content-experimentation-best-practices
콘텐츠 실험을 설계, 실행, 분석하여 전환율과 참여도를 개선하기 위한 체계적인 가이드입니다. 가설 프레임워크, 지표 선택, 표본 크기 계산, A/B 및 다변량 실험의 통계적 유의성 검정을 다룹니다. p-값, 신뢰 구간, 검정력 분석, 결과 해석을 위한 베이지안 방법에 대한 상세 자료를 포함합니다. 필드 수준에서 변형을 관리하고 외부 시스템과 연결하기 위한 CMS 통합 패턴을 제공합니다.
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