content-experimentation-best-practices

作者： sanity-io

结构化指导，用于设计、执行和分析内容实验，以提升转化率和参与度。涵盖假设框架、指标选择、样本量计算以及A/B和多变量实验中的统计显著性检验。包含关于p值、置信区间、功效分析和贝叶斯方法的详细资源，用于解读结果。提供CMS集成模式，用于在字段级别管理变体并连接外部...

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