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statistical-analysis

Guide statistical test selection, assumption checks, and APA-style result reporting.

Updated Feb 15, 2026
Search Tags:#statistics#data-analysis#assumption-checking#bayesian#power-analysis#test-selection#apa-reporting
One-click install
npx skills add https://github.com/aleph23/Natasha --skill statistical-analysis-aleph23
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/aleph23/Natasha/tree/main/skills/statistical-analysis
Command: npx skills add https://github.com/aleph23/Natasha --skill statistical-analysis-aleph23

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, matplotlib, seaborn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Statistical-analysis provides structured guidance for selecting appropriate statistical tests, validating assumptions, executing analyses, and producing APA-formatted results, reducing guesswork and errors in data reporting.

Core Features & Use Cases

  • Test selection guidance for common designs (t-tests, ANOVA, regression, nonparametric alternatives) based on data type and study design.
  • Automated assumption checks and diagnostics (normality, homogeneity of variance, independence) with clear interpretations and remediation suggestions.
  • Power analysis and sample-size planning, sensitivity analyses, and comprehensive reporting of effect sizes and confidence intervals.
  • APA-style results reporting and publication-ready figures/tables, integrating with SciPy, StatsModels, Pingouin, PyMC, and ArviZ.
  • Real-world use: academic research projects requiring rigorous analysis workflows and reproducible reporting.

Quick Start

Provide your dataset and I will guide you through selecting tests, checking assumptions, and generating APA-formatted results.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I select the right statistical test for my dataset?▼

Selecting the right statistical test requires matching your data type and study design to the appropriate method. This Skill guides you through choosing between t-tests, ANOVA, regression, and nonparametric alternatives based on your specific variables.

How do I check statistical assumptions like normality and homogeneity of variance in Python?▼

Checking statistical assumptions involves testing normality, homogeneity of variance, and independence. This Skill automates these diagnostics using libraries like SciPy and StatsModels, providing clear interpretations and remediation suggestions for violated assumptions.

Can I generate APA-style reports with effect sizes and confidence intervals using pandas and scipy?▼

Generating APA-style reports with pandas and SciPy is supported. This Skill formats your statistical results into publication-ready text, integrating descriptive statistics, effect sizes, confidence intervals, and accompanying figures.

What is the best way to perform power analysis and sample size planning for an experimental design?▼

Power analysis and sample size planning determine the minimum subjects needed to detect an effect. This Skill provides structured guidance for sensitivity analyses and comprehensive reporting to ensure your experimental design is adequately powered.

Does this statistical analysis workflow support Bayesian methods with PyMC and ArviZ?▼

Bayesian statistical analysis is supported through PyMC and ArviZ integration. This Skill guides you through executing Bayesian analyses alongside traditional frequentist methods, maintaining robust reporting standards across both approaches.

When should I use nonparametric alternatives instead of standard parametric tests?▼

Nonparametric alternatives are recommended when statistical assumption checks fail, particularly for normality or variance homogeneity. This Skill identifies assumption violations and automatically guides you toward the appropriate nonparametric test replacements.

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