Why the Gaussian is everywhere: sums of many independent contributions tend to it whatever their own shape.The central limit theorem (CLT) is a cornerstone of probability theory. It states that the sum (or average) of a large number of independent, identically distributed random variables, regardless of their original distribution, tends toward a normal (Gaussian) distribution. This explains why the Gaussian appears so often in nature, from measurement errors to biological traits.
The first version was proved by Pierre-Simon Laplace in 1810, building on earlier work by Abraham de Moivre. The modern formulation is due to Aleksandr Lyapunov in 1901.
The theorem requires that the variables have finite mean and variance. As the number of variables increases, the distribution of the standardized sum converges to the standard normal distribution. The rate of convergence depends on the shape of the original distribution; symmetric distributions converge faster than skewed ones.
Underlies statistical inference, quality control (e.g., control charts), and the justification for using normal approximations in fields from finance to psychology.
The CLT works even for discrete distributions like the Poisson or binomial, which is why coin flips and dice rolls produce bell-shaped histograms when repeated many times.









