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statistical-analysis

Automate statistical analysis workflows with hypothesis tests, regressions, and Bayesian methods.

15|2|Updated Dec 17, 2025
Search Tags:#regression#hypothesis-testing#statistical-analysis#bayesian#power-analysis#assumption-checks#reproducible-research
One-click install
npx skills add https://github.com/rubensliv/k-dense-ai --skill statistical-analysis-rubensliv
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/rubensliv/k-dense-ai/tree/main/scientific-skills/statistical-analysis
Command: npx skills add https://github.com/rubensliv/k-dense-ai --skill statistical-analysis-rubensliv

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 can be time-consuming and error-prone when performing hypothesis tests, regression analyses, and Bayesian modeling across datasets. This skill provides an automated, transparent framework to plan analyses, check assumptions, run tests, quantify effects, and generate APA-style reports. It integrates Python libraries for robust diagnostics, reproducible workflows, and publication-ready outputs, so researchers can focus on interpretation rather than bookkeeping.

Core Features & Use Cases

  • Automated analysis planning and test selection based on data type and design
  • Comprehensive assumption checks and diagnostics (normality, homogeneity of variance, independence, linearity, outliers)
  • Execution of standard and Bayesian tests (t-test, ANOVA, correlation, regression, Bayesian alternatives) with effect sizes, CIs, and diagnostics
  • APA-style reporting templates and generation of publication-ready summaries and figures
  • Power analysis and sensitivity analysis for study planning and post-hoc assessment

Quick Start

Load your dataset into a pandas DataFrame and call comprehensive_assumption_check to automatically evaluate normality, variance, and outliers, then run the recommended tests and generate a report.

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 and regression with automated assumption checks in Python?▼

Power analysis and sensitivity analysis support study planning and post-hoc assessment by calculating required sample sizes and detecting effect sizes. This ensures your experimental or observational data collection is statistically powered before analysis begins.

What's the best way to generate APA-style reports and reproducible documentation for statistical analysis?▼

Automated statistical analysis workflows generate APA-style reporting templates, publication-ready summaries, and figures alongside test results. This streamlines reproducible research by outputting formatted documentation directly from executed hypothesis tests and regressions.

Do I need pandas and SciPy to automate statistical diagnostics and assumption checks?▼

Yes, the framework relies on Python libraries including numpy, pandas, scipy, matplotlib, and seaborn to execute workflows. You load your dataset into a pandas DataFrame, then call functions to evaluate assumptions and run tests across experimental, observational, or simulated data.

When should I use Bayesian methods instead of standard t-tests or ANOVA for my dataset?▼

Bayesian methods provide alternatives to standard t-tests and ANOVA when you need posterior distributions rather than p-values. The framework applies both approaches across experimental, observational, and simulated data, generating diagnostics and ready-to-report outputs for either method.

How do I calculate statistical power and sample size for study planning in Python?▼

Statistical power analysis and sensitivity analysis are built into the workflow for study planning and post-hoc assessment. These functions calculate required sample sizes and detect effect sizes to ensure your experimental design is statistically powered.

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