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Central Limit Theorem

In probability, when independent random variables are added, their properly normalized sum tends toward a normal distribution (informally a "bell curve") even if the original variables themselves are not normally distributed.

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Origin

Abraham de Moivre first postulated a version in 1733, using the normal distribution to approximate coin-toss outcomes—a finding nearly forgotten until French mathematician Pierre-Simon Laplace rescued it in his 1812 Théorie analytique des probabilités. Laplace proved the standard version in 1810, developing the characteristic function for large-sample theory. Siméon-Denis Poisson began improvements in 1824; Russian mathematician Aleksandr Lyapunov defined it in general terms in 1901, proving its precise mechanism.

Updated February 22, 2026