Re:Understanding Gaussians
by klysm on Tuesday October 22, 2024 @08:48AM
(#41913691)
I think he’s saying that the distribution of human heights has definite scale, not the Gaussian?
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Nothing is Gaussian then. What probability distribution allows for Graham's Number to be a possibility?
The Gaussian has non-zero mass everywhere.
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. “
Human height (by gender) very nearly follows a gaussian distribution- because height is determined (hand-wave away complexity) by a sum of many independent random variables. In reality it's not truly gaussian for a number of reasons.
Re:Understanding Gaussians
by wodenokoto on Tuesday October 22, 2024 @10:57PM
(#41921312)
No, author very much says the Gaussian has definite scale:
> There are a few distributions like this with a definite scale, but the Gaussian is the most famous one.