ab-test-setup – skill Lập trình cho agent AI

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 .claude/product-marketing-context.md exists, 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

Calculate It (bundled tool)

Use this skill's own calculator — don't eyeball it:

python3 scripts/sample_size_calculator.py --baseline 0.05 --mde 0.20            # human-readable
python3 scripts/sample_size_calculator.py --baseline 0.05 --mde 0.20 --json    # for pipelines
python3 scripts/sample_size_calculator.py --baseline 0.05 --mde 0.20 --daily-traffic 2000  # adds test-duration estimate

Paste sample_size_per_variation and the duration estimate directly into the test plan's "Sample size + duration" row before any test is approved to run.

Quick Reference

Generated by sample_size_calculator.py (two-proportion z-test, α=0.05 two-tailed, 80% power; relative MDE):

| Baseline | 10% Lift | 20% Lift | 50% Lift |

|----------|----------|----------|----------|

| 1% | 163k/variant | 43k/variant | 7.7k/variant |

| 3% | 53k/variant | 14k/variant | 2.5k/variant |

| 5% | 31k/variant | 8.2k/variant | 1.5k/variant |

| 10% | 15k/variant | 3.8k/variant | 683/variant |

Cross-check calculators (should agree with the script within rounding):

• [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

DON'T:

• 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

| 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

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

Proactively offer A/B test design when:

1. Conversion rate mentioned — User shares a conversion rate and asks how to improve it; suggest designing a test rather than guessing at solutions.

2. Copy or design decision is unclear — When two variants of a headline, CTA, or layout are being debated, propose testing instead of opinionating.

3. Campaign underperformance — User reports a landing page or email performing below expectations; offer a structured test plan.

4. Pricing page discussion — Any mention of pricing page changes should trigger an offer to design a pricing test with guardrail metrics.

5. Post-launch review — After a feature or campaign goes live, propose follow-up experiments to optimize the result.

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Output Artifacts

| Artifact | Format | Description |

|----------|--------|-------------|

| Experiment Brief | Markdown doc | Hypothesis, variants, metrics, sample size, duration, owner |

| Sample Size Calculator Input | Table | Baseline rate, MDE, confidence level, power |

| Pre-Launch QA Checklist | Checklist | Implementation, tracking, variant rendering verification |

| Results Analysis Report | Markdown doc | Statistical significance, effect size, segment breakdown, decision |

| Test Backlog | Prioritized list | Ranked experiments by expected impact and feasibility |

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Communication

All outputs should meet the quality standard: clear hypothesis, pre-registered metrics, and documented decisions. Avoid presenting inconclusive results as wins. Every test should produce a learning, even if the variant loses. Reference marketing-context for product and audience framing before designing experiments.

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