Another way to think about it is the often-cited unit n-dimensional sphere. If you were to uniformly sample points from within this n-dimensional sphere, as n increased, the proportion of points lying near the surface of the sphere would increase.
Second the correlation is less significant than your assuming. My legs are the same length as some people a full foot shorter than I am.
Yes, the correlation is not as strong as I would assuming -- that was really the point of my comment. You are a sample size of one, so your anecdote doesn't mean much. However, based on this work, apparently almost everyone has a similar anecdote: after normalizing for height, there is another common dimension which is "unusually" large or small.
The slope of a bell curve near it’s center is almost flat. This means you end up with a fairly uniform distribution when looking at values near the median. Which makes outliers within that range more common than intuition suggests.
In layman's term. It's so narrow that there are more people 1 inch off than there are people within the expected height. It's crazy.
Probability is talking in terms of standard deviations nowadays. They are selecting less than half a standard deviation, it's hyper selective. I'm curious how many people would fit the norm if the study was looking at 1 standard deviation. Surely a lot more.
For reference. Selecting the 30% on six metrics is keeping less than 0.01% of participants. Selecting the 68% (one deviation) on six metrics is keeping 10% of participants. It's night and day. Should be even more in practice because measurements are correlated.
Just to add to add to the mathematical intuition here (please correct me if I'm wrong): if you're thinking of it as a unit line/square/cube then total n-dim area is 1^n, and the portion in the 50% range is (1/2)^n, where n = number of dimensions. So that should simplify to 2^(-n).
Note this works out to 10th dimension as 0.09765% or 0.1 person per 1000.
ETA: As one of the comments below points out, you can also model it as a binomial distribution.
Probability of getting all heads, given p=0.5 is (n!)/((n-k)!k!) / 2^n. Since n=k since we're looking to get all heads at all times, this also simplifies to 2^(-n).
But interestingly, every relevant comment here either got something wrong about the final frequency or percentage, or corrected the wrong thing. (Parent comment, grandparent, 2 aunts and 1 cousin.)
0.5^10 = 2^-10 = 1/(2^10) = 1/1024 [i.e. exactly 1 person per 1024] ~= 0.00097656 ~= 0.09766 %.
Beyond that, while there's no accounting for taste, I find it to be an appallingly bad article. It's poor man's Malcolm Gladwell.
It is very common for engineering projects to encounter problems caused by an issue which was understood but was not identified. Most engineering failures fall into this category. Humans simply make mistakes.