The normal distribution pops up everywhere in statistics. Contrary to popular belief, the name does not come from “normal” as in “conventional.” Instead the term comes from a detail in a proof by Gauss discussed below where he showed that two things were perpendicular in a sense.
(The word “normal” originally meant “at a right angle,” going back to the Latin word normalis for a carpenter’s square. Later the word took on the metaphorical meaning of something in line with custom. Mathematicians sometimes use “normal” in the original sense of being orthogonal.)
The mistaken etymology persists because the normal distribution is conventional. Statisticians often assume anything random has a normal distribution by default. While this assumption is not always justified, it often works remarkably well. This post gives four lines of reasoning that lead naturally to the normal distribution.
1) The earliest characterization of the normal distribution is the central limit theorem, going back to Abraham de Moivre. Roughly speaking, this theorem says that if you average enough distributions together, even if they’re not normal, in the limit their average is normal. But this justification for assuming normal distributions everywhere has a couple problems. First, the convergence in the central limit theorem may be slow, depending on what is being averaged. Second, if you relax the hypotheses on the central limit theorem, other stable distributions with thicker tails also satisfy a sort of central limit theorem. The characterizations given below are more satisfying because they do not rely on limit theorems.
2) The astronomer William Herschel discovered the simplest characterization of the normal. He wanted to characterize the errors in astronomical measurements. He assumed (1) the distribution of errors in the x and y directions must be independent, and (2) the distribution of errors must be independent of angle when expressed in polar coordinates. These are very natural assumptions for an astronomer, and the only solution is a product of the same normal distribution in x and y. James Clerk Maxwell came up with an analogous derivation in three dimensions when modeling gas dynamics.
3) Carl Friedrich Gauss came up with the characterization of the normal distribution that caused it to be called the “Gaussian” distribution. There are two strategies for estimating the mean of a random variable from a sample: the arithmetic mean of the samples, and the maximum likelihood value. Only for the normal distribution do these coincide.
4) The final characterization listed here is in terms of entropy. For a specified mean and variance, the probability density with the greatest entropy (least information) is the normal distribution. I don’t know who discovered this result, but I read it in C. R. Rao‘s book. Perhaps it’s his result. If anyone knows, please let me know and I’ll update this post. For advocates of maximum entropy this is the most important characterization of the normal distribution.
Related post: How the Central Limit Theorem began
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