content-experimentation-best-practices
コンテンツ実験の設計、実行、分析に関する構造化されたガイダンスで、コンバージョンとエンゲージメントを向上させます。仮説フレームワーク、指標の選択、サンプルサイズの計算、A/Bテストや多変量実験における統計的有意性検定を網羅。p値、信頼区間、検出力分析、結果解釈のためのベイズ手法に関する詳細なリソースを提供。フィールドレベルでバリアントを管理し、外部と接続するためのCMS統合パターンを含みます。
npx skills add https://github.com/sanity-io/agent-toolkit --skill content-experimentation-best-practicesContent 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 testreferences/statistical-foundations.md— p-values, confidence intervals, power analysis, Bayesian methodsreferences/cms-integration.md— CMS-managed variants, field-level variants, external platformsreferences/common-pitfalls.md— 17 common mistakes across statistics, design, execution, and interpretation