I'm not a surgeon myself, but when I was in medical school the program director of our local general surgery residency told me that in terms of hand skills 90% of surgeons are more or less average, 10% are masters, and 10% are horrific. (So basically a bell curve with very thin tails.) He also said the correlation between test scores and surgical hand skills was pretty week.
How much of surgery is based on dexterity vs knowledge/attention-to-detail? I sort of assumed that most operations are basic plumbing (A connects to B) while there are a few specialized domains that require exquisite deftness.
The question is too general. Depends a lot on the kind of surgery you're doing. I guess the answer you're looking for is that anyone could be a surgeon, but not for all kinds of surgery. Also 'basic plumbing' with no room for error is not an easy thing at all.
It's more in that if you improperly connect a to b someone dies, and it's not just a snap fit like a pipe. You need to do things like suture two blood vessels together, using pliers (not the technical term), inside a dark box lit up by a tube, while looking at an upside down TV image of what's going on.
I'm just relaying what my friend who is studying to become a doctor told me, but by his account there's a wealth of techniques for each procedure or even parts of it, like tying up the dangling bits after kidney removal.
Ultimately it boils down to what a given surgeon practiced in their career.
I don't think that "Bell curve" should be interpreted strictly as a Gaussian in this context, but more literally as a curve with a shape resembling a bell.
Kurtosis[0] is a term I came across when dealing with random vibration analysis, but I understand very little if it. The idea of "moments"[1] of various orders to describe a shape of distribution is interesting in general. It sounds analogous to Fourier series, describing a shape/graph with a series of values.
> Excess kurtosis, typically compared to a value of 0, characterizes the “tailedness” of a distribution. A univariate normal distribution has an excess kurtosis of 0. Negative excess kurtosis indicates a platykurtic distribution, which doesn’t necessarily have a flat top but produces fewer or less extreme outliers than the normal distribution. For instance, the uniform distribution (ie one that is uniformly finite over some bound and zero elsewhere) is platykurtic. On the other hand, positive excess kurtosis signifies a leptokurtic distribution. The Laplace distribution, for example, has tails that decay more slowly than a Gaussian, resulting in more outliers.
(Emphasis added.)
You can't have a bell curve with thin tails, because a bell curve has standard tails by definition.
