ab-testing – Coding skill for AI agents

A/B Test Setup

When to Use

Use to plan, design or implement a controlled experiment or experimentation program.

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

• A/B tests may compare a bundle, but then estimate the bundle's overall effect; individual component effects require a suitable factorial design

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

The root and reference tables previously gave inconsistent values. Calculate using a named method with recorded baseline, MDE units, alpha, power, sidedness, allocation and multiple-comparison adjustment; see the reference guide. Do not use unbound numeric shortcuts.

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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Assignment and safety checks

Define stable randomization unit, eligibility/exposure, conversion window and intention-to-treat analysis before launch. Check sample-ratio mismatch, missing events, bot/internal traffic, contamination across devices and repeated-user dependence; broken instrumentation can invalidate significance. Predeclare segments/comparisons and separate exploratory findings. Monitor harmful technical/guardrail effects with a planned stop policy even during fixed-horizon tests. Sequential methods require their own valid stopping rules. Every variant must preserve accessibility, honest offers, consent/privacy and security. Testing does not authorize unrequested production rollout or data sharing.

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

• Predefine the inference method and alpha; p < 0.05 is not a probability that the result is random or the hypothesis is true
• Report effect size and a compatible interval; evaluate practical value and guardrails

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 | Inconclusive unless a planned equivalence/noninferiority analysis supports a bounded conclusion; do not extend opportunistically |

| 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

For templates: See [references/test-templates.md](references/test-templates.md)

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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) |

|

… (full content is in the download package)