That's fair, I wasn't dimsissing the practice but rather just commenting that it's a shame the author didn't clarify their preference.
I don't think the popularity angle is a good proxy for usefulness/correction of the practice. Many factors can influence popularity.
Performance is a very fair point, I don't know enough to understand the details but I could see it being a strong argument. It is counter intuitive to move forward with calculations known to be useless, but maybe the cost of checking all calculations for validity is larger than the savings of skipping early the invalid ones.
There is a catch though. Numpy and R are very oriented to calculation pipelines, which is a very different usecase to general programming, where the side effects of undetected 'corrupt' values can be more serious.
Anyway, this topic of "error handling scoping/locality" may be the single most cross-cutting topic across CPUs, PLangs, Databases, and operating systems (I would bin Numpy/R under Plangs+Databases as they are kind of "data languages"). Consequently, opinions can be very strong (often having this sense of "Everything hinges on this!") in all directions, but rarely take a "complete" view.
If you are interested in "fundamental, not just popularity" discussions, and it sounds like you are, I feel like the database community discussions are probably the most "refined/complete" in terms of trade-offs, but that could simply be my personal exposure, and DB people tend to ignore CPU SIMD because it's such a "recent" innovation (hahaha, Seymore Cray was doing it in the 1980s for the Cray-3 Vector SuperComputer). Anyway, just trying to help. That link to the DB Null page I gave is probably a good starting point.