The regret metric is different for a false "no" compared to a false "yes." I'd rather say "no" to someone who could've been great than "yes" to someone who wasn't, so I would say that it isn't that important.

Depends a lot on the company and situation (how easy is it to fire a bad hire? Does the company do it?) - but you can use something like F2 or F0.5 where relevant.

Remember Facebook rejected Brian Acton, and paid billions for that. An oversimplification, I agree, but the larger point is that people tend to dismiss too easily the impact of bad “no hire” decisions, compared to the bad “hire” decisions. Just because you can’t or don’t measure it, doesn’t mean that the impact may not be arbitrarily large.

This comment boils down precisely what I’ve always thought is the key problem in tech hiring. Companies overestimate the cost of a bad hire and underestimate the benefit of taking a risk and having it pay off.

To borrow from poker: it’s commonly understood that if you’re not “caught bluffing” at least a little, it means you’re not bluffing enough. If your hiring process results in zero bad hires, you are playing it way too safe and I guarantee you are missing out on phenomenal candidates.

I think you're wrong. I think companies are completely correct in assessing the cost of a bad hire to be much greater than the loss of a good hire, assuming you're getting enough good hires to fill the positions available.

Now, this assumes that you're not missing out on great hires; in other words, it assumes that the good hires you miss are at the bottom of your hiring range. Your point might be that this can't be guaranteed, and while that may be true, it's unlikely that any tweaks to a given hiring process are going to bring them in either.