Analyzes A/B test results for statistical significance, sample size validation, confidence intervals, lift, guardrails, and ship/extend/stop recommendations. Handles CSV/Excel data via Python scripts.
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Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Designs, audits, and improves analytics tracking systems using Signal Quality Index for reliable, decision-ready data in marketing, product, and growth.
Enforces A/B test setup with gates for hypothesis locking, metrics definition, sample size calculation, assumptions checks, and execution readiness before implementation.
Evaluate A/B test results with statistical rigor and translate findings into clear product decisions.
You are analyzing A/B test results for $ARGUMENTS.
If the user provides data files (CSV, Excel, or analytics exports), read and analyze them directly. Generate Python scripts for statistical calculations when needed.
Understand the experiment:
Validate the test setup:
Calculate statistical significance:
If the user provides raw data, generate and run a Python script to calculate these.
Check guardrail metrics:
Interpret results:
| Outcome | Recommendation |
|---|---|
| Significant positive lift, no guardrail issues | Ship it — roll out to 100% |
| Significant positive lift, guardrail concerns | Investigate — understand trade-offs before shipping |
| Not significant, positive trend | Extend the test — need more data or larger effect |
| Not significant, flat | Stop the test — no meaningful difference detected |
| Significant negative lift | Don't ship — revert to control, analyze why |
Provide the analysis summary:
## A/B Test Results: [Test Name]
**Hypothesis**: [What we expected]
**Duration**: [X days] | **Sample**: [N control / M variant]
| Metric | Control | Variant | Lift | p-value | Significant? |
|---|---|---|---|---|---|
| [Primary] | X% | Y% | +Z% | 0.0X | Yes/No |
| [Guardrail] | ... | ... | ... | ... | ... |
**Recommendation**: [Ship / Extend / Stop / Investigate]
**Reasoning**: [Why]
**Next steps**: [What to do]
Think step by step. Save as markdown. Generate Python scripts for calculations if raw data is provided.