A/B Test Setup

When to Use

Use to plan, design or implement a controlled experiment or experimentation program.

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?

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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

• A/B tests may compare a bundle, but then estimate the bundle's overall effect; individual component effects require a suitable factorial design

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

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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."

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