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 analytics-tracking.
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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 analytics-tracking.
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📦 Ships with claude-code-templates
</> SKILL.md
ab-test-setup.SKILL.md
---name: ab-test-setup
description: 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 analytics-tracking.
---# 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
"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."
### Good Hypotheses Include
- **Observation**: What prompted this idea
- **Change**: Specific modification
- **Effect**: Expected outcome and direction
- **Audience**: Who this applies to
- **Metric**: How you'll measure success
---
## 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
- Complex analysis
### Split URL Test
- Different URLs for variants
- Good for major page changes
- Easier implementation sometimes
---
## Sample Size Calculation
### Inputs Needed
1. **Baseline conversion rate**: Your current rate
