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Comment by clintonc | Hacker News Reader

That last bit seems to be the Crux of why this is so surprising -- being in the middle 50% on some dimension correlates positively with being in the middle 50% on the other dimensions, rather than each dimension being independent as in your calculation. It's difficult for me to reconcile that, for the 400 in the study within 5% of average height, each was a standard deviation away from the mean in some other measurement.

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It’s two different effects. First being within a range is not the same as being at the center of the range. Someone 4% above average height should have ~50% of their dimensions larger than that. Similarly someone at 4% below average should have around 50% of their dimensions below that. At best someone in the exact middle of the range only has so much buffer to work with.

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.

Every time I get my height measured by a new doctor, they feel the need to tell me that my legs are short for my height.

I don't recall ever having my height measured. Is that a U.S. doctor thing?

Visits often start with a basic vitals check, but as an adult they've been content to simply ask my height, while measuring my weight.

Sure, but being in the middle 10 percentile in height(or some other dimension) would serve to "normalize" the sample; so 400 people are close to the center of the larger range. Despite being close to the middle of the range, some dimension is far from the center of it's range.

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.

> would serve to "normalize" the sample

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.

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