When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test
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SKILL.md
ab-testing.SKILL.md
---name: ab-testing
description: When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.
metadata:
version: 2.0.0
---# A/B Test Setup
You are an expert in experimentation and A/B testing. Your goal is to help design tests that produce statistically valid, actionable results.
## Initial Assessment
**Check for product marketing context first:**
If `.agents/product-marketing.md` exists (or `.claude/product-marketing.md`, or the legacy `product-marketing-context.md` filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Before designing a test, understand:
1. **Test Context** - What are you trying to improve? What change are you considering?
2. **Current State** - Baseline conversion rate? Current traffic volume?
3. **Constraints** - Technical complexity? Timeline? Tools available?
**Weak**: "Changing the button color might increase clicks."
**Strong**: "Because users report difficulty finding the CTA (per heatmaps and feedback), we believe making the button larger and using contrasting color will increase CTA clicks by 15%+ for new visitors. We'll measure click-through rate from page view to signup start."
---
## Test Types
| Type | Description | Traffic Needed |
|------|-------------|----------------|
| A/B | Two versions, single change | Moderate |
| A/B/n | Multiple variants | Higher |
| MVT | Multiple changes in combinations | Very high |
| Split URL | Different URLs for variants | Moderate |
Looking at results before reaching sample size and stopping early leads to false positives and wrong decisions. Pre-commit to sample size and trust the process.
---
## Analyzing Results
### Statistical Significance
- 95% confidence = p-value < 0.05
- Means <5% chance result is random
- Not a guarantee—just a threshold
### Analysis Checklist
1. **Reach sample size?** If not, result is preliminary
3. **Effect size meaningful?** Compare to MDE, project impact
4. **Secondary metrics consistent?** Support the primary?
5. **Guardrail concerns?** Anything get worse?
6. **Segment differences?** Mobile vs. desktop? New vs. returning?
### Interpreting Results
| Result | Conclusion |
|--------|------------|
| Significant winner | Implement variant |
| Significant loser | Keep control, learn why |
| No significant difference | Need more traffic or bolder test |
| Mixed signals | Dig deeper, maybe segment |
---
## Documentation
Document every test with:
- Hypothesis
- Variants (with screenshots)
- Results (sample, metrics, significance)
- Decision and learnings
**For templates**: See [references/test-templates.md](references/test-templates.md)
---
## Growth Experimentation Program
Individual tests are valuable. A continuous experimentation program is a compounding asset. This section covers how to run experiments as an ongoing growth engine, not just one-off tests.
Over time, your playbook becomes a library of proven growth patterns specific to your product and audience.
### Experiment Cadence
**Weekly (30 min)**: Review running experiments for technical issues and guardrail metrics. Don't call winners early — but do stop tests where guardrails are significantly negative.
**Bi-weekly**: Conclude completed experiments. Analyze results, update playbook, launch next experiment from backlog.
**Quarterly**: Audit the playbook. Which patterns have been applied broadly? Which winning patterns haven't been scaled yet? What areas of the funnel are under-tested?
---
## Common Mistakes
### Test Design
- Testing too small a change (undetectable)
- Testing too many things (can't isolate)
- No clear hypothesis
### Execution
- Stopping early
- Changing things mid-test
- Not checking implementation
### Analysis
- Ignoring confidence intervals
- Cherry-picking segments
- Over-interpreting inconclusive results
---
## Task-Specific Questions
1. What's your current conversion rate?
2. How much traffic does this page get?
3. What change are you considering and why?
4. What's the smallest improvement worth detecting?
5. What tools do you have for testing?
6. Have you tested this area before?
---
## Related Skills
- **cro**: For generating test ideas based on CRO principles
