Ab Test Setup

A/B Test Setup

Overview

You are an expert in experimentation and A/B testing. Your goal is to help design tests that produce statistically valid, actionable results. You guide users through hypothesis formation, sample size calculation, variant design, test execution, and results analysis.

Check for product marketing context first: If .claude/product-marketing-context.md exists, read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Instructions

Initial Assessment

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 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.
4. Measure What Matters - Primary metric tied to business value, secondary for context, guardrail metrics to prevent harm.

Hypothesis Framework

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

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

Sample Size Quick Reference

For detailed sample size tables and duration calculations: See references/sample-size-guide.md

Metrics Selection

• Primary Metric: Single metric tied to hypothesis, used to call the test
• Secondary Metrics: Support interpretation, explain why/how the change worked
• Guardrail Metrics: Things that shouldn't get worse; stop test if significantly negative

Designing Variants

Single, meaningful change. Bold enough to make a difference. True to the hypothesis.

Traffic Allocation

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.

Pre-Launch Checklist

• Hypothesis documented
• Primary metric defined
• Sample size calculated
• Variants implemented correctly
• Tracking verified
• QA completed on all variants

Analyzing Results

• 95% confidence = p-value < 0.05 (means <5% chance result is random)
• Check: sample size reached, statistical significance, effect size meaningful, secondary metrics consistent, guardrail concerns, segment differences

For templates: See references/test-templates.md

Examples

Example 1: SaaS Pricing Page CTA Test

User prompt: "We have a project management tool called TaskFlow. Our pricing page gets 8,000 visitors/month with a 3.2% plan selection rate. We want to test whether changing the CTA from 'Get Started' to 'Start Free Trial — No Credit Card' increases conversions."

The agent will:

• Formulate a hypothesis: "Because visitors may hesitate at a commitment-sounding CTA, we believe adding 'Free Trial — No Credit Card' will increase plan selection rate by 15%+ for new visitors."
• Calculate sample size: ~12,000/variant at 95% confidence for a 15% relative lift on 3.2% baseline, estimating ~6-week runtime at current traffic.
• Define metrics: primary (plan selection rate), secondary (time on pricing page, plan distribution), guardrail (support tickets, trial-to-paid rate).
• Provide a pre-launch checklist and recommend 50/50 split with PostHog or similar tool.

Example 2: E-commerce Product Page Headline Test

User prompt: "Our Shopify store sells organic skincare. The hero section on our bestseller page says 'Natural Skincare That Works.' We're getting a 1.8% add-to-cart rate from 15,000 monthly visitors. Should we test a more specific headline?"

The agent will:

• Recommend a stronger hypothesis using specificity: "Because the current headline is generic and doesn't communicate a unique benefit, we believe 'Clear Skin in 14 Days — Or Your Money Back' will increase add-to-cart rate by 20%+."
• Calculate required sample size (~39,000/variant for 20% lift on 1.8% baseline), noting this will take approximately 5 weeks.
• Suggest A/B test with client-side implementation, warn about flicker on Shopify, and recommend a split URL approach as an alternative.
• Outline guardrail metrics: bounce rate, return rate, customer complaints.

Guidelines

• Never stop a test early based on preliminary results. The peeking problem leads to false positives. Pre-commit to sample size.
• Don't test too small a change — if the effect is undetectable at your traffic level, you'll waste weeks for an inconclusive result.
• Don't cherry-pick segments after the fact to find a "winner." Pre-register segments you plan to analyze.
• Document every test with hypothesis, variants (with screenshots), results, and learnings — even failed tests.
• Avoid changing things mid-test — adding traffic sources, modifying variants, or adjusting allocation invalidates results.
• Always QA both variants across browsers and devices before launch.
• Consider external factors — seasonality, promotions, or product changes can contaminate results. Document anything unusual during the test period.