Guardrail metrics
Metrics that must not get meaningfully worse (errors, latency, complaints, refunds) — judged by whether the interval rules out harm larger than a pre-agreed tolerance, not by a non-significant p-value.
- one-sided upper confidence bound for the increase
- largest increase in the harmful rate the business is willing to tolerate (set before the test)
- observed rates of the harmful event in control and treatment
When to use
- Every A/B test: alongside the primary (decision) metric, list a few guardrails that protect users and the business — error rates, page load time, cancellations, support contacts — plus data-quality guardrails such as sample ratio mismatch.
Assumptions
- Each guardrail has a direction ("higher is worse") and a tolerance δ fixed in advance.
- Same randomisation and independence assumptions as the primary analysis.
How to interpret
- Pass: the confidence bound rules out harm larger than δ.
- Fail: the estimate itself shows harm beyond δ, or a clear significant deterioration.
- Inconclusive: the bound includes harm beyond δ though the estimate is small — the test was not large enough to show safety.
- With several guardrails, consider a multiple-comparison adjustment or treat them as alarms to investigate rather than automatic vetoes.
Common mistakes
- Treating "no significant change" in a guardrail as proof of no harm (absence of evidence is not evidence of absence).
- Choosing δ after seeing the data.
- Letting a big win on the primary metric excuse an uninvestigated guardrail failure.
Example
Support-contact rate: A 300/10 000 (3.0%), B 330/10 000 (3.3%); tolerance δ = +0.5 points. The two-sided test gives p = 0.22 (not significant), yet the one-sided 95% upper bound for B − A is +0.71 points, above δ. Harm larger than the tolerance is not ruled out: the guardrail is inconclusive, not passed.