Ab Test Setup

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

Sample Size

Quick Reference

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

Best Practices

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

Traffic Allocation

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

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:

ICE Prioritization

Score each hypothesis 1-10 on three dimensions:

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:

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

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