skills.rest
WanLanglin
WanLanglinCOMMUNITY
Explore all skills by this author →
GitHub

statistical-analysis

Automate statistical test selection and APA-style reporting for experimental data.

Updated Mar 16, 2026
Search Tags:#reproducibility#assumption-checking#statistical-analysis#power-analysis#test-selection#apa-formatting
One-click install
npx skills add https://github.com/WanLanglin/spec-driven-vibe-research-skills --skill statistical-analysis-wanlanglin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/WanLanglin/spec-driven-vibe-research-skills/tree/main/skills/experiment-analysis/statistical-analysis
Command: npx skills add https://github.com/WanLanglin/spec-driven-vibe-research-skills --skill statistical-analysis-wanlanglin

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Statistical analysis can be challenging, especially when selecting appropriate tests, verifying assumptions, performing advanced analyses, and reporting results in APA style. This skill guides researchers through a structured workflow to choose the right tests, check assumptions automatically, run analyses with best-practice defaults, compute effect sizes, and generate publication-ready reports.

Core Features & Use Cases

  • Test selection guidance for t-tests, ANOVA, regression, and Bayesian methods based on data characteristics.
  • Automatic assumption checks and diagnostics with recommended remedies.
  • Comprehensive reporting templates in APA format and ready-to-publish figures and tables.
  • Use Case: A researcher comparing two groups on a continuous outcome with normal data uses the t-test and reports results with effect size and CI.

Quick Start

Load your dataset and run a complete statistical analysis to obtain test selections, diagnostics, and 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 choose the right statistical test for my experimental data?▼

APA-formatting your statistical results is handled automatically, generating publication-ready reports that include effect sizes, confidence intervals, and diagnostic tables. This ensures your experimental output meets academic publishing standards without manual formatting.

Can I run statistical analysis with Python libraries like scipy and statsmodels for small samples?▼

Assumption checking is performed automatically before running analyses, evaluating normality, variance homogeneity, and other test prerequisites. When assumptions are violated, the workflow provides recommended remedies and alternative approaches to ensure valid results.

What's the best way to report effect sizes and power analysis in APA format?▼

Bayesian analysis is supported through the pymc library, allowing you to perform alternative statistical analyses beyond traditional frequentist methods. This provides probabilistic inference capabilities within the same structured workflow that handles assumption checks and reporting.

Does this statistical analysis workflow support Bayesian methods with pymc?▼

Assumption checking is performed automatically before running analyses, evaluating normality, variance homogeneity, and other test prerequisites. When assumptions are violated, the workflow provides recommended remedies and alternative approaches to ensure valid results.

Related Skills

Explore More Data & Analytics Skills→
galeepgaleep

statistical-analysis

Automate statistical hypothesis testing, regression, and Bayesian inference with Python libraries.

Community
Advanced
rubenslivrubensliv
15

scientific-writing

Generate IMRAD manuscripts with structured abstracts and formatted citations.

Community
Advanced
smukerjismukerji
1

data-analysis

Analyze structured data from files or databases to generate statistical summaries and visualizations.

Community
Advanced
CompSci-SquadCompSci-Squad

data-scientist

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

Official
Advanced