# A/B测试计划：[名称]
假设
如果[改变]，[指标]将[改善/增加]，因为[原因]。
变体
对照版(A)：[当前体验]
变体(B)：[建议更改——请具体说明]
指标
主要：[指标]——当前：[X%]——目标：[Y%]
护栏：[不应下降的指标]
样本量与持续时间
MDE：[最小可检测效应，例如10%相对]
所需样本：[每个变体N个]
当前流量：[测试区域每天X位访客]
预计持续时间：[Y天/周]
置信水平：95%
实施说明
[需要更改的内容、地点、任何技术考虑]
决策框架
如果主要指标改善≥MDE且p<0.05→发布变体
如果[持续时间]后无显著差异→保持对照版
如果护栏指标下降>[阈值]→立即停止测试

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

• Hypothesis: What are you testing and why?
• Metrics: Primary metric, guardrail metrics, success criteria
• Design: Control vs variant(s), what exactly changes
• Calculate: Sample size, test duration, minimum detectable effect
• 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