A better way would be to make the criticisms more concrete.

What isn't "concrete" about it? I think the whole article is an exercise in stating the obvious, to those who have had basic education in statistics. Stricter correlations tend to be more linear. A broader spectrum of data points is pretty much by definition "fatter". I don't see how this is actually very instructive. And to be honest, I don't see how I could be much more specific.

Where, and what did they say? We cannot know what better resources you know of unless you tell us.

You mean you've never had a statistics class? Honestly? I'm not trying to be snide, just asking.

Extreme data points are often called "outliers" for a reason. Since (again, almost -- but not quite -- by definition, it depends on circumstances) they do not generally show as strong a correlation, "other factors may weigh more". This is a not a revelation. I don't disagree with it, I'm simply saying it's rather elementary logic.

Which brings us back to the main point I was making: I did not feel this was particularly instructive.

Besides, you're talking there about something you previously called "just wrong".

Wrong in the sense that I don't see any actual demonstrated relationship between his ellipses and the data, except for simple, rather intuitive observation. It's merely an illustrative tool. More specifically:

So this offers an explanation why divergence at the tails is ubiquitous. Providing the sample size is largeish, and the correlation not to tight (the tighter the correlation, the larger the sample size required), one will observe the ellipses with the bulging sides of the distribution (2).

This is an incorrect statement. What he is offering is a way to describe how data at the extreme ends may vary from correlation. Not "why". There is nothing here establishing causation.

If we are to be "less wrong", then we should endeavor to not make confused comments like that.