The A/B Test Experiment Lifecycle: 6 Stages, Step by Step

A disciplined, six-stage process that takes an experiment from initial idea through analysis and knowledge sharing, ensuring rigor at every step.

Proposal & Peer Review

Document your hypothesis, metric framework (primary, secondary, guardrails), target audience, and expected duration. Requires peer sign-off from at least one PM and one engineering lead before kickoff.

Technical Setup & QA

Implement the treatment variants, verify that user bucketing (random assignment) works correctly, and confirm metric logging is accurate. Run a 24–48 hour smoke test at 5% traffic to catch issues early.

Gradual Ramp-Up

Begin exposing 10–20% of eligible traffic to the experiment. After 48 hours with no guardrail violations, increase to a full 50/50 split. Resist the urge to check the primary metric during this phase.

Monitoring & Observation Period

Let the experiment run for the pre-determined duration without interference. Monitor guardrail metrics daily and document any external events (marketing campaigns, outages, holidays) that could contaminate results.

Analysis & Ship Decision

Evaluate the primary metric for both statistical significance (is the result real?) and practical significance (is the effect large enough to matter?). Make a clear decision: Ship, Iterate, or Revert.

Documentation & Knowledge Sharing

Record the full results, methodology, and learnings regardless of outcome. Null and negative results are just as valuable as wins. They prevent other teams from repeating the same experiment and inform future product strategy.

Common pitfall

Never stop an experiment early just because the results look positive. This is called "peeking" and it dramatically inflates your false positive rate (from 5% to as high as 25%). Only stop early if you are using a sequential testing framework specifically designed for interim analyses.