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statistical-analysis

Perform statistical tests and generate APA-style reports with Python libraries.

Updated Jan 10, 2026
Search Tags:#reporting#data analysis#hypothesis testing#statistics#regression#bayesian
One-click install
npx skills add https://github.com/robinbarvaag/poynt --skill statistical-analysis-robinbarvaag
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/robinbarvaag/poynt/tree/main/.github/skills/statistical-analysis
Command: npx skills add https://github.com/robinbarvaag/poynt --skill statistical-analysis-robinbarvaag

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines complex statistical analysis, from selecting the right tests to generating professional APA-style reports, making advanced statistical methods accessible for research and data interpretation.

Core Features & Use Cases

  • Test Selection: Guides users to the most appropriate statistical tests (t-tests, ANOVA, regression, etc.) based on data and research questions.
  • Assumption Checking: Automates verification of statistical assumptions (normality, homogeneity of variance) with visualizations and recommendations.
  • Analysis Execution: Performs a wide range of statistical tests using Python libraries like SciPy, Statsmodels, and Pingouin.
  • Reporting: Generates publication-ready APA-style reports, including effect sizes and confidence intervals.
  • Use Case: A researcher has experimental data and needs to determine if there's a significant difference between three groups, check assumptions, run an ANOVA, and report the findings in APA format.

Quick Start

Use the statistical-analysis skill to perform an independent samples t-test on the provided 'group_a.csv' and 'group_b.csv' files.

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 generate an APA-style report from my experimental data?▼

Hypothesis testing and APA-style reporting are executed by selecting appropriate tests, verifying assumptions, calculating effect sizes, and generating publication-ready summaries using Python libraries like SciPy, Statsmodels, and Pingouin.

How does automated assumption checking work for statistical tests like ANOVA?▼

Automated assumption checking validates normality and homogeneity of variance using visualizations and data-driven recommendations before executing parametric tests like ANOVA or t-tests.

Can I use Python to perform Bayesian statistical analysis and regression?▼

Bayesian statistical analysis and regression are supported via Python libraries like PyMC and ArviZ, enabling probabilistic modeling and inference alongside deterministic parametric methods.

What is the best way to select the right statistical test for my dataset?▼

Selecting the right statistical test is handled by guiding you to appropriate parametric, non-parametric, or Bayesian methods based on your specific data characteristics and research questions.

Do I need to manually calculate effect sizes and confidence intervals after running a t-test?▼

Effect sizes and confidence intervals are calculated automatically during analysis execution, directly integrating these metrics into generated APA-style reports without requiring manual computation.

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