A/B Test
An A/B Test randomly assigns eligible people or items to versions. It compares a chosen measure to estimate cause and effect.
Another example
A service tests renewal reminder timing. It checks opt-outs and support issues, then waits for the planned sample.
Main parts
- 01Testable hypothesis
- 02Variant and exposure
- 03Metric and decision rule
Use it when
Use it when random assignment is fair, exposure is measurable, and one main measure has safety checks.
Do not use it when
Do not peek for a desired result, ignore multiple-metric correction, mix groups, or test unsafe outcomes.
Name used in code
random assignment + exposure + metric + guardrails Before you ship
Check the target user, wanted result, current baseline, evidence source, scope, owner, and success level. List assumptions, open risks, and the next test or decision.
Outcome: Use A/B Test to make a focused product decision backed by evidence. User context: A service tests renewal reminder timing. It checks opt-outs and support issues, then waits for the planned sample. Product concept to apply: A/B Test. Why it fits: Use it when random assignment is fair, exposure is measurable, and one main measure has safety checks. Do not use it when: Do not peek for a desired result, ignore multiple-metric correction, mix groups, or test unsafe outcomes. Product framing: Name the target user and wanted result. Record the current baseline and evidence. Set what is in and out of scope, the limits, the owner, and the time frame. Decision quality: Keep facts, guesses, and unknowns separate. Record the options you did not choose and what evidence would change the decision. Acceptance criteria: The document names an owner, target user, measurable result, scope limit, evidence source, open risks, and next test. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Target group, baseline, success level, owner, time frame, dependencies, and review schedule.
Check this request
Review the current use of A/B Test. Definition: An A/B Test randomly assigns eligible people or items to versions. It compares a chosen measure to estimate cause and effect. Release checks: Check the target user, wanted result, current baseline, evidence source, scope, owner, and success level. List assumptions, open risks, and the next test or decision. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.