It's another variation on "Looking for the watch where the light is better."
These situations aren't at all symmetric. A false positive always costs you (the company). A false negative is only a cost to you if you fail to hire well for that position.
Also the fact that a candidate you passed on does well elsewhere doesn't mean you were wrong to pass on them (it could, but it's not clear).
That applies in the case of caring more about false positives than false negatives, since nobody (or close enough) even knows how many false negatives they have.
Your statement that someone being successful elsewhere doesn't prove we have a false negative underscores the uncertainty involved. It doesn't invalidate what I'm saying in the least.
But if we want to be rigorous, we could do some direct testing. Let's say we give a telephone screening and four interviews to each candidate. Simpkifying somewhat, let's say that our policy is that a fail on any one eliminates the candidate.
If we hire lots of people, we can collect false positive and false negative stats by randomly passing someone who fails exactly one test.
If a lot of people who fail a particular test end up succeeding, we can infer that the test provides false negatives.
On the other hand, if everyone who gets the random "free pass" on one particular test ends up failing, we can presume it has low false negatives, and maybe abandon the random passes for that test.
The original joke/anectdote works because it leads to clearly suboptimal search - the guy knows his watch isn't near the streetlight, but he's looking there because he can see.
This is a very different situation. To abuse the analogy further, the guys best bet to find a watch seems to be just looking near the streetlight. After all, not only can you see better there, but most of the watches are near there too.
Your saying "false negative is only a cost to you if you fail to hire well for that position" could be easily disproved with examples of people, who were rejected and started their own competitive business (e.g. [1]). IMHO, one of the goals of having 10Ks of engineers on staff is to keep them from the competition. But since nobody is tracking this and the extreme cases like WhatsApp can be hand-waved away it's easy to pretend that the false negative's cost is miniscule.
The guy with the watch analogy only works if there is good reason to believe he’d be better off searching elsewhere. But we don’t appear to have that here....
Let's be generous and say the WhatsApp was a 10x unicorn so FB overpayed only 9B for it. Which makes 4.5B loss per false negative on each founder. If a false positive was, on average, 1M loss (which is an extremely generous in an organization such as FB in my recollection), it means you could have had 4000 false positives for each of these negatives and still come ahead.
And, it brings us back to the Type I and II errors - it's always cheaper to make one, which has a limited downside than the unknown (potentially very high one). Typical example given is that while you are on the way to an expensive cruise you realize that you might have forgotten to turn off the stove. If you go back you might miss your 10K cruise. If you cary on, you might lose you 1M house. On average, people who miss cruise in such circumstances fare much better than ones, who risk their houses.
I get what you guys are saying, there just doesn’t seem to be real evidence that is an issue nearly all cases. Whatsapp notwithstanding
So in order for it to make sense to spend that money, you want some evidence there will be a positive return. Far from contradicting my position, what limited data I have seen does not support that effort, at least in the general case. Certainly whatsapp and the like don’t speak to it - they are very much outliers.
By all means try it. If your false positive rate explodes it will be an expensive lesson, but that’s always a risk.
One last point, the idea that nobody thinks about this and try’s thinks is laughable , which is worth noting because some comments in this thread almost suggest that it’s true,.
With my example of sacrificing a 10K cruise for a chance to not lose a 1M home, I figure?
> Far from contradicting my position, what limited data I have seen does not support that effort, at least in the general case.
Indeed, the data does not contradict a statement "on average, false positives are 4.5B each", which nobody has argued. However, it does contradict the statement "false negatives have any cost only when you are unable to fill the position as the result", which you have made in this thread.
>One last point, the idea that nobody thinks about this and try’s thinks is laughable , which is worth noting because some comments in this thread almost suggest that it’s true,.
Again, it would be silly to say that nobody thinks about it. Evidently, a number of people think the same way you do.
At first, yes. But when one has had enough experience looking for scarce commodities (of whatever kind: genuinely attractive apartments, interesting music / restaurants, romantic partners, etc) one learns that the "where the light is" search technique only gets you so far.
Certainly in this scenario - the false positive rate is only important if the true positive rate is too low. It's not at all clear that is a factor.
That's the whole point -- in order to find evidence that it isn't working, you have to start looking at the false negatives --
or that is to say, where the "light is not".
LinkedIn could offer this as a service, if there was sufficient demand that they could monetise it. Every company will routinely check out every candidate’s profile anyway. They just need an automated way to check again in 6 months and see if everyone they reject is going to a competitor who is beating them in the market.
The problem is that very few companies know how much false negatives are costing them, so it is very difficult to evaluate whether the false negatives are—cough—a negligible tradeoff against weeding out the false positives.
False positives are right in your face. But it is nearly impossible to systematically keep track of the candidates that aren't hired to see if they are successful elsewhere.
So people lean towards eliminating false positives at all costs. It's another variation on "Looking for the watch where the light is better."