The above statistics are the result of dividing the Gaussian distribution of 126 with a standard deviation of 6.7 by the IQ distribution of the total population
and I’m sorry but this is complete nonsense unless you have good evidence that (even at the tails) the “elite professions” IQ distribution is close to being Gaussian with the mean and standard deviation you cite. I bet it isn’t.
To reiterate: the point is the shape of the distribution, not its mean and standard deviation. Lots of different distributions have mean 126 and standard deviation 6.5. Some of them lead to curves like the one you plotted. Some don’t.
Toy model: the “elite professions” simply reject everyone with an IQ below 120 and sample at random from those with higher IQs. This gives a mean of almost exactly 127 and a standard deviation of 6.1 -- very close to the ones you quote, and if you redid your calculation with those figures you would get an even worse “exclusion” for higher IQs. But in a world matching this model, there is no real “exclusion” at all.
So, show us your evidence that the distribution of IQs in “elite professions” is close to Gaussian or, alternatively, your evidence that your analysis still works if you use a more realistic estimate of that distribution. If you can do that, then you’ll have some evidence that people with very high IQ have less success in (or maybe less interest in) entering “elite professions”. Otherwise, not. The argument in your actual article, as it stands, is 100% hopeless, I’m afraid.
IQ tests are designed to produce a bell curve with a mean at 100 and a standard deviation of 15. That’s inherent to the definition of IQ. Actual implementations aren’t perfect, but they’re not far off.
The article says:
and I’m sorry but this is complete nonsense unless you have good evidence that (even at the tails) the “elite professions” IQ distribution is close to being Gaussian with the mean and standard deviation you cite. I bet it isn’t.
To reiterate: the point is the shape of the distribution, not its mean and standard deviation. Lots of different distributions have mean 126 and standard deviation 6.5. Some of them lead to curves like the one you plotted. Some don’t.
Toy model: the “elite professions” simply reject everyone with an IQ below 120 and sample at random from those with higher IQs. This gives a mean of almost exactly 127 and a standard deviation of 6.1 -- very close to the ones you quote, and if you redid your calculation with those figures you would get an even worse “exclusion” for higher IQs. But in a world matching this model, there is no real “exclusion” at all.
So, show us your evidence that the distribution of IQs in “elite professions” is close to Gaussian or, alternatively, your evidence that your analysis still works if you use a more realistic estimate of that distribution. If you can do that, then you’ll have some evidence that people with very high IQ have less success in (or maybe less interest in) entering “elite professions”. Otherwise, not. The argument in your actual article, as it stands, is 100% hopeless, I’m afraid.
IQ tests are designed to produce a bell curve with a mean at 100 and a standard deviation of 15. That’s inherent to the definition of IQ. Actual implementations aren’t perfect, but they’re not far off.
Sure. But that doesn’t tell us anything about the distribution of IQ among people in “elite professions”, and that’s the key question here.