Design, execute, and analyze A/B tests with statistical rigor.
npx skills add https://github.com/andginja/marketingskills --skill ab-test-setup-andginja
Please help me install this Agent Skill. Skill: ab-test-setup Source: https://github.com/andginja/marketingskills/tree/main/skills/ab-test-setup Command: npx skills add https://github.com/andginja/marketingskills --skill ab-test-setup-andginja
This Skill helps you design, execute, and analyze A/B tests with statistical rigor, ensuring your experiments yield reliable insights and avoid common pitfalls.
Help me design an A/B test to increase sign-ups on our landing page by formulating a hypothesis and calculating the required sample size.
High-intent search queries and answers about installing and using this skill.
Calculating A/B test sample size and duration requires determining the necessary visitor count and test runtime to achieve statistical validity and avoid underpowered tests. This Skill provides tables and tools to compute these metrics for reliable conversion rate optimization.
Common A/B testing mistakes include peeking at results early, running multiple comparisons without adjustment, and using underpowered tests. This Skill educates on these statistical errors and offers solutions to ensure your experimentation yields reliable insights.
Formulating a strong A/B testing hypothesis involves using a structured template to create clear, testable predictions for growth marketing. This Skill guides you through hypothesis formation to ensure your conversion rate optimization experiments are statistically sound.
Prioritizing A/B test ideas can be done using ICE, PIE, or RICE frameworks to rank experiments by potential impact, confidence, and feasibility. This Skill implements these prioritization frameworks to help you sequence your growth marketing tests effectively.
Yes, this Skill supports interpreting A/B test results using both frequentist and Bayesian statistical approaches. It guides you through hypothesis testing and experimentation analysis using whichever method suits your data.
You should avoid A/B testing when you lack the traffic to reach the required sample size, leading to underpowered tests and inconclusive statistics. This Skill helps identify these edge cases to prevent wasted experimentation effort.
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