Content experimentation and A/B testing guidance covering experiment design, hypotheses, metrics, sample size, statistical foundations, CMS-managed variants, and common analysis pitfalls. Use this skill when planning experiments, setting up variants, choosing success metrics, interpreting statistica

Content Experimentation Best Practices Principles and patterns for running effective content experiments to improve conversion rates, engagement, and user experience. When to Apply Reference these guidelines when: Setting up A/B or multivariate testing infrastructure Designing experiments for content changes Analyzing and interpreting test results Building CMS integrations for experimentation Deciding what to test and how Core Concepts A/B Testing Comparing two variants (A vs B) to determine which performs better. Multivariate Testing Testing multiple variables simultaneously to find optimal combinations. Statistical Significance The confidence level that results aren't due to random chance. Experimentation Culture Making decisions based on data rather than opinions (HiPPO avoidance). References Start with the reference that matches the current problem, such as design, statistics, CMS integration, or pitfalls. See references/ for detailed guidance: references/experiment design.md — Hypothesis framework, metrics, sample size, and what to test references/statistical foundations.md — p values, confidence intervals, power analysis, Bayesian methods references/cms integration.md — CMS managed variants, field level variants, external platforms references/common pitfalls.md — 17 common mistakes across statistics, design, execution, and interpretation

content-experimentation-best-practices