content-experimentation-best-practices

SKILL.md

Structured guidance for designing, executing, and analyzing content experiments to improve conversion and engagement.

Covers hypothesis frameworks, metric selection, sample size calculation, and statistical significance testing across A/B and multivariate experiments

Includes detailed resources on p-values, confidence intervals, power analysis, and Bayesian methods for interpreting results

Provides CMS integration patterns for managing variants at the field level and connecting external experimentation platforms

Documents 17 common pitfalls in experiment design, statistical analysis, execution, and result interpretation to avoid flawed conclusions

SKILL.md
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.