Because Gauss played such a prominent role in determining the usefulness of the normal probability distribution, the normal probability distribution is often called the Gaussian distribution. Carl Friedrich Gauss in 1809 used the normal distribution to solve the important statistical problem of combining observations. This was later extended by Laplace to the so-called CLT, which is one of the most important results in probability. He used it to approximate probabilities associated with binomial random variables when n is large. The normal probability distribution was introduced by the French mathematician Abraham de Moivre in 1733. The density function of a normal probability distribution is bell shaped and symmetric about the mean. The single most important distribution in probability and statistics is the normal probability distribution. Tsokos, in Mathematical Statistics with Applications in R (Third Edition), 2021 3.2.4 Normal probability distribution
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