A/B testing and content experimentation methodology for data-driven content optimization. Use when implementing experiments, analyzing results, or building experimentation infrastructure.
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--- name: content-experimentation-best-practices description: A/B testing and content experimentation methodology for data-driven content optimization. Use when implementing experiments, analyzing results, or building experimentation infrastructure. license: MIT metadata: author: sanity version: "1.0.0" --- # 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). ## Resources See `resources/` for detailed guidance: - Experiment design principles - Statistical foundations - CMS integration patterns - Common pitfalls