Fair point, but showing these graphs complete with the outliers gives a very poor platform to base handing out algebra tests in interviews without establishing causality.
I think the point being made is that there are probably predictors which have many orders of magnitude more predictive power (e.g. work experience, education, interview questions, programming exercise, available source code, etc.).
The male gender is probably correlated to programming ability as well due to the fact that there are more male programmers but it's not a very useful predictor.
First, it was suggested that this test should be used for programming job interviews. Second, why would a school who teaches programming care if students had programming ability? If you answer it is because the school wants to maintain a reputation of producing good programmers (hence adding value to the students' resumes), it contradicts your statement that this test is a better predictor than a resume. Otherwise, people would just study algebra instead of going to that school.
The author uses it as a predictor for acceptance at a school.
For the way, he was around the thread explaining that he can train a limited number of people, and that some of them not learning is wasteful and uninspiring.
> A high correlation corresponds to predictive power...
That's a bold statement. And one that is unfortunately often times false. Spurious correlations pop up everywhere. They can be notoriously bad at predicting things, and care must be taken not to take rash actions based on correlations that may have no actual clout. I'd say denying someone a job based off of an algebra test is sufficiently rash.
Why do you think those correlations don't have predictive power, eg, that if you give me a graph of the pirate rate over a long period of time, I can't make a pretty good guess about the global warming rate?
Now, obviously, these are only very coarsely correlated because they don't reflect small scale variations in either data set (which is why the graphs are so coarse). However, in this case, both seem to actually be driven by a hidden factor (technology and societal advancement), which means that they'll actually likely stay correlated (as long as the correlations with the hidden factor stay strong).
Similarly, internet explorer and murder rates.
tl;dr: Your articles are correct that correlation doesn't imply causation, but I never argued that increasing math or computer science education would improve scores in the other. Instead, I merely argued that as long as they're correlated, knowing about one will tell you about the other.
(Also, the examples you picked don't meet my definition of strongly correlated, because they don't mimic fine features of the data, but that's actually secondary, because the argument you presented doesn't even relate to what I claimed.)
Why?
It probably shouldn't be your only criterion, but if it has a high correlation, why wouldn't that be sufficient for handing out algebra tests?
A high correlation corresponds to predictive power, even if it's not a causal relationship.