skills.rest
crazymsn
crazymsnCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

statistical-analysis

Analyze scientific datasets with statistical tests and APA-formatted reports.

22|4|Updated May 25, 2026
Search Tags:#statistics#data-analysis#hypothesis-testing#bayesian#power-analysis#effect-sizes
One-click install
npx skills add https://github.com/crazymsn/academic-skills --skill statistical-analysis-crazymsn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/crazymsn/academic-skills/tree/main/academic-skills/statistical-analysis
Command: npx skills add https://github.com/crazymsn/academic-skills --skill statistical-analysis-crazymsn

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 is streamlined to guide researchers through choosing appropriate tests, validating assumptions, and reporting results with useful effect sizes and diagnostics.

Core Features & Use Cases

  • Guided test selection for t-tests, ANOVA, regression, correlation, and Bayesian analyses across single- and multi-group datasets.
  • Built-in assumption checks, diagnostics, and robust reporting aligned with APA standards.
  • Real-world use: plan studies, analyze experiments, and generate publication-ready results.

Quick Start

Provide a complete analysis pipeline from loading data to producing reports with minimal setup.

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 check statistical assumptions for academic research data?▼

Statistical analysis is streamlined through guided test selection, applying t-tests, ANOVA, regression, and Bayesian analyses while automatically validating assumptions and calculating effect sizes to deliver rigorous academic research conclusions.

What is the best way to generate APA-formatted reports from a pandas DataFrame for experimental designs?▼

APA-formatted reports are generated automatically as outputs alongside diagnostic visuals and test summaries after analyzing your experimental or observational datasets, providing publication-ready results directly from your loaded DataFrame.

Can I perform Bayesian analysis and power analysis using scipy and numpy for multi-group datasets?▼

Bayesian analyses and power analysis are supported across single- and multi-group datasets, leveraging Python libraries including scipy and numpy to execute rigorous statistical methods and compute effect sizes for experimental designs.

How do I choose the right statistical test for my observational study data?▼

Choosing the right statistical test is handled through a guided selection process that evaluates your observational study data structure, matching it to appropriate correlation, regression, or ANOVA methods with built-in assumption checks.

Does this statistical analysis approach support effect sizes and diagnostic visuals for publication?▼

Effect sizes and diagnostic visuals are explicitly supported as core outputs, combining automated diagnostics with APA-aligned reporting standards to produce publication-ready test summaries and visual plots for your research datasets.

Related Skills

Explore More Data & Analytics Skills→
StarlitnightlyStarlitnightly
1.2k

data-transform

Transform raw tabular data into cleaned, structured datasets using pandas and numpy.

Community
Advanced
CRDong233CRDong233
3

statistical-analysis

Plan, run, and report statistical analyses with APA-formatted outputs.

Community
Advanced
obinopaulobinopaul
2

exploratory-data-analysis

Load CSV data, assess missing values, compute statistics, and generate visualizations.

Community
Intermediate
CompSci-SquadCompSci-Squad

data-scientist

Perform advanced analytics, predictive modeling, and statistical modeling for data science tasks.

Official
Advanced