Interviewing is a noisy prediction problem
https://erikbern.com/2018/05/02/interviewing-is-a-noisy-prediction-problem.html
Over time I’ve come to the (slightly disappointing) realization that knowing who’s going to be good at their job is an extremely hard problem. The correlation between who did really well in the interview process and who performs really well at work is really weak.
Given that you have limited time to measure, you need spend your time measuring things that have high signal-to-noise ratio and things that have low correlation with each other.
Let’s start by stating the problem. We’re trying to predict job performance from a series of measurements (interviews). Those measurements are noisy meaning that any individual measurement is not very predictive in itself, but hopefully all of them taken in aggregate can be predictive. We can also choose what we want to observe ahead of time, by coming up with an interview process where we think the aggregate judgement correlates the most with job performance. So we can choose to spend one hour on system design, one hour on algorithms, etc.
Bad interview signals
Good interview signals
Combining measurements
Sanity checking your interview process: would your best engineers do well?