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content-experimentation-best-practices

Skill5 starsChanged 22 days agoMITView on GitHub

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.

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SKILL.md

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

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