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Re:Understanding Gaussians by wodenokoto on Tuesday October 22, 2024 @07:33AM (#41913255)
> 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.

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?
Re:Understanding Gaussians by nwnwhwje on Tuesday October 22, 2024 @11:10AM (#41915070)
Nothing is Gaussian then. What probability distribution allows for Graham's Number to be a possibility?
Re:Understanding Gaussians by klysm on Wednesday October 23, 2024 @08:50AM (#41924538)
The Gaussian has non-zero mass everywhere.
Re:Understanding Gaussians by deepnet on Tuesday October 22, 2024 @09:24AM (#41914005)
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. “

Re:Understanding Gaussians by dekhn on Tuesday October 22, 2024 @03:56PM (#41918063)
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.

Re:Understanding Gaussians by shiandow on Tuesday October 22, 2024 @09:32AM (#41914085)
It's an oversimplification but at some point there is really no difference between impossible and 'incredibly small probability'.

I mean sure it is possible for all air molecules to randomly all go to the same corner of the room at the same time (heck it is inevitable in some sense), you can play it back in reverse to check no laws of physics were broken, but practically that simply does not happen.

Re:Understanding Gaussians by KK7NIL on Tuesday October 22, 2024 @02:00PM (#41916910)
> at some point there is really no difference between impossible and 'incredibly small probability'.

This is not true.

Using your air molecules example: Every microstate (i.e. location and speed of all the molecules) possible under the given macrostate (temperature, number of molecules, etc) has a probability of happening of 0, but aren't impossible, simply because the microstates are real variables and real numbers are uncountable. Impossible microstates also have 0 probability but are obviously not the same.

Re:Understanding Gaussians by shiandow on Monday October 28, 2024 @09:56AM (#41971036)
A bit late, but if you do consider events with vanishingly small probability to be impossible then they do become equal.

It's just that you then have to contend with the paradox that impossible events happen all the time, just not the ones you had in mind.