content-experimentation-best-practices
Orientação estruturada para projetar, executar e analisar experimentos de conteúdo visando melhorar conversão e engajamento. Abrange frameworks de hipóteses, seleção de métricas, cálculo de tamanho amostral e testes de significância estatística em experimentos A/B e multivariados. Inclui recursos detalhados sobre valores-p, intervalos de confiança, análise de poder e métodos bayesianos para interpretação de resultados. Fornece padrões de integração com CMS para gerenciar variantes no nível de campo e conectar sistemas externos...
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