statistics

Everyone quotes p95 in latency SLOs, but few can compute it without a library and fewer can explain why the index is ceil(p*n)-1, not p*n. The warm-up that screens whether you actually understand the metric you live by.

Most working data scientists state the definition wrong, and interviewers know exactly which wrong versions to listen for. The correct one-sentence definition, the VP translation, and the three classic traps.

Daily peeking can quietly triple your false-positive rate, and almost every customer does it. The math of why, the multiple-comparisons cousin, and how to deliver the bad news without losing the room.

Averaging two scalar scores tells you almost nothing. Blind pairwise battles plus a win-rate confidence interval do, and the result is only trustworthy once you've controlled position bias, verbosity bias, and rater fatigue.

The regression with no smoking gun, no deploy to blame, no single bad night. Staff-level triage is about reading the trend, not the last run, and the four slow-burn causes that a one-night comparison can't see.

Everyone names PSI and KS; almost nobody knows why the KS p-value betrays you at production scale. The thresholds that are actually industry convention, the response ladder behind them, and the trap answer interviewers bait on purpose.