# Ab Testing

> When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program.

- Skill: `ranbot-ai/ab-testing` (Agent Skill)
- Install (CLI): `npx skillmds add ranbot-ai/ab-testing`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ranbot-ai/ab-testing/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: ranbot-ai (https://skillmd.com/u/ranbot-ai)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ranbot-ai/ab-testing

---



# A/B Test Setup
## When to Use

Use this skill when you need 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,"...


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?

---

## 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

### 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].
```

### Example

**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 |

---

## Sample Size

### Quick Reference

| Baseline | 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 |

**Calculators:**
- [Evan Miller's](https://www.evanmiller.org/ab-testing/sample-size.html)
- [Optimizely's](https://www.optimizely.com/sample-size-calculator/)

**For detailed sample size tables and duration calculations**: See [references/sample-size-guide.md](references/sample-size-guide.md)

---

## 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
- Stop test if significantly negative

### Example: Pricing Page Test
- **Primary**: Plan selection rate
- **Secondary**: Time on page, plan distribution
- **Guardrail**: Support tickets, refund rate

---

## Designing Variants

### What to Vary

| Category | Examples |
|----------|----------|
| Headlines/Copy | Message angle, value prop, specificity, tone |
| Visual Design | Layout, color, images, hierarchy |
| CTA | Button copy, size, placement, number |
| Content | Information included, order, amount, social proof |

### Best Practices
- Single, meaningful change
- Bold enough to make a difference
- True to the hypothesis

---

## Traffic Allocation

| Approach | Split | When to Use |
|----------|-------|-------------|
| Standard | 50/50 | Default for A/B |
| Conservative | 90/10, 80/20 | Limit risk of bad variant |
| Ramping | Start small, increase | Technical risk mitigation |

**Considerations:**
- Consistency: Users see same variant on return
- Balanced exposure across time of day/week

---

## Implementation

### Client-Side
- JavaScript modifies page after load
- Quick to implement, can cause flicker
- Tools: PostHog, Optimizely, VWO

### Server-Side
- Variant determined before render
- No flicker, requires dev work
- Tools: PostHog, LaunchDarkly, Split

---

## Running the Test

### Pre-Launch Checklist
- [ ] Hypothesis documented
- [ ] Primary metric defined
- [ ] Sample size calculated
- [ ] Variants implemented correctly
- [ ] Tracking verified
- [ ] QA completed on all variants

### During the Test

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

**Avoi