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statistical-analysis

Perform t-tests, ANOVA, and Chi-Square tests on datasets.

17|5|Updated Feb 6, 2026
Search Tags:#A/B testing#time series#data analysis#hypothesis testing#statistics#experimental design
One-click install
npx skills add https://github.com/Logos-Liber/Atlas-Agent-Teams --skill statistical-analysis-logos-liber
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/Logos-Liber/Atlas-Agent-Teams/tree/main/teams/data-science/skills/statistical-analysis
Command: npx skills add https://github.com/Logos-Liber/Atlas-Agent-Teams --skill statistical-analysis-logos-liber

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a comprehensive toolkit for understanding data through statistical methods, hypothesis testing, and experimental design, enabling data-driven decision-making.

Core Features & Use Cases

  • Statistical Methods: Perform descriptive and inferential statistics, including correlation and distribution analysis.
  • Hypothesis Testing: Conduct t-tests, ANOVA, and Chi-Square tests to validate hypotheses.
  • A/B Testing: Design and analyze experiments for product and feature optimization.
  • Time Series Analysis: Forecast trends and understand patterns in sequential data.
  • Use Case: A product manager can use this Skill to analyze user engagement data, perform an A/B test on a new feature, and forecast future user growth.

Quick Start

Use the statistical-analysis skill to perform a t-test on the provided dataset to compare the means of two groups.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I design and analyze an A/B test for product feature optimization?▼

You can design and analyze A/B tests for product optimization by applying experimental design principles and rigorous data interpretation. This Skill provides the necessary frameworks to validate assumptions and evaluate feature performance.

What statistical methods are used for hypothesis testing and validating assumptions?▼

Hypothesis testing validates assumptions using inferential statistical methods like t-tests, ANOVA, and Chi-Square tests. This Skill provides these frameworks to compare group means and confirm data-driven hypotheses.

Can I perform time series analysis to forecast trends in sequential data?▼

Yes, you can perform time series analysis to forecast trends and understand patterns in sequential data. This Skill provides time-dependent data modeling capabilities to address analytical forecasting requirements.

When do I need experimental design principles for my data analysis workflow?▼

You need experimental design principles when setting up analytical workflows requiring rigorous data interpretation and assumption validation. This Skill satisfies requirements for experimental setup before inferential testing begins.

What is the best way to analyze descriptive statistics and correlations in my dataset?▼

The best way to analyze descriptive statistics and correlations is to use a comprehensive suite of statistical methods for distribution analysis. This Skill enables you to perform these descriptive and inferential statistics on your dataset.

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