[beforeolives]

> they speak about how these exist to reduce false negatives

This doesn't sound right. Are you sure that they were referring to false negatives? Or is there something specific about the example problem? As far as I understand the interviewing process, the false negatives is the group the employer cares the least about. To be clear:

    True positive = good candidate, got hired

    False positive = bad candidate, got hired

    True negative = bad candidate, got rejected

   False negative = good candidate, got rejected

As the hiring company, you don't care how many false negatives you have (i.e. how many good candidates you reject) as long as you get a high number of true positives and a low number of false positives. The metric for this is called precision.

    Precision = TP/(TP+FP)

In my experience companies tend to optimise their hiring process for precision and algorithm interviews are a part of this.

[deadcore]

Sorry thank's for correcting