ab-testing
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.
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:
- Test Context - What are you trying to improve? What change are you considering?
- Current State - Baseline conversion rate? Current traffic volume?
- 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
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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:
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
- For a pre-specified fixed-horizon test, compare its p-value with the chosen significance level (commonly α = 0.05).
- A p-value describes how incompatible the data are with the null model: the probability, assuming that model and its assumptions, of a result at least as extreme as the observed one. It is not the probability that the result is random, that the null is true, or that a variant will win again.
- A 95% confidence interval comes from a procedure with 95% coverage over repeated samples under its assumptions. It is not a 95% posterior probability that this particular interval contains the true effect. Report the interval and effect size alongside the p-value.
- Use the planned analysis and stopping rule. Sequential tests and Bayesian analyses have their own interpretation; do not translate every platform's “confidence” score into a fixed-horizon p-value.
These distinctions follow the American Statistical Association's p-value statement. Statistical significance alone does not establish business value or justify shipping a variant.
Analysis Checklist
- Reach sample size? If not, result is preliminary
- Statistically significant? Check confidence intervals
- Effect size meaningful? Compare to MDE, project impact
- Secondary metrics consistent? Support the primary?
- Guardrail concerns? Anything get worse?
- Segment differences? Mobile vs. desktop? New vs. returning?
Interpreting Results
| Result | Conclusion |
|---|---|
| Significant winner | Check effect size, uncertainty and guardrails before implementing |
| Significant loser | Keep control, learn why |
| No significant difference | Report the effect interval: it may still allow meaningful benefit and harm, or may exclude effects worth pursuing. Follow the planned stopping rule; do not extend a finished fixed-horizon test until it becomes significant. Plan a new test if needed. |
| Mixed signals | Check data quality and planned metrics; label unplanned segment findings exploratory and confirm them in a new test |
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
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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:
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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
- What's your current conversion rate?
- How much traffic does this page get?
- What change are you considering and why?
- What's the smallest improvement worth detecting?
- What tools do you have for testing?
- 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
References
Sample Size Guide
sample-size-guide.md
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
- Baseline conversion rate: Your current rate
- Minimum detectable effect (MDE): Smallest change worth detecting
- Statistical significance level: Usually α = 0.05 (5%)
- 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?)
Significance level (α = 0.05): For the planned test and its assumptions, limits the long-run probability of rejecting a true null hypothesis to 5%. It is not the probability that an observed difference is “due to chance.” Repeated unadjusted looks or multiple comparisons do not retain that error rate. See the ASA's interpretation guidance.
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
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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:
- Increase MDE: Accept only detecting larger effects (20%+ lift)
- Lower confidence: Use 90% instead of 95% (risky, document it)
- Reduce variants: Test only the most promising variant
- Combine traffic: Test across multiple similar pages
- Test upstream: Test earlier in funnel where traffic is higher
- Don't test: Make decision based on qualitative data instead
- 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?
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Test Templates
test-templates.md
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
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Results Documentation Template
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Test Repository Entry Template
For tracking all tests in a central location:
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Quick Test Brief Template
For simple tests that don't need full documentation:
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Stakeholder Update Template
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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:
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Exported from Marketing Skills on 2026-10-12