ab-test-setup

When the user wants to plan, design, or implement an A/B test or experiment. 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," or "how long should I run this test." Use this whenever someone is comparing two approaches and wants to measure which performs better. For tracking implementation, see analytics-tracking. For page-level conversion optimization, see page-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-context.md exists (or .agents/product-marketing-context.md 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:

  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?

Core Principles

1. Start with a Hypothesis

2. Test One Thing

3. Statistical Rigor

4. Measure What Matters

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

Secondary Metrics

Guardrail Metrics

Example: Pricing Page Test

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

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:

Implementation

Client-Side

Server-Side

Running the Test

Pre-Launch Checklist

During the Test

DO:

Avoid:

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

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 |

Documentation

Document every test with:

For templates: See references/test-templates.md

Common Mistakes

Test Design

Execution

Analysis

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