name: ab-testing description: 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. metadata: version: 2.0.2

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

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

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

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

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)
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:

## [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

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

Install

Related skills