Ab Testing

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

Before designing a test, understand:

1. Test Context

• What are you trying to improve?
• What change are you considering?
• What made you want to test this?

2. Current State

• Baseline conversion rate?
• Current traffic volume?
• Any historical test data?

3. Constraints

• Technical implementation complexity?
• Timeline requirements?
• 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
• Save MVT for later

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].

Examples

Weak hypothesis: "Changing the button color might increase clicks."

Strong hypothesis: "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."

Good Hypotheses Include

• Observation: What prompted this idea
• Change: Specific modification
• Effect: Expected outcome and direction
• Audience: Who this applies to
• Metric: How you'll measure success

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Test Types

A/B Test (Split Test)

• Two versions: Control (A) vs. Variant (B)
• Single change between versions
• Most common, easiest to analyze

A/B/n Test

• Multiple variants (A vs. B vs. C...)
• Requires more traffic
• Good for testing several options

Multivariate Test (MVT)

• Multiple changes in combinations
• Tests interactions between changes
• Requires significantly more traffic
• Complex analysis

Split URL Test

• Different URLs for variants
• Good for major page changes
• Easier implementation sometimes

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Sample Size Calculation

Inputs Needed

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

Quick Reference

Formula Resources

• Evan Miller's calculator: https://www.evanmiller.org/ab-testing/sample-size.html
• Optimizely's calculator: https://www.optimizely.com/sample-size-calculator/

Test Duration

Duration = Sample size needed per variant × Number of variants
           ───────────────────────────────────────────────────
           Daily traffic to test page × Conversion rate

Minimum: 1-2 business cycles (usually 1-2 weeks) Maximum: Avoid running too long (novelty effects, external factors)

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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
• Help understand user behavior

Guardrail Metrics

• Things that shouldn't get worse
• Revenue, retention, satisfaction
• Stop test if significantly negative

Metric Examples by Test Type

Homepage CTA test:

• Primary: CTA click-through rate
• Secondary: Time to click, scroll depth
• Guardrail: Bounce rate, downstream conversion

Pricing page test:

• Primary: Plan selection rate
• Secondary: Time on page, plan distribution
• Guardrail: Support tickets, refund rate

Signup flow test:

• Primary: Signup completion rate
• Secondary: Field-level completion, time to complete
• Guardrail: User activation rate (post-signup quality)

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Designing Variants

Control (A)

• Current experience, unchanged
• Don't modify during test

Variant (B+)

Best practices:

• Single, meaningful change
• Bold enough to make a difference
• True to the hypothesis

What to vary:

Headlines/Copy:

• Message angle
• Value proposition
• Specificity level
• Tone/voice

Visual Design:

• Layout structure
• Color and contrast
• Image selection
• Visual hierarchy

CTA:

• Button copy
• Size/prominence
• Placement
• Number of CTAs

Content:

• Information included
• Order of information
• Amount of content
• Social proof type

Documenting Variants

Control (A):
- Screenshot
- Description of current state

Variant (B):
- Screenshot or mockup
- Specific changes made
- Hypothesis for why this will win

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Traffic Allocation

Standard Split

• 50/50 for A/B test
• Equal split for multiple variants

Conservative Rollout

• 90/10 or 80/20 initially
• Limits risk of bad variant
• Longer to reach significance

Ramping

• Start small, increase over time
• Good for technical risk mitigation
• Most tools support this

Considerations

• Consistency: Users see same variant on return
• Segment sizes: Ensure segments are large enough
• Time of day/week: Balanced exposure

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Implementation Approaches

Client-Side Testing

Tools: PostHog, Optimizely, VWO, custom

How it works:

• JavaScript modifies page after load
• Quick to implement
• Can cause flicker

Best for:

• Marketing pages
• Copy/visual changes
• Quick iteration

Server-Side Testing

Tools: PostHog, LaunchDarkly, Split, custom

How it works:

• Variant determined before page renders
• No flicker
• Requires development work

Best for:

• Product features
• Complex changes
• Performance-sensitive pages

Feature Flags

• Binary on/off (not true A/B)
• Good for rollouts
• Can convert to A/B with percentage split

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Running the Test

Pre-Launch Checklist

• [ ] Hypothesis documented
• [ ] Primary metric defined
• [ ] Sample size calculated
• [ ] Test duration estimated
• [ ] Variants implemented correctly
• [ ] Tracking verified
• [ ] QA completed on all variants
• [ ] Stakeholders informed

During the Test

DO:

• Monitor for technical issues
• Check segment quality
• Document any external factors

DON'T:

• Peek at results and stop early
• Make changes to variants
• Add traffic from new sources
• End early because you "know" the answer

Peeking Problem

Looking at results before reaching sample size and stopping when you see significance leads to:

• False positives
• Inflated effect sizes
• Wrong decisions

Solutions:

• Pre-commit to sample size and stick to it
• Use sequential testing if you must peek
• 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

Practical Significance

Statistical ≠ Practical

• Is the effect size meaningful for business?
• Is it worth the implementation cost?
• Is it sustainable over time?

What to Look At

1. Did you reach sample size?

• If not, result is preliminary

2. Is it statistically significant?

• Check confidence intervals
• Check p-value

3. Is the effect size meaningful?

• Compare to your MDE
• Project business impact

4. Are secondary metrics consistent?

• Do they support the primary?
• Any unexpected effects?

5. Any guardrail concerns?

• Did anything get worse?
• Long-term risks?

6. Segment differences?

• Mobile vs. desktop?
• New vs. returning?
• Traffic source?

Interpreting Results

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Documenting and Learning

Test Documentation

Test Name: [Name]
Test ID: [ID in testing tool]
Dates: [Start] - [End]
Owner: [Name]

Hypothesis:
[Full hypothesis statement]

Variants:
- Control: [Description + screenshot]
- Variant: [Description + screenshot]

Results:
- Sample size: [achieved vs. target]
- Primary metric: [control] vs. [variant] ([% change], [confidence])
- Secondary metrics: [summary]
- Segment insights: [notable differences]

Decision: [Winner/Loser/Inconclusive]
Action: [What we're doing]

Learnings:
[What we learned, what to test next]

Building a Learning Repository

• Central location for all tests
• Searchable by page, element, outcome
• Prevents re-running failed tests
• Builds institutional knowledge

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Output Format

Test Plan Document

# A/B Test: [Name]

## Hypothesis
[Full hypothesis using framework]

## Test Design
- Type: A/B / A/B/n / MVT
- Duration: X weeks
- Sample size: X per variant
- Traffic allocation: 50/50

## Variants
[Control and variant descriptions with visuals]

## Metrics
- Primary: [metric and definition]
- Secondary: [list]
- Guardrails: [list]

## Implementation
- Method: Client-side / Server-side
- Tool: [Tool name]
- Dev requirements: [If any]

## Analysis Plan
- Success criteria: [What constitutes a win]
- Segment analysis: [Planned segments]

Results Summary

When test is complete

Recommendations

Next steps based on results

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Common Mistakes

Test Design

• Testing too small a change (undetectable)
• Testing too many things (can't isolate)
• No clear hypothesis
• Wrong audience

Execution

• Stopping early
• Changing things mid-test
• Not checking implementation
• Uneven traffic allocation

Analysis

• Ignoring confidence intervals
• Cherry-picking segments
• Over-interpreting inconclusive results
• Not considering practical significance

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Questions to Ask

If you need more context: 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

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Credits & Attribution

This skill is based on the excellent work by [AITYTech](https://github.com/aitytech/agentkits-marketing).

Original repository: https://github.com/aitytech/agentkits-marketing

Special thanks to AITYTech for their generous open-source contributions, which helped shape this skill collection. Adapted by webconsulting.at for this skill collection

A/B Testing Statistical Guide

Based on research from Evan Miller, CXL, Optimizely.

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Statistical Foundations

Key Concepts

Statistical Significance (Confidence Level)

• Probability that result is not due to chance
• Standard: 95% (alpha = 0.05)
• Conservative: 99% (alpha = 0.01)
• Aggressive: 90% (alpha = 0.10)

Statistical Power

• Probability of detecting a real effect
• Standard: 80% (beta = 0.20)
• Conservative: 90% (beta = 0.10)
• Minimum: 70% (beta = 0.30)

Minimum Detectable Effect (MDE)

• Smallest improvement you want to detect
• Lower MDE = larger sample needed
• Realistic MDE = 10-20% for most tests

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Sample Size Quick Reference

Sample Size per Variant (95% confidence, 80% power)

Formula: n = 2 ((z_α + z_β)² p * (1-p)) / MDE²

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Test Duration Guidelines

Minimum Duration Rules

1. Absolute minimum: 7 days (capture weekly patterns) 2. Recommended minimum: 14 days 3. Maximum: 6-8 weeks (data decay)

Duration Formula

Days = (Sample Size per Variant × Number of Variants) / Daily Traffic to Test

Quick Duration Estimates

*Still run minimum 7 days for business cycle validity

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Test Types

A/B Test (Split Test)

• 2 variants (Control + Test)
• 50/50 traffic split
• Simplest to analyze
• Best for clear hypotheses

A/B/n Test

• 3+ variants
• Traffic split evenly
• Requires more sample
• Good for comparing multiple ideas

Multivariate Test (MVT)

• Multiple elements changed simultaneously
• Tests element combinations
• Requires MUCH larger sample
• Only for high-traffic sites

Sample Size for Multiple Variants

Sample per variant = Base sample / (Number of comparisons correction)
Bonferroni correction: α / number of comparisons

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Hypothesis Framework

PICO Format

P - Population (who are you testing?) I - Intervention (what are you changing?) C - Comparison (what's the control?) O - Outcome (what's the primary metric?)

Good Hypothesis Template

"If we [change], then [metric] will [improve/decrease] by [amount], because [rationale]."

Example: "If we change the CTA from 'Submit' to 'Get My Free Report', then form submissions will increase by 15%, because benefit-focused CTAs reduce friction and increase motivation."

Hypothesis Prioritization (ICE)

Priority = (I + C + E) / 3

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Common Pitfalls

Peeking Problem

Issue: Checking results early and stopping when significant Impact: 30%+ false positive rate Solution:

• Pre-commit to sample size
• Use sequential testing if must peek
• Set decision rules upfront

Multiple Comparison Problem

Issue: Testing many variants/metrics Impact: Inflated false positive rate Solution:

• Bonferroni correction (α / n tests)
• Designate ONE primary metric
• Pre-register secondary metrics

Novelty/Primacy Effect

Issue: New = initial spike, then decay Impact: False positives from early results Solution:

• Run full duration
• Segment new vs returning users
• Monitor for trend changes

Simpson's Paradox

Issue: Aggregate result hides segment differences Impact: Wrong winner for key segments Solution:

• Segment analysis
• Check for interaction effects
• Consider stratified randomization

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Analysis Framework

Step 1: Check Validity

• [ ] Sample size reached?
• [ ] Ran minimum 7 days?
• [ ] Sample Ratio Mismatch (SRM) check?
• [ ] Any technical issues during test?

Step 2: Primary Metric

• Conversion rate for each variant
• Lift (% change vs control)
• Confidence interval
• Statistical significance (p-value)

Step 3: Secondary Metrics

• Check for guardrail metric degradation
• Look for trade-offs
• Consider per-segment impact

Step 4: Segment Analysis

Step 5: Decision

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Test Velocity Optimization

Increasing Test Speed

1. Raise MDE - Accept detecting only larger effects 2. Lower confidence - Accept more risk (90% vs 95%) 3. Test high-traffic areas - Faster sample accumulation 4. Prioritize ruthlessly - Fewer, higher-impact tests

Testing Cadence

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Sample Size Calculators

Recommended Tools

1. Evan Miller - evanmiller.org/ab-testing/sample-size.html 2. Optimizely - optimizely.com/sample-size-calculator 3. Statsig - statsig.com/calculator 4. VWO - vwo.com/tools/duration-calculator 5. CXL - cxl.com/ab-test-calculator

Calculator Inputs

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Documentation Template

Test Brief

## Test: [Name]
**Hypothesis:** If we [change], then [metric] will [outcome] because [reason].

**Primary Metric:** [Metric name]
**Secondary Metrics:** [List]
**Guardrail Metrics:** [List]

**Audience:** [Who]
**Traffic Allocation:** [X%] Control / [Y%] Variant
**Duration:** [Start] to [End]
**Sample Size Target:** [Number per variant]

**Implementation Notes:** [Technical details]

Test Results

## Results: [Test Name]
**Duration:** [Start] to [End]
**Sample Size:** [Control: X, Variant: Y]

### Primary Metric: [Name]
| Variant | Rate | Lift | Confidence |
|---------|------|------|------------|
| Control | X% | - | - |
| Variant | Y% | +Z% | W% |

**Decision:** [Ship / Kill / Iterate / Inconclusive]
**Learnings:** [What we learned]
**Next Steps:** [Follow-up tests or actions]