Plan A/B tests with hypothesis frameworks and sample size calculations.
npx skills add https://github.com/lucasreydman/lloyd --skill ab-test-setup-lucasreydman
Please help me install this Agent Skill. Skill: ab-test-setup Source: https://github.com/lucasreydman/lloyd/tree/main/skills/ab-test-setup Command: npx skills add https://github.com/lucasreydman/lloyd --skill ab-test-setup-lucasreydman
Plan and run rigorous A/B tests to identify statistically valid improvements.
Plan and design your first test by defining goal, choosing a test type, setting baseline and MDE, calculate the required sample size, and document the hypothesis and plan.
High-intent search queries and answers about installing and using this skill.
To calculate sample size for an A/B test, you need to establish your baseline conversion rate, minimum detectable effect (MDE), and desired statistical significance level to ensure your test captures valid improvements.
A/B/n testing compares multiple independent variants against a control to find a single winning page, while multivariate testing (MVT) evaluates interactions between multiple elements on a single page simultaneously to identify optimal combinations.
Writing a hypothesis for conversion rate optimization requires defining the expected change, the target metric, and the rationale, ensuring your A/B test design enforces a structured framework for measurable validation.
Guardrail metrics in experimental design are secondary safety measures monitored during an A/B test to ensure variants do not negatively impact critical business health indicators like overall signup flow completion.
Use a split URL test instead of a standard A/B test when implementing fundamentally different page architectures or pricing layouts, requiring traffic allocation to separate URLs rather than dynamically swapping components.
Statistical significance in A/B testing is a mathematical measure indicating that the observed improvement in your primary metrics is likely not due to random chance, validating your experimental design results.
jmmLEvaluate test results and present four git integration options for a feature branch.
nimrodfisherCompute statistical significance, confidence intervals, and effect sizes for A/B test variants.
yoanbernabeuMonitor Product Hunt launch metrics including ranking, upvotes, comments, and traffic signals.
Gull-StackDesigns, plans, and analyzes A/B tests including hypothesis, metrics, and sample size calculations.
