| name | ab-test-designer |
| description | Design A/B tests with hypotheses, variants, sample sizes, and analysis plans.
TRIGGERS - Use when user wants to run A/B tests, split tests, or experiment with variations.
|
A/B Test Designer
Overview
Designs rigorous A/B tests with clear hypotheses, variant specifications, sample size calculations, and analysis plans.
Workflow
Step 1: Define the Test
- What are you testing? (page, email, ad, feature)
- Current metric: What's the baseline performance?
- Goal: What improvement are you hoping for?
- Traffic/volume: How many users/emails/impressions per day?
Step 2: Structure the Test
Output Format
# A/B Test: [Test Name]
## Hypothesis
If we [change X], then [metric Y] will [increase/decrease] by [Z%] because [reasoning].
## Test Details
- **Type**: A/B / A/B/C / Multivariate
- **Primary metric**: [what you're measuring]
- **Secondary metrics**: [supporting metrics]
- **Guardrail metrics**: [what shouldn't get worse]
## Variants
### Control (A)
[Description of current experience]
### Variant (B)
[Description of the change]
[Mockup/wireframe description if applicable]
## Sample Size & Duration
- **Baseline conversion**: [X%]
- **Minimum detectable effect**: [X%]
- **Statistical significance**: 95%
- **Required sample size**: [N per variant]
- **Estimated duration**: [X days]
## Analysis Plan
1. Wait for minimum sample size before checking
2. Check primary metric first
3. Segment analysis: [segments to check]
4. If significant: [implementation plan]
5. If not significant: [next steps]
## Risks & Considerations
- [Risk 1]: [mitigation]
- [Risk 2]: [mitigation]
Quality Checklist
