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statistical-analysis

Perform descriptive statistics, trend analysis, outlier detection, and hypothesis testing on datasets.

Updated Feb 12, 2026
Search Tags:#data analysis#hypothesis testing#statistics#forecasting#trend analysis#outlier detection
One-click install
npx skills add https://github.com/Bauhaus-InfAU/infau-skill-base --skill statistical-analysis-bauhaus-infau
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/Bauhaus-InfAU/infau-skill-base/tree/main/data/skills/statistical-analysis
Command: npx skills add https://github.com/Bauhaus-InfAU/infau-skill-base --skill statistical-analysis-bauhaus-infau

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps users understand their data by applying statistical methods, identifying trends, detecting anomalies, and testing hypotheses, making complex data analysis accessible.

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 patterns and forecast future values 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 are statistically significant using t-tests, chi-squared tests, and more.
  • Use Case: Analyze monthly sales data to understand growth trends, identify any unusual spikes or dips, and determine if a recent marketing campaign had a statistically significant impact on revenue.

Quick Start

Analyze the provided sales data to calculate descriptive statistics and identify any trends.

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 to check if a marketing campaign impacted revenue?▼

Hypothesis testing determines if observed differences in revenue are statistically significant using methods like t-tests and chi-squared tests. You can validate whether a marketing campaign had a real impact on your sales data.

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

Outlier detection identifies unusual data points using statistical methods like Z-scores and IQR. This helps you find and handle anomalies in your dataset to ensure accurate quantitative analysis.

How do I calculate descriptive statistics like mean and standard deviation for trend analysis?▼

Descriptive statistics calculate measures of central tendency and spread like mean, median, mode, and standard deviation. Summarizing this data provides the baseline needed to identify patterns and analyze trends.

Can I forecast future values using period-over-period comparisons?▼

Forecasting future values uses moving averages and period-over-period comparisons to identify patterns. This trend analysis approach helps you understand growth trajectories from historical quantitative information.

Do I need a specific platform to run statistical analysis on my sales data?▼

No specific platform is required as there are no dependencies. You can analyze your sales data directly to calculate descriptive statistics and identify trends using robust statistical methods.

When should I use t-tests versus chi-squared tests for statistical significance?▼

T-tests and chi-squared tests are both used for hypothesis testing to determine statistical significance. Choose based on your data distribution and whether you are comparing means or categorical variables.

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