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).

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

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.