AB Test Setup

Plan A/B tests with a clear hypothesis, defined metrics, variant design, sample size, duration, and statistical significance guidelines.

A/B Test Setup Skill

Trigger

Plan A/B tests with proper methodology — hypothesis, sample size, duration, variant design, statistical significance.

Trigger phrases: "A/B test", "split test", "experiment", "test this change", "variant", "multivariate test", "hypothesis"

Process

1. Hypothesis: What are you testing and why?
2. Metrics: Primary metric, guardrail metrics, success criteria
3. Design: Control vs variant(s), what exactly changes
4. Calculate: Sample size, test duration, minimum detectable effect
5. Plan: Implementation, QA, analysis timeline

Output Format

# A/B Test Plan: [Name]

## Hypothesis
If we [change], then [metric] will [improve/increase] because [reason].

## Variants
- **Control (A):** [current experience]
- **Variant (B):** [proposed change — be specific]

## Metrics
- **Primary:** [metric] — current: [X%] — target: [Y%]
- **Guardrail:** [metric that should NOT decrease]

## Sample Size & Duration
- MDE: [minimum detectable effect, e.g., 10% relative]
- Sample needed: [N per variant]
- Current traffic: [X visitors/day to test area]
- Estimated duration: [Y days/weeks]
- Confidence level: 95%

## Implementation Notes
[What needs to change, where, any technical considerations]

## Decision Framework
- If primary metric improves ≥ MDE with p < 0.05 → ship variant
- If no significant difference after [duration] → keep control
- If guardrail metric drops > [threshold] → stop test immediately

Rules

• Never run a test without a hypothesis
• One change per test (unless multivariate with sufficient traffic)
• Run for minimum 2 full business cycles (usually 2 weeks)
• Don't peek at results daily — pre-commit to evaluation date
• 95% confidence minimum. 80% power minimum.
• Document everything: future you needs to know why this was tested