Anytime-valid inference

Anytime-valid inference is a way of testing whose guarantees hold at every look simultaneously, so you can peek as much as you want and still trust the answer. If you watch a metric and check it whenever you feel like it — every minute, every deploy, every time an alert nags you — ordinary statistics quietly break. Each extra look is another chance to be fooled by noise, so your false-alarm rate creeps up without you noticing — anytime-valid inference is the fix.

For someone on-call, this is the difference between an alert you can act on and one you learn to ignore. When an anytime-valid monitor stays quiet, that silence is meaningful; when it fires, the error guarantee is exactly what it was designed to be — not something you eroded by refreshing the dashboard.

Under the hood it is powered by e-processes and confidence sequences, whose validity comes from Ville's inequality rather than from a fixed sample size. That is what makes "valid no matter how often you look" a theorem instead of a hope.