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-context.md exists (or .claude/product-marketing-context.md 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#

text

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#

TypeDescriptionTraffic NeededA/BTwo versions, single changeModerateA/B/nMultiple variantsHigherMVTMultiple changes in combinationsVery highSplit URLDifferent URLs for variantsModerate

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Sample Size#

Quick Reference#

Baseline10% Lift20% Lift50% Lift1%150k/variant39k/variant6k/variant3%47k/variant12k/variant2k/variant5%27k/variant7k/variant1.2k/variant10%12k/variant3k/variant550/variant

Calculators:

• Evan Miller's
• Optimizely's

For detailed sample size tables and duration calculations: See 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#

CategoryExamplesHeadlines/CopyMessage angle, value prop, specificity, toneVisual DesignLayout, color, images, hierarchyCTAButton copy, size, placement, numberContentInformation 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#

ApproachSplitWhen to UseStandard50/50Default for A/BConservative90/10, 80/20Limit risk of bad variantRampingStart small, increaseTechnical 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 sample size? If not, result is preliminary
2. Statistically significant? Check confidence intervals
3. Effect size meaningful? Compare to MDE, project impact
4. Secondary metrics consistent? Support the primary?
5. Guardrail concerns? Anything get worse?
6. Segment differences? Mobile vs. desktop? New vs. returning?

Interpreting Results#

ResultConclusionSignificant winnerImplement variantSignificant loserKeep control, learn whyNo significant differenceNeed more traffic or bolder testMixed signalsDig deeper, maybe segment

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Documentation#

Document every test with:

• Hypothesis
• Variants (with screenshots)
• Results (sample, metrics, significance)
• Decision and learnings

For templates: See references/test-templates.md

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Common Mistakes#

Test Design#

• Testing too small a change (undetectable)
• Testing too many things (can't isolate)
• No clear hypothesis

Execution#

• Stopping early
• Changing things mid-test
• Not checking implementation

Analysis#

• Ignoring confidence intervals
• Cherry-picking segments
• Over-interpreting inconclusive results

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Task-Specific Questions#

1. What's your current conversion rate?
2. How much traffic does this page get?
3. What change are you considering and why?
4. What's the smallest improvement worth detecting?
5. What tools do you have for testing?
6. Have you tested this area before?

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Related Skills#

• page-cro: For generating test ideas based on CRO principles
• analytics-tracking: For setting up test measurement
• copywriting: For creating variant copy