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statistical-analysis

Apply statistical methods to analyze data distributions and interpret results.

Updated Mar 5, 2026
Search Tags:#data analysis#hypothesis testing#statistics#trend analysis#outlier detection#descriptive statistics
One-click install
npx skills add https://github.com/8GG-Git/knowledge-work-plugins --skill statistical-analysis-8gg-git
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/8GG-Git/knowledge-work-plugins/tree/main/data/skills/statistical-analysis
Command: npx skills add https://github.com/8GG-Git/knowledge-work-plugins --skill statistical-analysis-8gg-git

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you understand and interpret data by applying statistical methods to identify patterns, test hypotheses, and detect anomalies.

Core Features & Use Cases

  • Descriptive Statistics: Calculate measures of central tendency (mean, median, mode) and spread (standard deviation, IQR) to summarize data.
  • Trend Analysis: Identify and forecast trends in time-series data using moving averages and period-over-period comparisons.
  • Outlier Detection: Find and handle unusual data points using statistical methods like Z-scores and IQR.
  • Hypothesis Testing: Determine if observed differences in data are statistically significant, crucial for A/B testing and segment analysis.
  • Use Case: Analyze customer engagement metrics to understand daily trends, identify unusual spikes or drops in activity, and test if a new feature significantly increased user retention.

Quick Start

Analyze the attached 'user_data.csv' file to compute descriptive statistics for the 'session_duration' column.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I calculate descriptive statistics for a CSV dataset?▼

To calculate descriptive statistics for a CSV dataset, the Skill computes measures of central tendency like mean and median, alongside spread metrics such as standard deviation and IQR, to summarize your data distributions effectively.

What is the best way to detect outliers in my data?▼

The best way to detect outliers involves applying statistical methods like Z-scores and IQR to identify unusual data points. This helps find and handle anomalies accurately within your dataset distributions.

How do I run hypothesis testing for A/B testing analysis?▼

To run hypothesis testing for A/B testing, the Skill determines if observed differences in your data are statistically significant. It applies statistical methods to interpret results and validate your segment analysis.

Can I identify time-series trends using moving averages?▼

Yes, you can identify time-series trends using moving averages. The Skill performs trend analysis by applying moving averages and period-over-period comparisons to forecast patterns in your data.

Does this statistical analysis tool require Python libraries?▼

Yes, this statistical analysis tool requires Python libraries for calculations and visualizations. It utilizes these libraries to apply statistical methods and provide actionable insights for business analysts.

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