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Understanding Gaussians

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105 points lapnect | 1 comments | 22 Oct 24 07:54 UTC | HN request time: 0s | source
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wodenokoto ◴[22 Oct 24 11:33 UTC] No.41913255[source]▶
> You can see that the data is clustered around the mean value. Another way of saying this is that the distribution has a definite scale. [..] it might theoretically be possible to be 2 meters taller than the mean, but that’s it. People will never be 3 or 4 meters taller than the mean, no matter how many people you see.

The way the author defines definite scale is that there is a max and a minimum, but that is not true for a gaussian distribution. It is also not true that if we keep sampling wealth (an example of a distribution without definite scale used in the article), there is no limit to the maximum.

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klysm ◴[22 Oct 24 12:48 UTC] No.41913691[source]▶
I think he’s saying that the distribution of human heights has definite scale, not the Gaussian?
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1. deepnet ◴[22 Oct 24 13:24 UTC] No.41914005[source]▶
Jinlian (1964–1982) of China was 8 feet, 1 inch (2.46 centimeters) when she died, making her the tallest woman ever. According to Guinness World Records, Zeng is the only woman to have passed 8 feet (about 2.44 meters)

Mean from article 163.

So the facts check out.

Author is correct.

Also very interesting the suggestion that human height is not Gaussian.

Snip :

“ Why female soldiers only? If we were to mix male and female soldiers, we would get a distribution with two peaks, which would not be Gaussian.

“

Which begs the question what other human statistics are non Gaussian if sexes are mixed and does this apply to other strong differentiators like historical time, nutrition, neural tribes ?

Statistics is highly non-trivial. “