/ab-testing

name: ab-testing
description: 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," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.
metadata:
  version: 2.0.0

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

**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

• 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

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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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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 |

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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)

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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

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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

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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

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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

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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

**Avoid:**

• Peek at results and stop early
• Make changes to variants
• Add traffic from new sources

The Peeking Problem

Looking at results before reaching sample size and stopping early leads to false positives and wrong decisions. Pre-commit to sample size and trust the process.

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Analyzing Results

Statistical Significance

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

Analysis Checklist

1. **Reach sam