content-experimentation-best-practices

作者： sanity-io

結構化指引，涵蓋設計、執行與分析內容實驗以提升轉換率與參與度。內容包括假設框架、指標選擇、樣本數計算，以及A/B測試與多變量實驗中的統計顯著性檢定。提供關於p值、信賴區間、統計檢定力分析及貝氏方法的詳細資源，用於解讀實驗結果。同時包含CMS整合模式，以便在欄位層級管理變體，並連接外部系統。

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