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statistical-analysis

Summarize data, detect anomalies, and assess significance with statistical methods.

1|Updated Apr 2, 2026
Search Tags:#data-quality#hypothesis-testing#statistical-analysis#trend-analysis#descriptive-statistics#business-intelligence#outlier-detection
One-click install
npx skills add https://github.com/kongaharsha/claude-skills --skill statistical-analysis-kongaharsha
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/kongaharsha/claude-skills/tree/main/statistical-analysis
Command: npx skills add https://github.com/kongaharsha/claude-skills --skill statistical-analysis-kongaharsha

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data teams need practical, rigorous methods to summarize data, detect anomalies, and test claims without misinterpretation.

Core Features & Use Cases

  • Descriptive statistics to summarize distributions
  • Trend analysis and basic forecasting guidance
  • Outlier and anomaly detection with robust reporting
  • Hypothesis testing guidelines and interpretation notes
  • Real-world example: evaluating changes in user engagement metrics across cohorts

Quick Start

Run a quick statistical check by computing descriptive statistics and performing a simple hypothesis test on the provided dataset.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I perform hypothesis testing on user engagement metrics across different cohorts?▼

Hypothesis testing across cohorts involves comparing engagement distributions to assess statistical significance. This Skill enforces structured guidance for interpreting test results, reporting effect sizes, and calculating confidence intervals to prevent data misinterpretation.

What is the best way to detect outliers and anomalies in my time-series data?▼

Outlier detection in time-series data identifies anomalies that deviate from expected trend distributions. This Skill applies robust statistical reporting methods to flag these anomalies and summarize their potential impact on your overall dataset.

Can I use descriptive statistics to summarize data distributions without advanced statistical knowledge?▼

Descriptive statistics summarize data distributions without requiring advanced statistical knowledge. This Skill computes practical statistical methods to provide clear insights into your data's central tendencies, variability, and overall structure.

How do I apply trend analysis and basic forecasting to my business intelligence datasets?▼

Trend analysis and basic forecasting apply statistical methods to time-series business intelligence data to identify directional patterns. This Skill guides you through evaluating these trends while cautioning against making unsubstantiated statistical claims.

What are the limitations of statistical analysis when evaluating changes in user engagement?▼

Statistical analysis limitations arise when evaluating engagement changes without proper context, risking misinterpreted significance. This Skill cautions against overstating statistical claims and enforces best practices for reporting effect sizes and confidence intervals to ensure accurate conclusions.

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