Ab Testing Analysis

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

A/B testing (also known as split testing) is a controlled experiment where two variants (A and B) are compared to determine which performs better for a specific metric. Effective A/B testing uses proper randomization, statistical significance, and clear success metrics to make data-driven decisions, enabling teams to validate assumptions and optimize user experiences based on statistical evidence rather than opinions.

Why This Matters

• Remove Guesswork: Test assumptions instead of relying on opinions or intuition
• Measure Real Impact: Quantify the effect of changes on user behavior with statistical confidence
• Optimize Continuously: Make incremental improvements over time through iterative testing
• Justify Decisions: Support decisions with statistical evidence for stakeholder buy-in
• Reduce Risk: Test changes before full rollout to avoid negative impacts
• Learn About Users: Gain insights into user preferences and behavior patterns

Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • Baseline conversion rate or metric value
  • Minimum detectable effect (MDE)
  • Significance level (α, typically 0.05)
  • Statistical power (1-β, typically 0.80)
  • Daily traffic or user volume
  • Test variants (Control A, Treatment B)
• Entry Conditions:
  • Tracking infrastructure in place (analytics, event tracking)
  • Randomization mechanism implemented
  • Primary metric defined and measurable
  • Guardrail metrics identified
• Outputs:
  • Sample size calculation (per variant and total)
  • Test duration estimate
  • Statistical analysis results (p-value, confidence interval, lift)
  • Recommendation (Ship, Iterate, Abandon)
  • Test report with findings and learnings
• Artifacts Required (Deliverables):
  • Hypothesis document
  • Test configuration (variants, traffic split)
  • Sample size calculation
  • Statistical analysis report
  • Recommendation document
• Acceptance Evidence:
  • Statistical significance achieved (p < 0.05)
  • Required sample size reached
  • Guardrail metrics not negatively impacted
  • Documented decision with rationale
• Success Criteria:
  • p-value < 0.05 for statistically significant results
  • Confidence interval does not cross zero
  • Practical significance (meaningful business impact)
  • No negative impact on guardrail metrics

Skill Composition

• Depends on: Dashboard Design, KPI Metrics, SQL for Analytics
• Compatible with: Feature Toggles, Data Collection
• Conflicts with: None
• Related Skills: dashboard-design, kpi-metrics, sql-for-analytics

Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions / Constraints / Non-goals

• Assumptions:
  • Development environment is properly configured
  • Required dependencies are available
  • Team has basic understanding of domain
• Constraints:
  • Must follow existing codebase conventions
  • Time and resource limitations
  • Compatibility requirements
• Non-goals:
  • This skill does not cover edge cases outside scope
  • Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:
  • Python 3.8+
  • Node.js 16+
  • Modern browsers (Chrome, Firefox, Safari, Edge)
• Required AI Tools:
  • Code editor (VS Code recommended)
  • Testing framework appropriate for language
  • Version control (Git)
• Dependencies:
  • Language-specific package manager
  • Build tools
  • Testing libraries
• Environment Setup:
  • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/pagination for large data
• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/Typecheck passed
• Logging/Metrics/Trace implemented
• Security checks passed
• Documentation/Changelog updated
• Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries
• ⚠️ Watch out for: Common symptoms and quick fixes
• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure