This should be straightforwardly testable by standard statistics
Agreed.
That may require prohibitively large sample sizes, i.e. not be testable.
With regards to measuring g, and high IQs, you need to keep in mind regression towards the mean, which becomes fairly huge at the high range, even for fairly strongly correlated variables.
Other more subtle issue is that proxies generally fare even worse far from the mean than you'd expect from regression alone. I.e. if you use grip strength as a proxy for how quick someone runs a mile, that'll obviously work great for your average person, but at the very high range - professional athletes - you could obtain negative correlation because athletes with super strong grip - weightlifters maybe? - aren't very good runners, and very good runners do not have extreme grip strength. It's not very surprising that folks like Chris Langan are at very best mediocre crackpots rather than super-Einsteins.
That may require prohibitively large sample sizes, i.e. not be testable.
At least for certain populations the sample sizes should be pretty large. Also a smaller-than-desired sample size doesn't mean it's not testable, all it means is that your confidence in the outcome will be lower.
proxies generally fare even worse far from the mean than you'd expect from regression alone
Yes, I agree. The tails are a problem in general, estimation in the tails gets very fuzzy very quickly.
I've been wondering how useful it is for the typical academically strong high schooler to learn math deeply. Here by "learn deeply" I mean "understanding the concepts and their interrelations" as opposed to learning narrow technical procedures exclusively.
My experience learning math deeply
When I started high school, I wasn't interested in math and I wasn't good at my math coursework. I even got a D in high school geometry, and had to repeat a semester of math.
I subsequently became interested in chemistry, and I thought that I might become a chemist, and so figured that I should learn math better. During my junior year of high school, I supplemented the classes that I was taking by studying calculus on my own, and auditing a course on analytic geometry. I also took physics concurrently.
Through my studies, I started seeing the same concepts over and over again in different contexts, and I became versatile with them, capable of fluently applying them in conjunction with one another. This awakened a new sense of awareness in me, of the type that Bill Thurston described in his essay Mathematics Education:
I understood the physical world, the human world, and myself in a way that I had never before. Reality seemed full of limitless possibilities. Those months were the happiest of my life to date.
More prosaically, my academic performance improved a lot, and I found it much easier to understand technical content (physics, economics, statistics etc.) ever after.
So in my own case, learning math deeply had very high returns.
How generalizable is this?
I have an intuition that many other people would benefit a great deal from learning math deeply, but I know that I'm unusual, and I'm aware of the human tendency to implicitly assume that others are similar to us. So I would like to test my beliefs by soliciting feedback from others.
Some ways in which learning math deeply can help are:
Some arguments against learning math deeply being useful are:
I'd be grateful to anyone who's able to expand on these three considerations, or who offers additional considerations against the utility of learning math deeply. I would also be interested in any anecdotal evidence about benefits (or lack thereof) that readers have received from learning math deeply.