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 Given its importance to statistics, a number of papers and computer packages are available that demonstrate the convergence involved in the central limit theorem. History Dutch mathematician Henk Tijms writes: Sir Francis Galton described the Central Limit Theorem in this way: The actual term "central limit theorem" (in German: "zentraler Grenzwertsatz") was first used by George Pólya in 1920 in the title of a paper. 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". The abstract of the paper On the central limit theorem of calculus of probability and the problem of moments by Pólya in 1920 translates as follows. 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. 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. Le Cam describes a period around 1935. Bernstein 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. See also Asymptotic equipartition property Asymptotic distribution Bates distribution Benford's law – Result of extension of CLT to product of random variables. Berry–Esseen theorem 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 )