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