skills.rest
EvolutionAPI
EvolutionAPIOFFICIAL
Explore all skills by this author →
GitHub

data-statistical-analysis

Analyze workspace datasets with descriptive statistics, anomaly detection, and hypothesis tests.

520|175|Updated Apr 8, 2026
Search Tags:#python#time-series#hypothesis-testing#statistical-analysis#descriptive-statistics#outlier-detection#business-metrics
One-click install
npx skills add https://github.com/EvolutionAPI/evo-nexus --skill data-statistical-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-statistical-analysis
Source: https://github.com/EvolutionAPI/evo-nexus/tree/main/.claude/skills/data-statistical-analysis
Command: npx skills add https://github.com/EvolutionAPI/evo-nexus --skill data-statistical-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a structured, reproducible approach to analyze business metrics and workspace datasets by producing descriptive statistics, identifying trends and anomalies, and validating observed changes with hypothesis tests so teams make informed, cautious decisions.

Core Features & Use Cases

  • Descriptive statistics: Guidance on mean, median, IQR, percentiles, dispersion, and when to prefer robust measures for skewed business metrics like MRR.
  • Trend & time-series analysis: Practical methods such as moving averages, period-over-period comparisons, and simple forecasting baselines for operational monitoring.
  • Outlier and anomaly detection: Multiple approaches (Z-score, IQR, percentile) and a decision framework for investigating and treating outliers in financial and licensing data.
  • Hypothesis testing & experiment analysis: Recommended tests (t-test, chi-square, Mann-Whitney), sample size considerations, effect sizes, and guidance on statistical vs. practical significance for A/B and before/after comparisons.
  • Use case: Analyze MRR distribution, detect anomalous drops in license instances, or evaluate whether a product change moved conversion rates with appropriate statistical rigor.

Quick Start

Analyze the dataset to compute descriptive statistics, flag outliers, and run hypothesis tests relevant to your business question.

Frequently Asked Questions about data-statistical-analysis

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

FAQPage Schema
How do I run statistical analysis on business metrics like MRR and churn distributions?▼

Run statistical analysis on business metrics by applying descriptive statistics, outlier detection, and hypothesis tests using a Python environment with pandas, scipy, and statsmodels to validate observed changes in MRR and churn distributions.

What is the best way to detect anomalies and outliers in license instance data?▼

The best way to detect anomalies in license instance data is applying Z-score, IQR, or percentile methods within a Python statistical analysis workflow to flag outliers and establish a decision framework for investigating financial and licensing data.

How do I evaluate A/B test results and calculate statistical significance for product changes?▼

Evaluate A/B test results by running hypothesis tests such as t-tests, chi-square, or Mann-Whitney using Python statistical libraries, measuring effect sizes and sample sizes to distinguish statistical significance from practical significance in product comparisons.

Can I use Python and scipy for time-series analysis on operational monitoring metrics?▼

Yes, you can use Python with pandas and statsmodels for time-series analysis on operational monitoring metrics by computing moving averages, period-over-period comparisons, and simple forecasting baselines to identify trends.

Do I need a specific Python execution environment to perform hypothesis testing on workspace datasets?▼

Yes, you need a Python execution environment with data manipulation and statistics libraries like pandas, scipy, and statsmodels to run numeric analyses and hypothesis tests on workspace datasets for reproducible business metric insights.

When should I use robust statistical measures instead of mean for skewed business metrics?▼

Use robust statistical measures like median and IQR instead of mean for skewed business metrics when distributions are uneven, ensuring reliable descriptive statistics for financial data such as MRR.

Related Skills

Explore More Data & Analytics Skills→
SJTU-IPADSSJTU-IPADS
7

data-analyst-cn

Clean datasets and generate statistics, correlations, and visualization templates.

Official
Intermediate
chelleboyerchelleboyer

statistical-analysis

Analyze datasets with descriptive statistics, trend analysis, anomaly detection, and hypothesis testing.

Community
Advanced
axiomhqaxiomhq
59

detect-anomalies

Identify anomalies in Axiom datasets using statistical analysis on logs, metrics, and traces.

Official
Advanced
sagunkayasthasagunkayastha

aeon

Analyze time series data for classification, regression, clustering, forecasting, and anomaly detection.

Community
Advanced