Guide A/B and MVT experiment planning with hypothesis, sample size, and metrics.
npx skills add https://github.com/DevMatt6/marknet --skill ab-testing-devmatt6
Please help me install this Agent Skill. Skill: ab-testing Source: https://github.com/DevMatt6/marknet/tree/main/.agents/skills/ab-testing Command: npx skills add https://github.com/DevMatt6/marknet --skill ab-testing-devmatt6
This Skill aids in the design, planning, and implementation of A/B tests and growth experimentation programs, helping users make data-driven decisions and improve their testing processes.
Use the ab-testing skill to get advice on setting up an A/B test for a new feature.
High-intent search queries and answers about installing and using this skill.
To calculate A/B test sample size, you must define your primary metric, expected effect size, and significance level. This Skill assists by structuring those inputs to determine the required sample size for valid statistical results.
Structuring hypotheses for growth experimentation requires documenting the expected impact and rationale before execution. This Skill provides a structured hypothesis framework to formulate and document your testing predictions systematically.
Selecting metrics for an A/B test involves defining primary targets, secondary indicators, and guardrails to prevent negative side effects. This Skill helps define these distinct metric categories to drive data-informed decision making.
Yes, this framework supports both A/B/n tests and multivariate tests (MVT). It guides the planning and execution of these statistical experiments, allowing you to test multiple variants simultaneously for data-informed decisions.
A structured A/B test design framework is needed when you want to ensure statistical validity and drive data-informed decision making. This Skill provides templates for structuring tests to properly interpret results and improve your testing process.
mrsknetworkPlan statistically sound A/B tests with sample-size calculations and metric selection.
w95Apply descriptive statistics, trend analysis, outlier detection, and hypothesis testing to datasets.
proffesor-for-testingGenerate and index test reporting analytics for QE metrics in CI/CD pipelines.
olavocarvalhoAnalyze A/B test data with Frequentist and Bayesian significance testing.
