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content-experimentation-best-practices▌

sanity-io/agent-toolkit · updated Apr 8, 2026

$npx skills add https://github.com/sanity-io/agent-toolkit --skill content-experimentation-best-practices
summary

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 exter
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.

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

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