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TarzanGhimire
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ads-test

Design A/B tests and calculate statistical significance for ad platform experiments.

Updated May 24, 2026
Search Tags:#experiment design#ad optimization#ab test#marketing analysis#ads test
One-click install
npx skills add https://github.com/TarzanGhimire/Claude-Ads-Skill --skill ads-test-tarzanghimire
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ads-test
Source: https://github.com/TarzanGhimire/Claude-Ads-Skill/tree/main/skills/ads-test
Command: npx skills add https://github.com/TarzanGhimire/Claude-Ads-Skill --skill ads-test-tarzanghimire

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

The ads-test Skill simplifies A/B test design and experiment planning, providing structured hypothesis frameworks, statistical significance calculators, test duration estimators, and platform-specific setup guides for various ad platforms.

Core Features & Use Cases

  • A/B Test Design Framework: Structured framework for building hypothesis, including examples.
  • Statistical Significance Calculator: Calculates required sample size based on MDE and confidence levels.
  • Test Duration Estimator: Calculates duration based on sample size and daily traffic.
  • Platform-Specific Setup: Guides for Meta, Google, LinkedIn, TikTok, Microsoft, Apple, and Amazon ad experiments.
  • Use Case: If you're planning an A/B test on Facebook, the ads-test Skill will provide step-by-step guidance tailored for Facebook’s platform.

Quick Start

Generate a test plan for a Meta experiment to test two ad variations with the 'ads-test' skill.

Frequently Asked Questions about ads-test

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

FAQPage Schema
How do I design an A/B test for ad optimization?▼

To design an ad optimization A/B test, use structured hypothesis creation frameworks, estimate required sample size based on minimum detectable effect, and calculate test duration from daily traffic to ensure statistical significance.

What's the best way to calculate statistical significance for marketing analysis?▼

Calculate statistical significance for marketing analysis by determining the required sample size using your minimum detectable effect and desired confidence levels before evaluating the advertising experiment results.

Does this ads test framework support platform-specific setup for Meta and Google?▼

Yes, the ads test framework provides detailed platform-specific experiment setup guides for Meta, Google, LinkedIn, TikTok, Microsoft, Apple, and Amazon ad platforms to ensure accurate test execution.

How do I estimate test duration for an advertising experiment?▼

Estimate advertising experiment test duration by dividing the required sample size by your expected daily traffic volume, ensuring the window is long enough to achieve valid statistical significance.

Can I use this A/B test design without prior statistical principles knowledge?▼

No, effective A/B test design requires familiarity with statistical principles for accurate hypothesis development and significance assessment, alongside basic knowledge of your chosen ad platform UI.

Why does my ad optimization experiment require a minimum detectable effect?▼

Ad optimization experiments require a minimum detectable effect to calculate the necessary sample size accurately, ensuring your statistical significance calculator has enough data to validate the hypothesis.

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