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adrianliechti
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statistical-analysis

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

4|4|Updated Dec 15, 2024
Search Tags:#hypothesis testing#trend analysis#data interpretation#business metrics#statistical-analysis#outlier detection#descriptive statistics
One-click install
npx skills add https://github.com/adrianliechti/wingman-chat --skill statistical-analysis-adrianliechti
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/adrianliechti/wingman-chat/tree/main/skills/data/statistical-analysis
Command: npx skills add https://github.com/adrianliechti/wingman-chat --skill statistical-analysis-adrianliechti

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.

Core Features & Use Cases

  • Descriptive statistics, central tendency, variability, percentiles, and distribution profiling.
  • Trend analysis and forecasting: moving averages, period-over-period comparisons (WoW, MoM, YoY).
  • Outlier and anomaly detection: Z-score, IQR, percentile methods, with guidance on interpretation.
  • Hypothesis testing: t-tests, z-tests, ANOVA, non-parametric alternatives; report p-values, effect sizes, and confidence intervals.
  • Cautionary notes: interpret correlations carefully and emphasize practical significance.

Quick Start

Provide a dataset and ask the AI to compute descriptive statistics, detect outliers, analyze trends, and perform hypothesis tests.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I run hypothesis testing on business metrics like revenue and user engagement?▼

To run hypothesis testing on business metrics, this skill applies t-tests, z-tests, and ANOVA based on your data distribution. It reports p-values, effect sizes, and confidence intervals to support data-driven conclusions for your revenue and engagement datasets.

What descriptive statistics can I compute to understand my dataset's distribution and variability?▼

Descriptive statistics computed include central tendency, variability, percentiles, and distribution profiling. The skill summarizes statistical properties to identify distribution shape and variability measures, providing a clear profile of your dataset's overall structure.

How do I detect outliers and anomalies in my dataset using Z-score or IQR?▼

Outlier detection is performed using Z-score, IQR, and percentile methods. The skill identifies anomalies in your dataset and provides guidance on interpreting these outliers to ensure they reflect true data variations rather than measurement errors.

Can I use trend analysis for period-over-period comparisons like WoW, MoM, and YoY?▼

Trend analysis supports period-over-period comparisons including WoW, MoM, and YoY. The skill calculates moving averages and evaluates trends over time, enabling you to track business metric changes and forecast future performance accurately.

When should I choose non-parametric alternatives over standard t-tests for statistical analysis?▼

Non-parametric alternatives are selected when your data distribution violates normality assumptions required by standard t-tests. The skill evaluates distribution assumptions to choose the appropriate hypothesis testing method, ensuring valid statistical results.

What is the best way to interpret correlations and avoid mistaking them for practical significance?▼

To interpret correlations correctly, the skill provides cautionary notes emphasizing practical significance over mere statistical correlation. It reports effect sizes and confidence intervals to help you distinguish meaningful business impact from random statistical noise.

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