content-experimentation-best-practices

par sanity-io

Conseils structurés pour concevoir, exécuter et analyser des expériences de contenu afin d’améliorer la conversion et l’engagement. Couvre les cadres d’hypothèses, la sélection des métriques, le calcul de la taille de l’échantillon et les tests de signification statistique pour les expériences A/B et multivariées. Inclut des ressources détaillées sur les valeurs p, les intervalles de confiance, l’analyse de puissance et les méthodes bayésiennes pour interpréter les résultats. Fournit des modèles d’intégration CMS pour gérer les variantes au niveau du champ et connecter des systèmes externes...

npx skills add https://github.com/sanity-io/agent-toolkit --skill content-experimentation-best-practices

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Content 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 test
references/statistical-foundations.md — p-values, confidence intervals, power analysis, Bayesian methods
references/cms-integration.md — CMS-managed variants, field-level variants, external platforms
references/common-pitfalls.md — 17 common mistakes across statistics, design, execution, and interpretation