Content Experimentation Best Practices
Solid foundation for anyone running A/B tests on content who wants to avoid the usual statistical traps. Covers the full cycle from hypothesis formation and sample size calculations through statistical significance and interpretation. The 17 common pitfalls reference is genuinely useful, the kind of thing you'd want open when explaining to a PM why their test needs to run another week. Includes practical CMS integration patterns for managing variants at the field level. Best suited for teams moving past "let's just try this and see what happens" toward structured experimentation. The statistical foundations section will save you from p-hacking yourself into bad decisions.
- skills.sh installs
- 4K
- GitHub stars
- 197
- last updated
- Aug 21, 2026
Install
$ npx -y skills add sanity-io/agent-toolkit --skill content-experimentation-best-practices --agent claude-codeInstalls into .claude/skills of the current project.
Files
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
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