| name | ab-test-setup |
| description | Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness. |
| category | Document Processing |
| source | antigravity |
| tags | ["ai","workflow","design","document"] |
| url | https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/ab-test-setup |
A/B Test Setup
1️⃣ Purpose & Scope
Ensure every A/B test is valid, rigorous, and safe before a single line of code is written.
- Prevents "peeking"
- Enforces statistical power
- Blocks invalid hypotheses
2️⃣ Pre-Requisites
You must have:
- A clear user problem
- Access to an analytics source
- Roughly estimated traffic volume
Hypothesis Quality Checklist
A valid hypothesis includes:
- Observation or evidence
- Single, specific change
- Directional expectation
- Defined audience
- Measurable success criteria
3️⃣ Hypothesis Lock (Hard Gate)
Before designing variants or metrics, you MUST:
- Present the final hypothesis
- Specify:
- Target audience
- Primary metric
- Expected direction of effect
- Minimum Detectable Effect (MDE)
Ask explicitly:
“Is this the final hypothesis we are committing to for this test?”
Do NOT proceed until confirmed.
4️⃣ Assumptions & Validity Check (Mandatory)
Explicitly list assumptions about:
- Traffic stability
- User independence
- Metric reliability
- Randomization quality
- External factors (seasonality, campaigns, releases)
If assumptions are weak or violated:
- Warn the user
- Recommend delaying or redesigning the test
5️⃣ Test Type Selection
Choose the simplest valid test:
- A/B Test – single change, two variants
- A/B/n Test – multiple variants, higher traffic required
- Multivariate Test (MVT) – interaction effects, very high traffic
- Split URL Test – major structural changes
Default to A/B unless there is a clear reason otherwise.
6️⃣ Metrics Definition
Primary Metric (Mandatory)
- Single metric used to evaluate success
- Directly tied to the hypothesis
- Pre-defined and frozen before launch
Secondary Metrics
- Provide context
- Explain why results occurred
- Must not override the primary metric
Guardrail Metrics
- Metrics that must not degrade
- Used to prevent harmful wins
- Trigger test stop if significantly negative
7️⃣ Sample Size & Duration
Define upfront:
- Baseline rate
- MDE
- Significance level (typically 95%)
- Statistical power (typically 80%)
Estimate:
- Required sample size per variant
- Expected test duration
Do NOT proceed without a realistic sample size estimate.
Tracking Verification (Required before Gate 8)
Before entering the Execution Readiness Gate below, run through this checklist to make "Tracking is verified" mean something concrete:
- Event firing: Trigger each event the primary and secondary metrics depend on (sign-up, add-to-cart, custom event) on staging or a debug page, and confirm it lands in your analytics destination within 30 seconds.
- Variant attribution: Verify that the variant assignment ID is attached to every fired event — not just the entry event. Use your analytics' raw event view to compare a sample of 5+ events per variant.
- De-duplication: Confirm that a user reloading the page does not cause double-counted events. If your stack uses client-side de-duping, the variant ID must be part of the dedup key.
- Sample randomization: Pull the first 100 assignment records from your assignment table; the variant split should be within ±5% of the configured allocation.
- Guardrail metric pipeline: Each guardrail metric defined in §6️⃣ must have a working dashboard or alert by the time the test launches.
If any of the above fails, stop and resolve it before Gate 8.
8️⃣ Execution Readiness Gate (Hard Stop)
You may proceed to implementation only if all are true:
- Hypothesis is locked
- Primary metric is frozen
- Sample size is calculated
- Test duration is defined
- Guardrails are set
- Tracking is verified
If any item is missing, stop and resolve it.
Running the Test
During the Test
DO:
- Monitor technical health
- Document external factors
DO NOT:
- Stop early due to “good-looking” results
- Change variants mid-test
- Add new traffic sources
- Redefine success criteria
Analyzing Results
Analysis Discipline
When interpreting results:
- Do NOT generalize beyond the tested population
- Do NOT claim causality beyond the tested change
- Do NOT override guardrail failures
- Separate statistical significance from business judgment
Interpretation Outcomes
| 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 |
Documentation & Learning
Test Record (Mandatory)
Document:
- Hypothesis
- Variants
- Metrics
- Sample size vs achieved
- Results
- Decision
- Learnings
- Follow-up ideas
Store records in a shared, searchable location to avoid repeated
