentraler Grenzwertsatz") was first used by George Pólya in 1920 in the title of a paper.^{[37]}^{[38]} Pólya referred to the theorem as "central" due to its importance in probability theory. According to Le Cam, the French school of probability interprets the word *central* in the sense that "it describes the behaviour of the centre of the distribution as opposed to its tails".^{[38]} The abstract of the paper *On the central limit theorem of calculus of probability and the problem of moments* by Pólya^{[37]} in 1920 translates as follows.

The occurrence of the Gaussian probability density 1 =

e^{−x2}in repeated experiments, in errors of measurements, which result in the combination of very many and very small elementary errors, in diffusion processes etc., can be explained, as is well-known, by the very same limit theorem, which plays a central role in the calculus of probability. The actual discoverer of this limit theorem is to be named Laplace; it is likely that its rigorous proof was first given by Tschebyscheff and its sharpest formulation can be found, as far as I am aware of, in an article by Liapounoff. [...]

A thorough account of the theorem's history, detailing Laplace's foundational work, as well as Cauchy's, Bessel's and Poisson's contributions, is provided by Hald.^{[39]} Two historical accounts, one covering the development from Laplace to Cauchy, the second the contributions by von Mises, Pólya, Lindeberg, Lévy, and Cramér during the 1920s, are given by Hans Fischer.^{[40]} Le Cam describes a period around 1935.^{[38]} Bernstein^{[41]} presents a historical discussion focusing on the work of Pafnuty Chebyshev and his students Andrey Markov and Aleksandr Lyapunov that led to the first proofs of the CLT in a general setting.

A curious footnote to the history of the Central Limit Theorem is that a proof of a result similar to the 1922 Lindeberg CLT was the subject of Alan Turing's 1934 Fellowship Dissertation for King's College at the University of Cambridge. Only after submitting the work did Turing learn it had already been proved. Consequently, Turing's dissertation was never published.^{[42]}^{[43]}^{[44]}

- Asymptotic equipartition property
- Benford's Law - Result of extension of CLT to product of random variables.
- Central limit theorem for directional statistics - Central limit theorem applied to the case of directional statistics
- Delta method – to compute the limit distribution of a function of a random variable.
- Erdős-Kac theorem - connects the number of prime factors of an integer with the normal probability distribution
- Fisher–Tippett–Gnedenko theorem – limit theorem for extremum values (such as max{
*X*})_{n} - Illustration of the central limit theorem
- Stable distribution - distributions such that linear combinations of i.i.d. samples lead to samples with the same distribution
- Theorem of de Moivre–Laplace
- Tweedie convergence theorem - A theorem that can be considered to bridge between the central limit theorem and the Poisson convergence theorem
^{[45]}

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- Central Limit Theorem interactive simulation to experiment with various parameters
- CLT in NetLogo (Connected Probability — ProbLab) interactive simulation w/ a variety of modifiable parameters
- General Central Limit Theorem Activity & corresponding SOCR CLT Applet (Select the Sampling Distribution CLT Experiment from the drop-down list of SOCR Experiments)
- Generate sampling distributions in Excel Specify arbitrary population, sample size, and sample statistic.
- MIT OpenCourseWare Lecture 18.440
*Probability and Random Variables*, Spring 2011, Scott Sheffield Another proof. Retrieved 2012-04-08. - CAUSEweb.org is a site with many resources for teaching statistics including the Central Limit Theorem
- The Central Limit Theorem by Chris Boucher, Wolfram Demonstrations Project.
- Weisstein, Eric W., "Central Limit Theorem",
*MathWorld*. - Animations for the Central Limit Theorem by Yihui Xie using the R package animation
- Teaching demonstrations of the CLT:
**clt.examp**function in Greg Snow (2012).*TeachingDemos: Demonstrations for teaching and learning. R package version 2.8.*.