false positive vs. false negative

Recently saw this great post on Slashdot by an anonymous author:

To Google, hiring is mathematically equivalent to Information Retrieval, except that they only care about "precision" not "recall".

What that means to lay-people is that so long as they can maintain 10,000 applications coming through per-month, false negatives (passing on a suitable applicant) do not matter because there'll be another candidate along in a minute. False positives (hiring an unsuitable applicant) are all they need to focus on. The "fit factor" is effectively the search string of traits; however, with such a large candidate pool, they can focus their "hiring algorithm" entirely on rejecting candidates where it is even slightly difficult to ascertain whether they fit or not.

So, their advertising blitz "aren't we a great place to work for" is a part of what lets them keep their hiring process easy. If they get bad PR and applications fall, then they'll need to worry about recall as well as precision.

Also, read Two Kinds of Judgement, which discusses this issue in some depth.