Cómo usar
- Copia el comando y revisa el paquete antes de instalarlo en tu entorno de agente.
- Consulta las herramientas, credenciales y entornos necesarios en el documento original.
- Describe las entradas y el resultado deseado; contrasta la salida con las fuentes.
npx skills add https://github.com/phuryn/pm-skills --skill ab-test-analysisAntes de empezar
- La instalación no incluye suscripción al modelo, cuota API ni permisos sobre tus archivos.
- Las estrellas e instalaciones reflejan actividad, no una prueba independiente de calidad o seguridad.
Documento original del Skill
Contenido del repositorio de origen con títulos, ejemplos, código, tablas, enlaces e imágenes preservados.
A/B Test Analysis
Evaluate A/B test results with statistical rigor and translate findings into clear product decisions.
Context
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.
Instructions
- Understand the experiment:
- What was the hypothesis?
- What was changed (the variant)?
- What is the primary metric? Any guardrail metrics?
- How long did the test run?
- What is the traffic split?
- Validate the test setup:
- Sample size: Is the sample large enough for the expected effect size?
- Use the formula: n = (Z²α/2 × 2 × p × (1-p)) / MDE²
- Flag if the test is underpowered (<80% power)
- Duration: Did the test run for at least 1-2 full business cycles?
- Randomization: Any evidence of sample ratio mismatch (SRM)?
- Novelty/primacy effects: Was there enough time to wash out initial behavior changes?
- Calculate statistical significance:
- Conversion rate for control and variant
- Relative lift: (variant - control) / control × 100
- p-value: Using a two-tailed z-test or chi-squared test
- Confidence interval: 95% CI for the difference
- Statistical significance: Is p < 0.05?
- Practical significance: Is the lift meaningful for the business?
If the user provides raw data, generate and run a Python script to calculate these.
- Check guardrail metrics:
- Did any guardrail metrics (revenue, engagement, page load time) degrade?
- A winning primary metric with degraded guardrails may not be a true win
- 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.
