content-experimentation-best-practices

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:

• 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