Candidate selection, 37%, and the Optimal Stopping Theory

You may have heard of the "secretary problem" which is a famous math problem which uses optimal stopping theory. The problem has been studied extensively.

It goes like this: Imagine a manager who wants to hire the best secretary from a large number of applicants.

The applicants are interviewed in random order, and for the sake of providing a positive experience, a decision about each applicant is made immediately following each interview, and once rejected, applicants are not recalled.

During each interview, the manager can rank the applicant among all applicants interviewed so far, but is unaware of the quality of yet unseen applicants.

The problem comes down to this -- when is the optimal time to stop and make an offer to a candidate?

I recently watched a video with a twist on this problem, which really helped me understand the theory.

"Spoiler alert" - when all the complex math is done, the answer with the highest probability of success, is to reject the first 37%, then offer the position to the first candidate you come to next, who is better than the first 37%.

Ever since watching this video, I've wondered if Optimal Stopping Theory and the Secretary Problem has real world relevance to Talent Acquisition, in particular for high volume recruitment, when selecting candidates from evergreen expression of interest or very high ad response?

If there are any experts out there with an opinion, please add it to the comments.