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kwp-data-statistical-analysis

Apply descriptive statistics, trend analysis, and hypothesis testing to raw datasets.

7|5|Updated May 7, 2026
Search Tags:#data analysis#business intelligence#hypothesis testing#statistics#anomaly detection#trend analysis
One-click install
npx skills add https://github.com/14790897/MiQi --skill kwp-data-statistical-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kwp-data-statistical-analysis
Source: https://github.com/14790897/MiQi/tree/main/miqi/skills/kwp/data/statistical-analysis
Command: npx skills add https://github.com/14790897/MiQi --skill kwp-data-statistical-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the challenge of interpreting raw data by providing a structured framework for descriptive statistics, trend analysis, and hypothesis testing, ensuring that business decisions are backed by sound mathematical reasoning rather than intuition alone.

Core Features & Use Cases

  • Statistical Methodology: Provides clear guidance on selecting appropriate measures of central tendency and variability based on data distribution.
  • Trend & Anomaly Detection: Offers techniques for smoothing noise, identifying seasonality, and detecting outliers using Z-score or IQR methods.
  • Hypothesis Testing: Enables rigorous A/B testing and segment comparison to determine if observed differences are statistically significant or merely random chance.

Quick Start

Ask the agent to perform a statistical analysis on your dataset by specifying the metric and the desired test, such as requesting a trend analysis and outlier detection for your monthly sales figures.

Frequently Asked Questions about kwp-data-statistical-analysis

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

FAQPage Schema
How do I determine if my A/B test results are statistically significant?▼

A/B test significance is determined through hypothesis testing, which compares observed metric differences against random chance to verify if the variation is a meaningful pattern rather than statistical noise.

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

Anomaly detection in time-series data is best handled using Z-score or IQR methods, which identify outliers by measuring deviations from expected data distribution and smoothing out random noise.

How do I choose the right descriptive statistics for skewed distributions?▼

Selecting descriptive statistics for skewed distributions requires evaluating data variability and central tendency, choosing robust mathematical measures that accurately reflect the dataset without being distorted by outliers.

Can I use trend analysis to identify seasonality in monthly sales figures?▼

Trend analysis can identify seasonality in monthly sales figures by applying smoothing techniques to raw datasets, isolating recurring temporal patterns from underlying business metrics and random noise.

When should I use hypothesis testing instead of just comparing raw metrics?▼

Hypothesis testing is necessary when comparing raw metrics to ensure observed differences reflect true business changes rather than random variance, providing mathematical rigor for data-driven decision making.

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