Design a framework for real-time monitoring of A/B test results.
Instruction: Explain the components of the system, including data collection, analysis, and decision-making processes.
Updated
Example Answer
I would build the monitoring framework around assignment integrity, safety, and a prespecified decision policy. The dashboard should show exposure counts, sample-ratio mismatch, logging failures, latency, and business guardrails alongside the primary effect estimate and uncertainty. I would define the unit of randomization, metric windows, exclusions, and ownership before launch.
I would separate operational monitoring from the statistical rule for declaring a winner. If we use a fixed-horizon test, repeatedly checking an ordinary p-value and stopping at the first value below 0.05 inflates false positives. For ongoing decisions, I would use a valid sequential method with its assumptions, or a planned analysis schedule with appropriate error control. A serious safety issue can still justify stopping, but I would record that reason rather than call it proof of a winning variant. Metric and hypothesis changes need a documented new analysis plan.
Worked example
A launch checklist should name the primary metric, minimum worthwhile effect, maximum duration, sequential or fixed-horizon rule, guardrail stop thresholds, and the person authorized to stop the test.
Why this works
The dashboard becomes actionable without turning unrestricted peeking into an unacknowledged inference procedure.