EDIT2: OK folks we're smart, let's use MATH.
Take above quote, which compares "1.9% of Arnolds are accountants" to the "0.55% of Shanes [are accountants]". They're implying that the probability of being an accountant (J), given that ones name (N) is Arnold, is above the expected probability of being an accountant in general. So they're looking for a high P[J|N]/P[J].
Now compare with what we were expecting to see. We assumed the chart showed, for a given job, names which had a higher incidence than normal. i.e., we're looking for a high P[N|J]/P[N].
Guess what. P[J|N]/P[J]=P[N|J]/P[N] by Bayes' Theorem [1]. These are EXACTLY the same metric! So their technique, and the chart, is correct. (And my original post, below, was wrong.)
(Not saying anything about causation here, and I don't think they were either.)
[1] http://en.wikipedia.org/wiki/Bayes%27_theorem
-----------
Yes, that is completely backward. 99% of Arnolds could go into farming, yet still the 1% who go into accounting dominate that field, and hence show up on this chart.
EDIT: but you missed the first half of that quote: "In our sample of two and a half million people, a whopping 1.9% of Arnolds are accountants. Contrast that with just 0.55% of Shanes." So I think the quote is correct. Makes me wonder if their chart is backward. (i.e., they put "Arnold" under "Accountant" because Arnolds are likely to be accountants, not because accountants are likely to be Arnolds, as the grouping implies).