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Data Analysis · Business · Testing / AGENT SKILL
content-experimentation-best-practices
0
Plan and analyze content A/B tests with hypotheses, success metrics, sample sizes, and CMS variants.
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 statistical results, or building experimentation workflows in a CMS or frontend stack.
Understand the trade-offs.
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# 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