content-experimentation-best-practices
Dönüşüm ve etkileşimi artırmak için içerik deneyleri tasarlama, yürütme ve analiz etme konusunda yapılandırılmış rehberlik. Hipotez çerçeveleri, metrik seçimi, örneklem büyüklüğü hesaplama ve A/B ile çok değişkenli deneylerde istatistiksel anlamlılık testini kapsar. Sonuçları yorumlamak için p-değerleri, güven aralıkları, güç analizi ve Bayes yöntemleri hakkında ayrıntılı kaynaklar sunar. Varyantları alan düzeyinde yönetmek ve harici bağlantı kurmak için CMS entegrasyon modelleri sağlar...
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