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# The Most Beautiful Statistic: The History and the Science of the Humble Mean

**[Towards Data Science](https://daily.dev/sources/tds)** · 44 min read · 0 upvotes · 0 comments

## Summary

A deep dive into the arithmetic mean covering its mathematical foundations, historical origins, and practical significance. Topics include the mean as a probability-weighted sum, its connection to the Weak Law of Large Numbers (Jakob Bernoulli), and its relationship to least squares estimation (Legendre vs. Gauss priority dispute). The piece also covers why probability p is the correct weight in the mean's formula, the mean's role in regression and neural networks, and its serious drawbacks: inability to capture distributional shape, sensitivity to outliers, and the dangers of the 'average man' fallacy illustrated by the 1950 U.S. Air Force anthropometric study and Adolphe Quetelet's misuse of averages.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/the-most-beautiful-statistic-the-history-and-the-science-of-the-humble-mean>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#data-science](https://daily.dev/tags/data-science), [#statistics](https://daily.dev/tags/statistics)

[View this post on daily.dev](https://daily.dev/posts/the-most-beautiful-statistic-the-history-and-the-science-of-the-humble-mean-0ktdegklt)

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