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.

Central Limit Theorem
TypeTheorem
FieldProbability theory
StatementThe scaled sum of a sequence of i.i.d. random variables with finite positive variance converges in distribution to the normal distribution.
Generalizations Lindeberg's CLT

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 as late as 1920.[1]

In statistics, the CLT can be stated as: let denote a statistical sample of size from a population with expected value (average) and finite positive variance , and let denote the sample mean (which is itself a random variable). Then the limit as of the distribution of is a normal distribution with mean and variance .[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 that 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 was large enough, the probability distribution of these averages will closely approximate a normal distribution.

The central limit theorem has several variants. In its common form, the random variables must be independent and identically distributed (i.i.d.). This requirement can be weakened; convergence of the mean to the normal distribution also occurs for non-identical distributions or for non-independent observations if they comply with certain conditions.

The earliest version of this theorem, that the normal distribution may be used as an approximation to the binomial distribution, is the de Moivre–Laplace theorem.

Independent sequences

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Whatever the form of the population distribution, the sampling distribution tends to a Gaussian, and its dispersion is given by the central limit theorem.[3]

Classical CLT

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Let   be a sequence of i.i.d. random variables having a distribution with expected value given by   and finite variance given by   Suppose we are interested in the sample average  

By the law of large numbers, the sample average converges almost surely (and therefore also converges in probability) to the expected value   as  

The classical central limit theorem describes the size and the distributional form of the stochastic fluctuations around the deterministic number   during this convergence. More precisely, it states that as   gets larger, the distribution of the difference between the sample average   and its limit   when multiplied by the factor   — that is,   — approaches the normal distribution with mean   and variance   For large enough   the distribution of   gets arbitrarily close to the normal distribution with mean   and variance  

The usefulness of the theorem is that the distribution of   approaches normality regardless of the shape of the distribution of the individual   Formally, the theorem can be stated as follows:

Lindeberg–Lévy CLT — Suppose   is a sequence of i.i.d. random variables with   and   Then, as   approaches infinity, the random variables   converge in distribution to a normal  :[4]  

In the case   convergence in distribution means that the cumulative distribution functions of   converge pointwise to the cdf of the   distribution: for every real number     where   is the standard normal cdf evaluated at   The convergence is uniform in   in the sense that   where   denotes the least upper bound (or supremum) of the set.[5]

Lyapunov CLT

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In this variant of the central limit theorem the random variables   have to be independent, but not necessarily identically distributed. The theorem also requires that random variables   have moments of some order  , and that the rate of growth of these moments is limited by the Lyapunov condition given below.

Lyapunov CLT[6] — Suppose   is a sequence of independent random variables, each with finite expected value   and variance  . Define  

If for some  , Lyapunov’s condition   is satisfied, then a sum of   converges in distribution to a standard normal random variable, as   goes to infinity:  

In practice it is usually easiest to check Lyapunov's condition for  .

If a sequence of random variables satisfies Lyapunov's condition, then it also satisfies Lindeberg's condition. The converse implication, however, does not hold.

Lindeberg (-Feller) CLT

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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     where   is the indicator function. Then the distribution of the standardized sums   converges towards the standard normal distribution  .

Multidimensional CLT

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Proofs that use characteristic functions can be extended to cases where each individual   is a random vector in  , with mean vector   and covariance matrix   (among the components of the vector), and these random vectors are independent and identically distributed. The multidimensional central limit theorem states that when scaled, sums converge to a multivariate normal distribution.[7] Summation of these vectors is done component-wise.

For   let   be independent random vectors. The sum of the random vectors   is   and their average is   Therefore,  

The multivariate central limit theorem states that   where the covariance matrix   is equal to  

The multivariate central limit theorem can be proved using the Cramér–Wold theorem.[7]

The rate of convergence is given by the following Berry–Esseen type result:

Theorem[8] — Let   be independent  -valued random vectors, each having mean zero. Write   and assume   is invertible. Let   be a  -dimensional Gaussian with the same mean and same covariance matrix as  . Then for all convex sets  ,   where   is a universal constant,  , and   denotes the Euclidean norm on  .

It is unknown whether the factor   is necessary.[9]

The Generalized Central Limit Theorem

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The Generalized Central Limit Theorem (GCLT) was an effort of multiple mathematicians (Bernstein, Lindeberg, Lévy, Feller, Kolmogorov, and others) over the period from 1920 to 1937.[10] The first published complete proof of the GCLT was in 1937 by Paul Lévy in French.[11] An English language version of the complete proof of the GCLT is available in the translation of Gnedenko and Kolmogorov's 1954 book.[12]

The statement of the GCLT is as follows:[13]

A non-degenerate random variable Z is α-stable for some 0 < α ≤ 2 if and only if there is an independent, identically distributed sequence of random variables X1, X2, X3, ... and constants an > 0, bn ∈ ℝ with
an (X1 + ... + Xn) − bnZ.
Here → means the sequence of random variable sums converges in distribution; i.e., the corresponding distributions satisfy Fn(y) → F(y) at all continuity points of F.

In other words, if sums of independent, identically distributed random variables converge in distribution to some Z, then Z must be a stable distribution.

Dependent processes

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CLT under weak dependence

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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   where   is so-called strong mixing coefficient.

A simplified formulation of the central limit theorem under strong mixing is:[14]

Theorem — Suppose that   is stationary and  -mixing with   and that   and  . Denote  , then the limit   exists, and if   then   converges in distribution to  .

In fact,   where the series converges absolutely.

The assumption   cannot be omitted, since the asymptotic normality fails for   where   are another stationary sequence.

There is a stronger version of the theorem:[15] the assumption   is replaced with  , and the assumption   is replaced with  

Existence of such   ensures the conclusion. For encyclopedic treatment of limit theorems under mixing conditions see (Bradley 2007).

Martingale difference CLT

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Theorem — Let a martingale   satisfy

  •   in probability as n → ∞,
  • for every ε > 0,   as n → ∞,

then   converges in distribution to   as  .[16][17]

Remarks

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Proof of classical CLT

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The central limit theorem has a proof using characteristic functions.[18] It is similar to the proof of the (weak) law of large numbers.

Assume   are independent and identically distributed random variables, each with mean   and finite variance  . The sum   has mean   and variance  . Consider the random variable   where in the last step we defined the new random variables  , each with zero mean and unit variance ( ). The characteristic function of   is given by   where in the last step we used the fact that all of the   are identically distributed. The characteristic function of   is, by Taylor's theorem,   where   is "little o notation" for some function of   that goes to zero more rapidly than  . By the limit of the exponential function ( ), the characteristic function of   equals  

All of the higher order terms vanish in the limit  . The right hand side equals the characteristic function of a standard normal distribution  , which implies through Lévy's continuity theorem that the distribution of   will approach   as  . Therefore, the sample average   is such that   converges to the normal distribution  , from which the central limit theorem follows.

Convergence to the limit

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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.[citation needed]

The convergence in the central limit theorem is uniform because the limiting cumulative distribution function is continuous. If the third central moment   exists and is finite, then the speed of convergence is at least on the order of   (see Berry–Esseen theorem). Stein's method[19] can be used not only to prove the central limit theorem, but also to provide bounds on the rates of convergence for selected metrics.[20]

The convergence to the normal distribution is monotonic, in the sense that the entropy of   increases monotonically to that of the normal distribution.[21]

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 realizations of the sum of n independent identical discrete variables, the piecewise-linear 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.

Common misconceptions

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Studies have shown that the central limit theorem is subject to several common but serious misconceptions, some of which appear in widely used textbooks.[22][23][24] These include:

  • The misconceived belief that the theorem applies to random sampling of any variable, rather than to the mean values (or sums) of iid random variables extracted from a population by repeated sampling. That is, the theorem assumes the random sampling produces a sampling distribution formed from different values of means (or sums) of such random variables.
  • The misconceived belief that the theorem ensures that random sampling leads to the emergence of a normal distribution for sufficiently large samples of any random variable, regardless of the population distribution. In reality, such sampling asymptotically reproduces the properties of the population, an intuitive result underpinned by the Glivenko-Cantelli theorem.
  • The misconceived belief that the theorem leads to a good approximation of a normal distribution for sample sizes greater than around 30,[25] allowing reliable inferences regardless of the nature of the population. In reality, this empirical rule of thumb has no valid justification, and can lead to seriously flawed inferences. See Z-test for where the approximation holds.

Relation to the law of large numbers

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The law of large numbers as well as the central limit theorem are partial solutions to a general problem: "What is the limiting behavior of Sn as n approaches infinity?" In mathematical analysis, asymptotic series are one of the most popular tools employed to approach such questions.

Suppose we have an asymptotic expansion of  :  

Dividing both parts by φ1(n) and taking the limit will produce a1, 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 a1φ1(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 a2φ2(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 happens when the sum, Sn, of independent identically distributed random variables, X1, ..., Xn, is studied in classical probability theory.[citation needed] If each Xi has finite mean μ, then by the law of large numbers, Sn/nμ.[26] If in addition each Xi has finite variance σ2, then by the central limit theorem,   where ξ is distributed as N(0,σ2). This provides values of the first two constants in the informal expansion  

In the case where the Xi 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.[27] 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 bn may be proportional to nc, for any c1/2; it may also be multiplied by a slowly varying function of n.[28][29]

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 n log log n, intermediate in size between n of the law of large numbers and n of the central limit theorem, provides a non-trivial limiting behavior.

Alternative statements of the theorem

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Density functions

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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 Petrov[30] for a particular local limit theorem for sums of independent and identically distributed random variables.

Characteristic functions

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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. Specifically, 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.

Calculating the variance

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Let Sn be the sum of n random variables. Many central limit theorems provide conditions such that Sn/Var(Sn) converges in distribution to N(0,1) (the normal distribution with mean 0, variance 1) as n → ∞. In some cases, it is possible to find a constant σ2 and function f(n) such that Sn/(σn⋅f(n)) converges in distribution to N(0,1) as n→ ∞.

Lemma[31] — Suppose   is a sequence of real-valued and strictly stationary random variables with   for all  ,  , and  . Construct  

  1. If   is absolutely convergent,  , and   then   as   where  .
  2. If in addition   and   converges in distribution to   as   then   also converges in distribution to   as  .

Extensions

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Products of positive random variables

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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. This multiplicative version of the central limit theorem is sometimes called Gibrat's law.

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.[32]

Beyond the classical framework

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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.

Convex body

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Theorem — There exists a sequence εn ↓ 0 for which the following holds. Let n ≥ 1, and let random variables X1, ..., Xn have a log-concave joint density f such that f(x1, ..., xn) = f(|x1|, ..., |xn|) for all x1, ..., xn, and E(X2
k
) = 1
for all k = 1, ..., n. Then the distribution of   is εn-close to   in the total variation distance.[33]

These two εn-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(x1, ..., xn) = const · exp(−(|x1|α + ⋯ + |xn|α)β) where α > 1 and αβ > 1. If β = 1 then f(x1, ..., xn) factorizes into const · exp (−|x1|α) … exp(−|xn|α), which means X1, ..., Xn are independent. In general, however, they are dependent.

The condition f(x1, ..., xn) = f(|x1|, ..., |xn|) ensures that X1, ..., Xn are of zero mean and uncorrelated;[citation needed] still, they need not be independent, nor even pairwise independent.[citation needed] By the way, pairwise independence cannot replace independence in the classical central limit theorem.[34]

Here is a Berry–Esseen type result.

Theorem — Let X1, ..., Xn satisfy the assumptions of the previous theorem, then [35]   for all a < b; here C is a universal (absolute) constant. Moreover, for every c1, ..., cnR such that c2
1
+ ⋯ + c2
n
= 1
,  

The distribution of X1 + ⋯ + Xn/n need not be approximately normal (in fact, it can be uniform).[36] However, the distribution of c1X1 + ⋯ + cnXn is close to   (in the total variation distance) for most vectors (c1, ..., cn) according to the uniform distribution on the sphere c2
1
+ ⋯ + c2
n
= 1
.

Lacunary trigonometric series

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Theorem (SalemZygmund) — Let U be a random variable distributed uniformly on (0,2π), and Xk = rk cos(nkU + ak), where

  • nk satisfy the lacunarity condition: there exists q > 1 such that nk + 1qnk for all k,
  • rk are such that  
  • 0 ≤ ak < 2π.

Then[37][38]   converges in distribution to  .

Gaussian polytopes

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Theorem — Let A1, ..., An be independent random points on the plane R2 each having the two-dimensional standard normal distribution. Let Kn be the convex hull of these points, and Xn the area of Kn Then[39]   converges in distribution to   as n tends to infinity.

The same also holds in all dimensions greater than 2.

The polytope Kn is called a 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.[40]

Linear functions of orthogonal matrices

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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[41] the distribution of X is close to   in the total variation metric up to[clarification needed] 23/n − 1.

Subsequences

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Theorem — Let random variables X1, X2, ... ∈ L2(Ω) be such that Xn → 0 weakly in L2(Ω) and X
n
→ 1
weakly in L1(Ω). Then there exist integers n1 < n2 < ⋯ such that   converges in distribution to   as k tends to infinity.[42]

Random walk on a crystal lattice

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The central limit theorem may be established for the simple random walk on a crystal lattice (an infinite-fold abelian covering graph over a finite graph), and is used for design of crystal structures.[43][44]

Applications and examples

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A simple example of the central limit theorem is rolling many 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.

Comparison of probability density functions p(k) for the sum of n fair 6-sided dice to show their convergence to a normal distribution with increasing n, in accordance to the central limit theorem. In the bottom-right graph, smoothed profiles of the previous graphs are rescaled, superimposed and compared with a normal distribution (black curve).
This figure demonstrates the central limit theorem. The sample means are generated using a random number generator, which draws numbers between 0 and 100 from a uniform probability distribution. It illustrates that increasing sample sizes result in the 500 measured sample means being more closely distributed about the population mean (50 in this case). It also compares the observed distributions with the distributions that would be expected for a normalized Gaussian distribution, and shows the chi-squared values that quantify the goodness of the fit (the fit is good if the reduced chi-squared value is less than or approximately equal to one). The input into the normalized Gaussian function is the mean of sample means (~50) and the mean sample standard deviation divided by the square root of the sample size (~28.87/n), which is called the standard deviation of the mean (since it refers to the spread of sample means).
 
Another simulation using the binomial distribution. Random 0s and 1s were generated, and then their means calculated for sample sizes ranging from 1 to 2048. Note that as the sample size increases the tails become thinner and the distribution becomes more concentrated around the mean.

Regression

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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 many independent error terms; even if the individual error terms are not normally distributed, by the central limit theorem their sum can be well approximated by a normal distribution.

Other illustrations

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Given its importance to statistics, a number of papers and computer packages are available that demonstrate the convergence involved in the central limit theorem.[45]

History

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Dutch mathematician Henk Tijms writes:[46]

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 in this way:[47]

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 marshalled 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.[48][49] 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".[49] The abstract of the paper On the central limit theorem of calculus of probability and the problem of moments by Pólya[48] in 1920 translates as follows.

The occurrence of the Gaussian probability density 1 = ex2 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.[50] 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.[51] Le Cam describes a period around 1935.[49] Bernstein[52] 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 not published.[53]

See also

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Notes

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  1. ^ Fischer (2011), p. [page needed].
  2. ^ Montgomery, Douglas C.; Runger, George C. (2014). Applied Statistics and Probability for Engineers (6th ed.). Wiley. p. 241. ISBN 9781118539712.
  3. ^ Rouaud, Mathieu (2013). Probability, Statistics and Estimation (PDF). p. 10. Archived (PDF) from the original on 2022-10-09.
  4. ^ Billingsley (1995), p. 357.
  5. ^ Bauer (2001), p. 199, Theorem 30.13.
  6. ^ Billingsley (1995), p. 362.
  7. ^ a b van der Vaart, A.W. (1998). Asymptotic statistics. New York, NY: Cambridge University Press. ISBN 978-0-521-49603-2. LCCN 98015176.
  8. ^ O’Donnell, Ryan (2014). "Theorem 5.38". Archived from the original on 2019-04-08. Retrieved 2017-10-18.
  9. ^ Bentkus, V. (2005). "A Lyapunov-type bound in  ". Theory Probab. Appl. 49 (2): 311–323. doi:10.1137/S0040585X97981123.
  10. ^ Le Cam, L. (February 1986). "The Central Limit Theorem around 1935". Statistical Science. 1 (1): 78–91. JSTOR 2245503.
  11. ^ Lévy, Paul (1937). Theorie de l'addition des variables aleatoires [Combination theory of unpredictable variables]. Paris: Gauthier-Villars.
  12. ^ Gnedenko, Boris Vladimirovich; Kologorov, Andreĭ Nikolaevich; Doob, Joseph L.; Hsu, Pao-Lu (1968). Limit distributions for sums of independent random variables. Reading, MA: Addison-wesley.
  13. ^ Nolan, John P. (2020). Univariate stable distributions, Models for Heavy Tailed Data. Springer Series in Operations Research and Financial Engineering. Switzerland: Springer. doi:10.1007/978-3-030-52915-4. ISBN 978-3-030-52914-7. S2CID 226648987.
  14. ^ Billingsley (1995), Theorem 27.4.
  15. ^ Durrett (2004), Sect. 7.7(c), Theorem 7.8.
  16. ^ Durrett (2004), Sect. 7.7, Theorem 7.4.
  17. ^ Billingsley (1995), Theorem 35.12.
  18. ^ Lemons, Don (2003). An Introduction to Stochastic Processes in Physics. Johns Hopkins University Press. doi:10.56021/9780801868665. ISBN 9780801876387. Retrieved 2016-08-11.
  19. ^ Stein, C. (1972). "A bound for the error in the normal approximation to the distribution of a sum of dependent random variables". Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability. 6 (2): 583–602. MR 0402873. Zbl 0278.60026.
  20. ^ Chen, L. H. Y.; Goldstein, L.; Shao, Q. M. (2011). Normal approximation by Stein's method. Springer. ISBN 978-3-642-15006-7.
  21. ^ Artstein, S.; Ball, K.; Barthe, F.; Naor, A. (2004). "Solution of Shannon's Problem on the Monotonicity of Entropy". Journal of the American Mathematical Society. 17 (4): 975–982. doi:10.1090/S0894-0347-04-00459-X.
  22. ^ Brewer, J.K. (1985). "Behavioral statistics textbooks: Source of myths and misconceptions?". Journal of Educational Statistics. 10 (3): 252–268. doi:10.3102/10769986010003252. S2CID 119611584.
  23. ^ Yu, C.; Behrens, J.; Spencer, A. Identification of Misconception in the Central Limit Theorem and Related Concepts, American Educational Research Association lecture 19 April 1995
  24. ^ Sotos, A.E.C.; Vanhoof, S.; Van den Noortgate, W.; Onghena, P. (2007). "Students' misconceptions of statistical inference: A review of the empirical evidence from research on statistics education". Educational Research Review. 2 (2): 98–113. doi:10.1016/j.edurev.2007.04.001.
  25. ^ "Sampling distribution of the sample mean (video) | Khan Academy". 2 June 2023. Archived from the original on 2023-06-02. Retrieved 2023-10-08.
  26. ^ Rosenthal, Jeffrey Seth (2000). A First Look at Rigorous Probability Theory. World Scientific. Theorem 5.3.4, p. 47. ISBN 981-02-4322-7.
  27. ^ Johnson, Oliver Thomas (2004). Information Theory and the Central Limit Theorem. Imperial College Press. p. 88. ISBN 1-86094-473-6.
  28. ^ Uchaikin, Vladimir V.; Zolotarev, V.M. (1999). Chance and Stability: Stable distributions and their applications. VSP. pp. 61–62. ISBN 90-6764-301-7.
  29. ^ Borodin, A. N.; Ibragimov, I. A.; Sudakov, V. N. (1995). Limit Theorems for Functionals of Random Walks. AMS Bookstore. Theorem 1.1, p. 8. ISBN 0-8218-0438-3.
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