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ab-testing

Design, execute, and analyze statistically valid A/B tests with sample size calculations.

Updated May 28, 2026
Search Tags:#ab-testing#hypothesis-testing#growth-experiments#conversion-optimization#statistical-significance#ice-scoring#experimentation-program
One-click install
npx skills add https://github.com/marcosbatallas99/Marketing --skill ab-testing-marcosbatallas99
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ab-testing
Source: https://github.com/marcosbatallas99/Marketing/tree/main/skills/ab-testing
Command: npx skills add https://github.com/marcosbatallas99/Marketing --skill ab-testing-marcosbatallas99

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the risk of running poorly designed A/B tests that produce inconclusive or misleading results, which wastes growth and marketing team resources and leads to suboptimal product decisions.

Core Features & Use Cases

  • Structured Hypothesis Framing: Uses a proven hypothesis framework to ensure every test is tied to a clear, measurable business outcome.
  • Statistical Rigor Tools: Provides sample size calculators, duration guidelines, and peeking problem mitigation to ensure test results are reliable.
  • End-to-End Experimentation Programs: Supports building systematic growth practices with ICE prioritization, experiment playbooks, and velocity tracking. Use case example: A growth marketer can use this Skill to design a pricing page CTA test, calculate the required sample size based on current traffic and baseline conversion rate, define guardrail metrics, and document learnings in a standardized playbook for future tests.

Quick Start

Use the ab-testing skill to plan a statistically valid A/B test for our homepage signup flow, including a structured hypothesis, required sample size, and guardrail metrics.

Frequently Asked Questions about ab-testing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate the required sample size for an A/B test to ensure statistical significance?▼

A/B test sample size calculation requires your current traffic volume and baseline conversion rate to determine the exact duration needed to reach a 95% confidence threshold and avoid false positives.

How do I structure a hypothesis for conversion optimization experiments?▼

Hypothesis framing for conversion optimization requires tying every test to a clear, measurable business outcome using a structured framework, ensuring your growth experiments produce actionable learnings rather than inconclusive results.

What is the best way to prioritize growth experiments for a marketing team?▼

Growth experiments are best prioritized using ICE scoring, which ranks test ideas within an experimentation program by impact, confidence, and ease to systematically allocate marketing resources to high-value tests.

How do I set up guardrail metrics for a pricing page experiment?▼

Guardrail metrics for a pricing page experiment are defined within a three-tier metric framework to monitor secondary business outcomes, preventing a CTA test from accidentally degrading overall revenue or user retention.

Can I use this approach for multivariate testing on a landing page CTA?▼

Multivariate testing on landing page CTAs is fully supported, applying the same statistical rigor tools, hypothesis framing, and standardized playbook documentation used for standard A/B tests to evaluate multiple variables simultaneously.

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