A_B Test Design

A/B Test Design Skill

Overview

Specialized skill for statistical experiment design and analysis capabilities. Enables product teams to design rigorous experiments, calculate sample sizes, and interpret results with statistical confidence.

Capabilities

Experiment Design

• Calculate required sample sizes for experiments
• Design experiment variants and hypotheses
• Define success metrics and guardrail metrics
• Create experiment documentation templates
• Design multi-variant tests (A/B/n)
• Plan sequential and Bayesian experiments

Statistical Analysis

• Validate statistical significance of results
• Calculate practical significance and effect sizes
• Detect interaction effects and segments
• Perform power analysis
• Calculate confidence intervals
• Handle multiple comparison corrections

Decision Support

• Recommend ship/iterate/kill decisions
• Identify segment-specific impacts
• Assess long-term vs short-term effects
• Generate experiment reports
• Track experiment velocity metrics

Target Processes

This skill integrates with the following processes:

• product-market-fit.js - Validation experiments for PMF hypotheses
• conversion-funnel-analysis.js - Funnel optimization experiments
• beta-program.js - A/B testing during beta phases

Input Schema

{
  "type": "object",
  "properties": {
    "experimentType": {
      "type": "string",
      "enum": ["ab", "multivariate", "sequential", "bandit"],
      "description": "Type of experiment to design"
    },
    "hypothesis": {
      "type": "string",
      "description": "Hypothesis to test"
    },
    "primaryMetric": {
      "type": "object",
      "properties": {
        "name": { "type": "string" },
        "baseline": { "type": "number" },
        "mde": { "type": "number", "description": "Minimum detectable effect" }
      }
    },
    "guardrailMetrics": {
      "type": "array",
      "items": { "type": "string" },
      "description": "Metrics that should not regress"
    },
    "trafficAllocation": {
      "type": "number",
      "description": "Percentage of traffic for experiment"
    },
    "confidenceLevel": {
      "type": "number",
      "default": 0.95,
      "description": "Statistical confidence level"
    }
  },
  "required": ["experimentType", "hypothesis", "primaryMetric"]
}

Output Schema

{
  "type": "object",
  "properties": {
    "experimentPlan": {
      "type": "object",
      "properties": {
        "name": { "type": "string" },
        "hypothesis": { "type": "string" },
        "variants": { "type": "array", "items": { "type": "object" } },
        "sampleSize": { "type": "number" },
        "duration": { "type": "string" },
        "metrics": { "type": "object" }
      }
    },
    "powerAnalysis": {
      "type": "object",
      "properties": {
        "requiredSampleSize": { "type": "number" },
        "estimatedDuration": { "type": "string" },
        "power": { "type": "number" }
      }
    },
    "implementation": {
      "type": "object",
      "properties": {
        "trackingEvents": { "type": "array", "items": { "type": "string" } },
        "segmentation": { "type": "array", "items": { "type": "string" } },
        "rolloutPlan": { "type": "string" }
      }
    },
    "analysisFramework": {
      "type": "object",
      "properties": {
        "primaryAnalysis": { "type": "string" },
        "secondaryAnalyses": { "type": "array", "items": { "type": "string" } },
        "decisionCriteria": { "type": "object" }
      }
    }
  }
}

Usage Example

const experimentDesign = await executeSkill('ab-test-design', {
  experimentType: 'ab',
  hypothesis: 'Adding social proof to pricing page increases conversion by 10%',
  primaryMetric: {
    name: 'pricing_page_conversion',
    baseline: 0.05,
    mde: 0.10
  },
  guardrailMetrics: ['revenue_per_visitor', 'bounce_rate'],
  trafficAllocation: 50,
  confidenceLevel: 0.95
});

Dependencies

• Statistical libraries for power analysis
• Experimentation platform integrations (Optimizely, LaunchDarkly, etc.)