Design statistically valid A/B tests with sample sizes and metrics.
npx skills add https://github.com/MileniumTick/skills --skill ab-test-setup-mileniumtick
Please help me install this Agent Skill. Skill: ab-test-setup Source: https://github.com/MileniumTick/skills/tree/main/skills/ab-test-setup Command: npx skills add https://github.com/MileniumTick/skills --skill ab-test-setup-mileniumtick
This skill helps users design, plan, and execute statistically valid A/B tests to compare two approaches and measure impact, enabling data-driven product decisions.
Create a test plan by formulating a clear hypothesis, selecting a test type, and documenting metrics and sample size using the provided templates.
High-intent search queries and answers about installing and using this skill.
To calculate the required sample size for an A/B test, you need to define your primary metrics, expected effect size, and statistical significance thresholds. This skill provides templates to compute sample sizes and plan tests that isolate changes for product features, pricing pages, and onboarding flows.
A/B and A/B/n tests compare two or more distinct variants, MVT evaluates multiple variable combinations simultaneously, and split URL tests route traffic to entirely different pages. This skill helps you select the appropriate test type based on your hypothesis and product experimentation requirements.
To set up metrics for experimentation, define primary metrics to measure the main goal, secondary metrics for additional insights, and guardrail metrics to protect business outcomes. This skill provides templates to structure these metrics and ensure statistically valid product experiments.
Yes, you can use this skill to design A/B tests for pricing pages and onboarding flows. It supports formulating clear hypotheses, selecting test types, and documenting metrics and sample sizes to compare variants and decide which performs better for data-driven product decisions.
The best way to construct an A/B test hypothesis is to clearly define the expected change, the target metric, and the predicted outcome. This skill provides end-to-end guidance and runbooks to build hypothesis-driven test designs that isolate changes and measure impact effectively.
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