[EDIT: I had some other criticisms that were incorrect and overly harsh anyway. I've removed them.]
Also, to defend myself a little: I think I'm at least being responsible by in no way claiming that it's statistically valid, and in fact making the point that it's not, and citing the reasons why, several times in the article.
I do stand by my point, however, that this helps the public understand the danger of metadata for data-mining, as well as introducing them to the pitfalls of statistics.
The most important thing is getting an estimate of how good your averages are. Try to modify a couple of things, for example how do the 67% change with sample size (verification)? How does the number turn out if you feed it biased data, e.g. only male phone numbers (falsification)?
The goal is to use this as a hook to get the public interested, then take them for the ride as I learn. That's why I tried to be really cautious about pointing out all the reasons why this particular model is bad.
As is the case with any statistical data, a single value holds only very little information. The article does not do any analysis on how good these 67% are: could it be that changing the parameters changes it to 50%? There's a pretty large configuration space in question, and therefore I think giving out a simple number is very misleading.
What I also don't agree with is the conclusion, "Most importantly, if that's what I can do with a limited set of my own data, imagine what the NSA can do with the datasets it has access to." In the light of the previous paragraph, this draws the conclusion that the NSA can do pretty big things, but it's based on the wrong premise that the analysis of the "own data" has any statistical significance.
Note that I don't want to argue about the conclusion - data mining is very powerful, and so is statistics - but this kind of approach is very easily abused, be it intentionally or unintentionally.
So, in short: be careful trusting statistics, even when you really really like the result.