If our goal is, again, to get the single best person for the job

 Then we have what mathematicians call “full information,” and
eveiything changes. “No buildup of experience is needed
to set a standard,” as the seminal 1966 paper on the
problem put it, “and a profitable choice can sometimes be
made immediately.” In other words, if a 95th-percentile
applicant happens to be the first one we evaluate, we know
it instantly and can confidently hire her on the spot—that
is, of course, assuming we don’t think there’s a 96th-
percentile applicant in the pool.
And there’s the rub. If our goal is, again, to get the single
best person for the job, we still need to weigh the
likelihood that there’s a stronger applicant out there.
However, the fact that we have full information gives us
eveiything we need to calculate those odds directly. The
chance that our next applicant is in the 96th percentile or
higher will always be 1 in 20, for instance. Thus the
decision of whether to stop comes down entirely to how
many applicants we have left to see. Full information
means that we don’t need to look before we leap. We can
instead use the Threshold Rule.

accept an applicant if she is above a certain percentile. We
don’t need to look at an initial group of candidates to set
this threshold—but we do, however, need to be keenly
aware of how much looking remains available.

The math shows that when there are a lot of applicants
left in the pool, _you should pass up even a very good
applicant in the hopes of finding someone still better than
that—but as your options dwindle, you should be prepared
to hire anyone who’s simply better than average. It’s a
familiar, if not exactly inspiring, message: in the face of
slim pickings, lower your standards.

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About Unknown

ZAKARIA AL BAZZAR, 19 yo, university student. love everything about new tech, and I'm sharing it with you :)
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