v1.0.0 to v1.0.0
4 added, 4 removed. Audit A to A.
---
name: content-experimentation-best-practices
description: A/B testing and content experimentation methodology for data-driven content optimization. Use when implementing experiments, analyzing results, or building experimentation infrastructure.
license: MIT
metadata:
author: sanity
version: "1.0.0"
---
# 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
See `resources/` for detailed guidance:
- - Experiment design principles
- - Statistical foundations
- - CMS integration patterns
- - Common pitfalls
+ - `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