git:20260305.80872f6 to git:20260407.ad50ea4
6 added, 6 removed. Audit A to A.
---
name: content-experimentation-best-practices
description: Content experimentation and A/B testing guidance covering experiment design, hypotheses, metrics, sample size, statistical foundations, CMS-managed variants, and common analysis pitfalls. Use this skill when planning experiments, setting up variants, choosing success metrics, interpreting statistical results, or building experimentation workflows in a CMS or frontend stack.
---
# 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).
- ## Resources
+ ## References
- Start with the resource that matches the current problem, such as design, statistics, CMS integration, or pitfalls. See `resources/` for detailed guidance:
- - `resources/experiment-design.md` — Hypothesis framework, metrics, sample size, and what to test
- - `resources/statistical-foundations.md` — p-values, confidence intervals, power analysis, Bayesian methods
- - `resources/cms-integration.md` — CMS-managed variants, field-level variants, external platforms
- - `resources/common-pitfalls.md` — 17 common mistakes across statistics, design, execution, and interpretation
+ 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