ab-test-setup

name	ab-test-setup
description	When the user wants to plan, design, or implement an A/B test or experiment — "split test," "variant copy," "multivariate test," "hypothesis," "statistical significance," "how long should I run this test," or comparing two versions. For tracking implementation, see analytics-tracking. For page-level conversion optimization, see page-cro.
category	marketing
tier	on-demand

A/B Test Setup

Initial Assessment

Product context: If .agents/product-marketing-context.md exists (or .claude/product-marketing-context.md in older setups), read it first and tailor output to it; only ask for what it doesn't cover.

Establish:

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 "let's see what happens" — a 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
• Optimizely's

Detailed sample size tables and duration calculations: references/sample-size-guide.md

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Metrics Selection

• Primary Metric — the single metric that matters most, directly tied to the hypothesis; what you'll use to call the test.
• Secondary Metrics — support interpretation; explain why/how the change worked.
• Guardrail Metrics — things that shouldn't get worse; stop the 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: users see the 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: peeking at results and stopping early, changing variants mid-test, adding traffic from new sources.

The Peeking Problem

Stopping early on pre-sample-size results produces 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, i.e. <5% chance the result is random — a threshold, not a guarantee.

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

Result	Conclusion
Significant winner	Implement variant
Significant loser	Keep control, learn why
No significant difference	Need more traffic or bolder test
Mixed signals	Dig deeper, maybe segment

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Documentation

Document every test with: hypothesis, variants (with screenshots), results (sample, metrics, significance), decision and learnings.

Templates: 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