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Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.
| name | ab-test-setup |
| description | Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness. |
Ensure every A/B test is valid, rigorous, and safe before a single line of code is written.
You must have:
A valid hypothesis includes:
Before designing variants or metrics, you MUST:
Ask explicitly:
“Is this the final hypothesis we are committing to for this test?”
Do NOT proceed until confirmed.
Explicitly list assumptions about:
If assumptions are weak or violated:
Choose the simplest valid test:
Default to A/B unless there is a clear reason otherwise.
Define upfront:
Estimate:
Do NOT proceed without a realistic sample size estimate.
You may proceed to implementation only if all are true:
If any item is missing, stop and resolve it.
DO:
DO NOT:
When interpreting results:
| Result | Action |
|---|---|
| Significant positive | Consider rollout |
| Significant negative | Reject variant, document learning |
| Inconclusive | Consider more traffic or bolder change |
| Guardrail failure | Do not ship, even if primary wins |
Document:
Store records in a shared, searchable location to avoid repeated failures.
Refuse to proceed if:
Explain why and recommend next steps.
A/B testing is not about proving ideas right. It is about learning the truth with confidence.
If you feel tempted to rush, simplify, or “just try it” — that is the signal to slow down and re-check the design.
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