What it does. 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. Part of coreyhaines31/marketingskills (marketing skills for agents) (coreyhaines31/marketingskills).

Upstream	coreyhaines31/marketingskills
Skill file	skills/ab-testing/SKILL.md
License	MIT
Author	Corey Haines
Fetched	2026-09-10

Install

• npx skills add coreyhaines31/marketingskills --skill ab-testing, or copy the skill folder into ~/.claude/skills/ab-testing/.
• Raw file: curl -sL https://raw.githubusercontent.com/coreyhaines31/marketingskills/HEAD/skills/ab-testing/SKILL.md

SKILL.md (verbatim)

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?

---

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

---

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."

---

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

---

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

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

---

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

---

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

---

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

---

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

---

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.

---

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

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

---

Documentation

Document every test with:

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

For templates: See references/test-templates.md

---

Growth Experimentation Program

Individual tests are valuable. A continuous experimentation program is a compounding asset. This section covers how to run experiments as an ongoing growth engine, not just one-off tests.

The Experiment Loop

1. Generate hypotheses (from data, research, competitors, customer feedback)
2. Prioritize with ICE scoring
3. Design and run the test
4. Analyze results with statistical rigor
5. Promote winners to a playbook
6. Generate new hypotheses from learnings
→ Repeat

Hypothesis Generation

Feed your experiment backlog from multiple sources:

Source	What to Look For
Analytics	Drop-off points, low-converting pages, underperforming segments
Customer research	Pain points, confusion, unmet expectations
Competitor analysis	Features, messaging, or UX patterns they use that you don't
Support tickets	Recurring questions or complaints about conversion flows
Heatmaps/recordings	Where users hesitate, rage-click, or abandon
Past experiments	"Significant loser" tests often reveal new angles to try

ICE Prioritization

Score each hypothesis 1-10 on three dimensions:

Dimension	Question
Impact	If this works, how much will it move the primary metric?
Confidence	How sure are we this will work? (Based on data, not gut.)
Ease	How fast and cheap can we ship and measure this?

ICE Score = (Impact + Confidence + Ease) / 3

Run highest-scoring experiments first. Re-score monthly as context changes.

Experiment Velocity

Track your experimentation rate as a leading indicator of growth:

Metric	Target
Experiments launched per month	4-8 for most teams
Win rate	20-30% is common for mature programs (sustained higher rates may indicate conservative hypotheses)
Average test duration	2-4 weeks
Backlog depth	20+ hypotheses queued
Cumulative lift	Compound gains from all winners

The Experiment Playbook

When a test wins, don't just implement it — document the pattern:

## [Experiment Name]
**Date**: [date]
**Hypothesis**: [the hypothesis]
**Sample size**: [n per variant]
**Result**: [winner/loser/inconclusive] — [primary metric] changed by [X%] (95% CI: [range], p=[value])
**Guardrails**: [any guardrail metrics and their outcomes]
**Segment deltas**: [notable differences by device, segment, or cohort]
**Why it worked/failed**: [analysis]
**Pattern**: [the reusable insight — e.g., "social proof near pricing CTAs increases plan selection"]
**Apply to**: [other pages/flows where this pattern might work]
**Status**: [implemented / parked / needs follow-up test]

Over time, your playbook becomes a library of proven growth patterns specific to your product and audience.

Experiment Cadence

Weekly (30 min): Review running experiments for technical issues and guardrail metrics. Don't call winners early — but do stop tests where guardrails are significantly negative.

Bi-weekly: Conclude completed experiments. Analyze results, update playbook, launch next experiment from backlog.

Monthly (1 hour): Review experiment velocity, win rate, cumulative lift. Replenish hypothesis backlog. Re-prioritize with ICE.

Quarterly: Audit the playbook. Which patterns have been applied broadly? Which winning patterns haven't been scaled yet? What areas of the funnel are under-tested?

---

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

---

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?

---

Related Skills

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

Other files in this skill

• evals/evals.json
• references/sample-size-guide.md
• references/test-templates.md

references/sample-size-guide.md (verbatim)

Sample Size Guide

Reference for calculating sample sizes and test duration.

Contents

• Sample Size Fundamentals (required inputs, what these mean)
• Sample Size Quick Reference Tables
• Duration Calculator (formula, examples, minimum duration rules, maximum duration guidelines)
• Online Calculators
• Adjusting for Multiple Variants
• Common Sample Size Mistakes
• When Sample Size Requirements Are Too High
• Sequential Testing
• Quick Decision Framework

Sample Size Fundamentals

Required Inputs

1. Baseline conversion rate: Your current rate
2. Minimum detectable effect (MDE): Smallest change worth detecting
3. Statistical significance level: Usually 95% (α = 0.05)
4. Statistical power: Usually 80% (β = 0.20)

What These Mean

Baseline conversion rate: If your page converts at 5%, that's your baseline.

MDE (Minimum Detectable Effect): The smallest improvement you care about detecting. Set this based on:

• Business impact (is a 5% lift meaningful?)
• Implementation cost (worth the effort?)
• Realistic expectations (what have past tests shown?)

Statistical significance (95%): Means there's less than 5% chance the observed difference is due to random chance.

Statistical power (80%): Means if there's a real effect of size MDE, you have 80% chance of detecting it.

---

Sample Size Quick Reference Tables

Conversion Rate: 1%

Lift to Detect	Sample per Variant	Total Sample
5% (1% → 1.05%)	1,500,000	3,000,000
10% (1% → 1.1%)	380,000	760,000
20% (1% → 1.2%)	97,000	194,000
50% (1% → 1.5%)	16,000	32,000
100% (1% → 2%)	4,200	8,400

Conversion Rate: 3%

Lift to Detect	Sample per Variant	Total Sample
5% (3% → 3.15%)	480,000	960,000
10% (3% → 3.3%)	120,000	240,000
20% (3% → 3.6%)	31,000	62,000
50% (3% → 4.5%)	5,200	10,400
100% (3% → 6%)	1,400	2,800

Conversion Rate: 5%

Lift to Detect	Sample per Variant	Total Sample
5% (5% → 5.25%)	280,000	560,000
10% (5% → 5.5%)	72,000	144,000
20% (5% → 6%)	18,000	36,000
50% (5% → 7.5%)	3,100	6,200
100% (5% → 10%)	810	1,620

Conversion Rate: 10%

Lift to Detect	Sample per Variant	Total Sample
5% (10% → 10.5%)	130,000	260,000
10% (10% → 11%)	34,000	68,000
20% (10% → 12%)	8,700	17,400
50% (10% → 15%)	1,500	3,000
100% (10% → 20%)	400	800

Conversion Rate: 20%

Lift to Detect	Sample per Variant	Total Sample
5% (20% → 21%)	60,000	120,000
10% (20% → 22%)	16,000	32,000
20% (20% → 24%)	4,000	8,000
50% (20% → 30%)	700	1,400
100% (20% → 40%)	200	400

---

Duration Calculator

Formula

Duration (days) = (Sample per variant × Number of variants) / (Daily traffic × % exposed)

Examples

Scenario 1: High-traffic page

• Need: 10,000 per variant (2 variants = 20,000 total)
• Daily traffic: 5,000 visitors
• 100% exposed to test
• Duration: 20,000 / 5,000 = 4 days

Scenario 2: Medium-traffic page

• Need: 30,000 per variant (60,000 total)
• Daily traffic: 2,000 visitors
• 100% exposed
• Duration: 60,000 / 2,000 = 30 days

Scenario 3: Low-traffic with partial exposure

• Need: 15,000 per variant (30,000 total)
• Daily traffic: 500 visitors
• 50% exposed to test
• Effective daily: 250
• Duration: 30,000 / 250 = 120 days (too long!)

Minimum Duration Rules

Even with sufficient sample size, run tests for at least:

• 1 full week: To capture day-of-week variation
• 2 business cycles: If B2B (weekday vs. weekend patterns)
• Through paydays: If e-commerce (beginning/end of month)

Maximum Duration Guidelines

Avoid running tests longer than 4-8 weeks:

• Novelty effects wear off
• External factors intervene
• Opportunity cost of other tests

---

Online Calculators

Recommended Tools

Evan Miller's Calculator https://www.evanmiller.org/ab-testing/sample-size.html

• Simple interface
• Bookmark-worthy

Optimizely's Calculator https://www.optimizely.com/sample-size-calculator/

• Business-friendly language
• Duration estimates

AB Test Guide Calculator https://www.abtestguide.com/calc/

• Includes Bayesian option
• Multiple test types

VWO Duration Calculator https://vwo.com/tools/ab-test-duration-calculator/

• Duration-focused
• Good for planning

---

Adjusting for Multiple Variants

With more than 2 variants (A/B/n tests), you need more sample:

Variants	Multiplier
2 (A/B)	1x
3 (A/B/C)	~1.5x
4 (A/B/C/D)	~2x
5+	Consider reducing variants

Why? More comparisons increase chance of false positives. You're comparing:

• A vs B
• A vs C
• B vs C (sometimes)

Apply Bonferroni correction or use tools that handle this automatically.

---

Common Sample Size Mistakes

1. Underpowered tests

Problem: Not enough sample to detect realistic effects Fix: Be realistic about MDE, get more traffic, or don't test

2. Overpowered tests

Problem: Waiting for sample size when you already have significance Fix: This is actually fine—you committed to sample size, honor it

3. Wrong baseline rate

Problem: Using wrong conversion rate for calculation Fix: Use the specific metric and page, not site-wide averages

4. Ignoring segments

Problem: Calculating for full traffic, then analyzing segments Fix: If you plan segment analysis, calculate sample for smallest segment

5. Testing too many things

Problem: Dividing traffic too many ways Fix: Prioritize ruthlessly, run fewer concurrent tests

---

When Sample Size Requirements Are Too High

Options when you can't get enough traffic:

1. Increase MDE: Accept only detecting larger effects (20%+ lift)
2. Lower confidence: Use 90% instead of 95% (risky, document it)
3. Reduce variants: Test only the most promising variant
4. Combine traffic: Test across multiple similar pages
5. Test upstream: Test earlier in funnel where traffic is higher
6. Don't test: Make decision based on qualitative data instead
7. Longer test: Accept longer duration (weeks/months)

---

Sequential Testing

If you must check results before reaching sample size:

What is it?

Statistical method that adjusts for multiple looks at data.

When to use

• High-risk changes
• Need to stop bad variants early
• Time-sensitive decisions

Tools that support it

• Optimizely (Stats Accelerator)
• VWO (SmartStats)
• PostHog (Bayesian approach)

Tradeoff

• More flexibility to stop early
• Slightly larger sample size requirement
• More complex analysis

---

Quick Decision Framework

Can I run this test?

Daily traffic to page: _____
Baseline conversion rate: _____
MDE I care about: _____

Sample needed per variant: _____ (from tables above)
Days to run: Sample / Daily traffic = _____

If days > 60: Consider alternatives
If days > 30: Acceptable for high-impact tests
If days < 14: Likely feasible
If days < 7: Easy to run, consider running longer anyway

references/test-templates.md (verbatim)

A/B Test Templates Reference

Templates for planning, documenting, and analyzing experiments.

Contents

• Test Plan Template
• Results Documentation Template
• Test Repository Entry Template
• Quick Test Brief Template
• Stakeholder Update Template
• Experiment Prioritization Scorecard
• Hypothesis Bank Template

Test Plan Template

# A/B Test: [Name]

## Overview
- **Owner**: [Name]
- **Test ID**: [ID in testing tool]
- **Page/Feature**: [What's being tested]
- **Planned dates**: [Start] - [End]

## Hypothesis

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

## Test Design

| Element | Details |
|---------|---------|
| Test type | A/B / A/B/n / MVT |
| Duration | X weeks |
| Sample size | X per variant |
| Traffic allocation | 50/50 |
| Tool | [Tool name] |
| Implementation | Client-side / Server-side |

## Variants

### Control (A)
[Screenshot]
- Current experience
- [Key details about current state]

### Variant (B)
[Screenshot or mockup]
- [Specific change #1]
- [Specific change #2]
- Rationale: [Why we think this will win]

## Metrics

### Primary
- **Metric**: [metric name]
- **Definition**: [how it's calculated]
- **Current baseline**: [X%]
- **Minimum detectable effect**: [X%]

### Secondary
- [Metric 1]: [what it tells us]
- [Metric 2]: [what it tells us]
- [Metric 3]: [what it tells us]

### Guardrails
- [Metric that shouldn't get worse]
- [Another safety metric]

## Segment Analysis Plan
- Mobile vs. desktop
- New vs. returning visitors
- Traffic source
- [Other relevant segments]

## Success Criteria
- Winner: [Primary metric improves by X% with 95% confidence]
- Loser: [Primary metric decreases significantly]
- Inconclusive: [What we'll do if no significant result]

## Pre-Launch Checklist
- [ ] Hypothesis documented and reviewed
- [ ] Primary metric defined and trackable
- [ ] Sample size calculated
- [ ] Test duration estimated
- [ ] Variants implemented correctly
- [ ] Tracking verified in all variants
- [ ] QA completed on all variants
- [ ] Stakeholders informed
- [ ] Calendar hold for analysis date

---

Results Documentation Template

# A/B Test Results: [Name]

## Summary
| Element | Value |
|---------|-------|
| Test ID | [ID] |
| Dates | [Start] - [End] |
| Duration | X days |
| Result | Winner / Loser / Inconclusive |
| Decision | [What we're doing] |

## Hypothesis (Reminder)
[Copy from test plan]

## Results

### Sample Size
| Variant | Target | Actual | % of target |
|---------|--------|--------|-------------|
| Control | X | Y | Z% |
| Variant | X | Y | Z% |

### Primary Metric: [Metric Name]
| Variant | Value | 95% CI | vs. Control |
|---------|-------|--------|-------------|
| Control | X% | [X%, Y%] | — |
| Variant | X% | [X%, Y%] | +X% |

**Statistical significance**: p = X.XX (95% = sig / not sig)
**Practical significance**: [Is this lift meaningful for the business?]

### Secondary Metrics

| Metric | Control | Variant | Change | Significant? |
|--------|---------|---------|--------|--------------|
| [Metric 1] | X | Y | +Z% | Yes/No |
| [Metric 2] | X | Y | +Z% | Yes/No |

### Guardrail Metrics

| Metric | Control | Variant | Change | Concern? |
|--------|---------|---------|--------|----------|
| [Metric 1] | X | Y | +Z% | Yes/No |

### Segment Analysis

**Mobile vs. Desktop**
| Segment | Control | Variant | Lift |
|---------|---------|---------|------|
| Mobile | X% | Y% | +Z% |
| Desktop | X% | Y% | +Z% |

**New vs. Returning**
| Segment | Control | Variant | Lift |
|---------|---------|---------|------|
| New | X% | Y% | +Z% |
| Returning | X% | Y% | +Z% |

## Interpretation

### What happened?
[Explanation of results in plain language]

### Why do we think this happened?
[Analysis and reasoning]

### Caveats
[Any limitations, external factors, or concerns]

## Decision

**Winner**: [Control / Variant]

**Action**: [Implement variant / Keep control / Re-test]

**Timeline**: [When changes will be implemented]

## Learnings

### What we learned
- [Key insight 1]
- [Key insight 2]

### What to test next
- [Follow-up test idea 1]
- [Follow-up test idea 2]

### Impact
- **Projected lift**: [X% improvement in Y metric]
- **Business impact**: [Revenue, conversions, etc.]

---

Test Repository Entry Template

For tracking all tests in a central location:

| Test ID | Name | Page | Dates | Primary Metric | Result | Lift | Link |
|---------|------|------|-------|----------------|--------|------|------|
| 001 | Hero headline test | Homepage | 1/1-1/15 | CTR | Winner | +12% | [Link] |
| 002 | Pricing table layout | Pricing | 1/10-1/31 | Plan selection | Loser | -5% | [Link] |
| 003 | Signup form fields | Signup | 2/1-2/14 | Completion | Inconclusive | +2% | [Link] |

---

Quick Test Brief Template

For simple tests that don't need full documentation:

## [Test Name]

**What**: [One sentence description]
**Why**: [One sentence hypothesis]
**Metric**: [Primary metric]
**Duration**: [X weeks]
**Result**: [TBD / Winner / Loser / Inconclusive]
**Learnings**: [Key takeaway]

---

Stakeholder Update Template

## A/B Test Update: [Name]

**Status**: Running / Complete
**Days remaining**: X (or complete)
**Current sample**: X% of target

### Preliminary observations
[What we're seeing - without making decisions yet]

### Next steps
[What happens next]

### Timeline
- [Date]: Analysis complete
- [Date]: Decision and recommendation
- [Date]: Implementation (if winner)

---

Experiment Prioritization Scorecard

For deciding which tests to run:

Factor	Weight	Test A	Test B	Test C
Potential impact	30%
Confidence in hypothesis	25%
Ease of implementation	20%
Risk if wrong	15%
Strategic alignment	10%
Total

Scoring: 1-5 (5 = best)

---

Hypothesis Bank Template

For collecting test ideas:

| ID | Page/Area | Observation | Hypothesis | Potential Impact | Status |
|----|-----------|-------------|------------|------------------|--------|
| H1 | Homepage | Low scroll depth | Shorter hero will increase scroll | High | Testing |
| H2 | Pricing | Users compare plans | Comparison table will help | Medium | Backlog |
| H3 | Signup | Drop-off at email | Social login will increase completion | Medium | Backlog |

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