> Counterintuitively, if you pass on too many of the "good" candidates, in your overzealousness to reject bad candidates, you're actually INCREASING THE ODDS OF A BAD HIRE.
Yes, but what hiring managers are doing isn't passing over candidates that they know are good, they're passing over candidates that they're not very sure about.
> Because "good" candidates are rare, every time you pass on a good candidate that increases your odds of making a bad hire! This is simple Bayesian reasoning!
Similarly, of course this is true, but again hiring managers aren't passing up candidates that they know to be good, they're passing up on candidates that they're not sure about, whom the acknowledge could be good.
Under certain conditions, every time you pass on a candidate you're not sure about, you decrease your chance of making a bad hire. The conditions are that increasing hiring standards must weed out more bad candidates than good candidates.
Let's work with your model. Suppose 1% of candidates coming for an interview are good and 99% are bad.
Hiring strategy A manages to hire all good candidates it interviews, and 1% of the bad candidates it interviews. End result: for every 100 people you interview, you get 1 good candidate and 1 bad candidate.
Hiring strategy B is more conservative and hires 50% of all good candidates it interviews, and 0.1% of the bad candidates it interviews. End result: for every 100 people you interview, you get 0.5 good candidates and 0.1 bad candidates.
The claim is something like, the team produced by strategy B is better than the team produced by strategy A, even though team B has less good candidates than team B.
I agree with you that if hiring strategy B led to hiring 10% of the good candidates and cut the odds of hiring the bad candidate to 0.5%, compared to 100% and 1% for hiring strategy A, then strategy B is worse than strategy A. However
1) This means that by raising your hiring bar (adopting B instead of A), you eliminate more good candidates than bad candidates. You now have to prove this empirical claim.
2) All I wanted to say was that the statements you made like "every time you pass on a good candidate that increases your odds of making a bad hire!" is only true under certain conditions (namely that raising hiring standards eliminates more good candidates than bad candidates), and it's not "simple bayesian reasoning".
The original slogan "It is better to reject a good candidate than hire a bad candidate." specifically says to make that fallacy.
As long as good candidates are rare, and bad candidates have a small-but-nonzero chance of tricking you into hiring them, it's very expensive to pass on a good candidate.
Even if you pass on 50% of the good candidates (no matter how many bad ones trick you), you're doubling the amount of time you're spending interviewing.
The only way to be sure about how many "good" candidates your are missing is to hire some random percentage of the people who fail your interview. Only a large tech company would have the resources to do that, and I'm not sure if it would be ethical, or even get the employer in legal trouble.
NO employer knows how many good candidates they miss out on, which makes this analysis very difficult.
Also, I question the ability of most employers to evaluate employees AFTER they are hired, so any analysis of post-interview performance might just be reinforcing whatever biases are present.
Also, candidates fall into THREE groups: great, mediocre, and toxic. The catch is that the toxic ones are most likely to trick you into thinking they're great. That makes it even more expensive to pass on a good candidate.
> As long as good candidates are rare, and bad candidates have a small-but-nonzero chance of tricking you into hiring them, it's very expensive to pass on a good candidate.
Yes, I agree with you. My point is that even taking this into account, even after taking into account all the other costs you mention into account, it might still be better to pass on candidates you're not sure about.
> The original slogan "It is better to reject a good candidate than hire a bad candidate." specifically says to make that fallacy.
So going by the slogan, even if passing on too many good candidates increases your odds of a bad hire (say from 50% to 80%), it's still better than hiring a bad candidate, since if you hire a bad candidate, the odds that you made a bad hire is 100%!
My interpretation of the slogan seems different from yours, mine is something like: if you hire a good person the value of your business will increase by X, if you hire a bad person the value of your business will decrease by Y, and Y is much greater than X.
Yes, but what hiring managers are doing isn't passing over candidates that they know are good, they're passing over candidates that they're not very sure about.
> Because "good" candidates are rare, every time you pass on a good candidate that increases your odds of making a bad hire! This is simple Bayesian reasoning!
Similarly, of course this is true, but again hiring managers aren't passing up candidates that they know to be good, they're passing up on candidates that they're not sure about, whom the acknowledge could be good.
Under certain conditions, every time you pass on a candidate you're not sure about, you decrease your chance of making a bad hire. The conditions are that increasing hiring standards must weed out more bad candidates than good candidates.
Let's work with your model. Suppose 1% of candidates coming for an interview are good and 99% are bad.
Hiring strategy A manages to hire all good candidates it interviews, and 1% of the bad candidates it interviews. End result: for every 100 people you interview, you get 1 good candidate and 1 bad candidate.
Hiring strategy B is more conservative and hires 50% of all good candidates it interviews, and 0.1% of the bad candidates it interviews. End result: for every 100 people you interview, you get 0.5 good candidates and 0.1 bad candidates.
The claim is something like, the team produced by strategy B is better than the team produced by strategy A, even though team B has less good candidates than team B.