content-experimentation-best-practices
Strukturierte Anleitung für die Konzeption, Durchführung und Analyse von Content-Experimenten zur Verbesserung von Conversion und Engagement. Behandelt Hypothesen-Frameworks, Metrikauswahl, Stichprobengrößenberechnung und statistische Signifikanztests bei A/B- und multivariaten Experimenten. Enthält detaillierte Ressourcen zu p-Werten, Konfidenzintervallen, Power-Analyse und Bayes'schen Methoden zur Ergebnisinterpretation. Bietet CMS-Integrationsmuster für die Verwaltung von Varianten auf Feldebene und die Anbindung externer...
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