# AbTestSetup

> When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see AnalyticsTracking. USE WHEN ab test, split test, experiment setup, A/B test, multivariate.

- Skill: `phuaky/abtestsetup` (Agent Skill)
- Install (CLI): `npx skillmds@latest add phuaky/abtestsetup`
- Raw SKILL.md: https://api.skillmd.com/api/skills/phuaky/abtestsetup/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: phuaky (https://skillmd.com/u/phuaky)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/phuaky/abtestsetup

---


# 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

Before designing a test, understand:

1. **Test Context**
   - What are you trying to improve?
   - What change are you considering?
   - What made you want to test this?

2. **Current State**
   - Baseline conversion rate?
   - Current traffic volume?
   - Any historical test data?

3. **Constraints**
   - Technical implementation complexity?
   - Timeline requirements?
   - Tools available?

---

## Core Principles

### 1. Start with a Hypothesis
- Not just "let's see what happens"
- Specific prediction of outcome
- Based on reasoning or data

### 2. Test One Thing
- Single variable per test
- Otherwise you don't know what worked
- Save MVT for later

### 3. Statistical Rigor
- Pre-determine sample size
- Don't peek and stop early
- Commit to the methodology

### 4. Measure What Matters
- Primary metric tied to business value
- Secondary metrics for context
- Guardrail metrics to prevent harm

---

## Hypothesis Framework

### Structure

```
Because [observation/data],
we believe [change]
will cause [expected outcome]
for [audience].
We'll know this is true when [metrics].
```

### Examples

**Weak hypothesis:**
"Changing the button color might increase clicks."

**Strong hypothesis:**
"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

### A/B Test (Split Test)
- Two versions: Control (A) vs. Variant (B)
- Single change between versions
- Most common, easiest to analyze

### A/B/n Test
- Multiple variants (A vs. B vs. C...)
- Requires more traffic
- Good for testing several options

### Multivariate Test (MVT)
- Multiple changes in combinations
- Tests interactions between changes
- Requires significantly more traffic

### Split URL Test
- Different URLs for variants
- Good for major page changes

---

## Sample Size Calculation

### Quick Reference

| Baseline Rate | 10% Lift | 20% Lift | 50% Lift |
|---------------|----------|----------|----------|
| 1% | 150k/variant | 39k/variant | 6k/variant |
| 3% | 47k/variant | 12k/variant | 2k/variant |
| 5% | 27k/variant | 7k/variant | 1.2k/variant |
| 10% | 12k/variant | 3k/variant | 550/variant |

### Formula Resources
- Evan Miller's calculator: https://www.evanmiller.org/ab-testing/sample-size.html
- Optimizely's calculator: https://www.optimizely.com/sample-size-calculator/

---

## Metrics Selection

### Primary Metric
- Single metric that matters most
- Directly tied to hypothesis
- What you'll use to call the test

### Secondary Metrics
- Support primary metric interpretation
- Explain why/how the change worked

### Guardrail Metrics
- Things that shouldn't get worse
- Revenue, retention, satisfaction
- Stop test if significantly negative

---

## Running the Test

### Pre-Launch Checklist

- [ ] Hypothesis documented
- [ ] Primary metric defined
- [ ] Sample size calculated
- [ ] Test duration estimated
- [ ] Variants implemented correctly
- [ ] Tracking verified
- [ ] QA completed on all variants

### During the Test

**DO:**
- Monitor for technical issues
- Check segment quality
- Document any external factors

**DON'T:**
- Peek at results and stop early
- Make changes to variants
- End early because you "know" the answer

---

## Analyzing Results

### Statistical Significance

- 95% confidence = p-value < 0.05
- Means: <5% chance result is random
- Not a guarantee—just a threshold

### 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 |

---

## Output Format

### Test Plan Document

```
# A/B Test: [Name]

## Hypothesis
[Full hypothesis using framework]

## Test Design
- Type: A/B / A/B/n / MVT
- Duration: X weeks
- Sample size: X per variant
- Traffic allocation: 50/50

## Variants
[Control and variant descriptions with visuals]

## Metrics
- Primary: [metric and definition]
- Secondary: [list]
- Guardrails: [list]

## Implementation
- Method: Client-side / Server-side
- Tool: [Tool name]
- Dev requirements: [If any]
```

---

## Questions to Ask

If you need more context:
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?

---

## Related Skills

- **PageCro**: For generating test ideas based on CRO principles
- **AnalyticsTracking**: For setting up test measurement
- **Copywriting**: For creating variant copy