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sample-size

Compute sample size and experiment duration for A/B tests.

1|Updated Mar 19, 2026
Search Tags:#python#statistics#ab-testing#sample-size#power-analysis#experiment-design
One-click install
npx skills add https://github.com/weisberg/agile_agentic_analytics --skill sample-size
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sample-size
Source: https://github.com/weisberg/agile_agentic_analytics/tree/main/plugins/ab-testing/skills/sample-size
Command: npx skills add https://github.com/weisberg/agile_agentic_analytics --skill sample-size

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill computes the required sample size and estimated duration for A/B tests, enabling rigorous planning and guardrails.

Core Features & Use Cases

  • Calculate per-variant sample size for both proportions and continuous outcomes
  • Provide total sample size, duration estimates, and a sensitivity table
  • Produce reproducible Python code to validate results

Quick Start

Provide baseline rate (or mean and standard deviation for continuous metrics), minimum detectable effect, alpha, power, number of variants, and optional daily traffic to compute the results.

Frequently Asked Questions about sample-size

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

FAQPage Schema
How do I calculate sample size for an A/B test with proportions and continuous metrics?▼

To calculate A/B test sample size, provide the baseline rate or mean and standard deviation, minimum detectable effect, alpha, power, and number of variants to compute per-variant and total required sizes.

What inputs do I need to estimate A/B test duration and traffic requirements?▼

Estimating A/B test duration requires baseline rate, minimum detectable effect, alpha, power, number of variants, and optional daily traffic to calculate the total sample size and estimated experiment run time.

Can I compute power analysis for experiments with multiple variants?▼

Yes, power analysis supports multiple variants by accepting the number of variants as an input, adjusting the required per-variant and total sample size to maintain the specified alpha and power levels.

How do I run a sensitivity analysis for minimum detectable effect in experiment design?▼

Sensitivity analysis for experiment design is generated automatically, providing a sensitivity table that shows how varying the minimum detectable effect impacts the required sample size and test duration.

Does the sample size calculation provide reproducible Python code for validation?▼

Yes, the sample size calculation outputs reproducible Python code, allowing you to independently validate the computed per-variant size, duration estimates, and statistical parameters.

What statistical parameters are needed for A/B testing power analysis?▼

A/B testing power analysis requires alpha, power, baseline conversion rate or mean with standard deviation, minimum detectable effect, and variant count to compute experiment guardrails and sample sizes.

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