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statistical-analysis

Conduct statistical tests and generate APA-style reports.

Updated Apr 17, 2026
Search Tags:#hypothesis testing#statistical analysis#power analysis#apa reporting#effect sizes
One-click install
npx skills add https://github.com/CompSci-Squad/tcc_ai --skill statistical-analysis-compsci-squad
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/CompSci-Squad/tcc_ai/tree/main/.github/skills/statistical-analysis
Command: npx skills add https://github.com/CompSci-Squad/tcc_ai --skill statistical-analysis-compsci-squad

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scipy, statsmodels, pingouin, pymc, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides expert guidance and automated tools for conducting statistical analyses, saving you time and ensuring accuracy in your research.

Core Features & Use Cases

  • Test Selection and Planning: Automatically choose appropriate statistical tests based on your research questions and data characteristics.
  • Assumption Checking: Perform comprehensive checks for normality, homogeneity of variance, and other key assumptions before running tests.
  • Statistical Testing: Execute a range of statistical tests including t-tests, ANOVA, chi-square, regression, correlation, and Bayesian analysis.
  • Effect Sizes and Interpretation: Calculate and interpret effect sizes for all analyses, distinguishing statistical from practical significance.
  • Professional Reporting: Generate APA-style statistical reports and create publication-ready figures and tables.
  • Use Case: Suppose you're conducting a study on the effectiveness of a new medication. Use this Skill to determine the appropriate statistical tests, check assumptions, analyze the data, calculate effect sizes, and generate a comprehensive statistical report in APA format.

Quick Start

Use the statistical-analysis skill to conduct a t-test comparing the means of two groups and generate an APA-formatted report.

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 research data?▼

Statistical test selection is automated by analyzing your research questions and data characteristics to recommend appropriate tests like t-tests, ANOVA, or chi-square. It evaluates your data structure to determine whether parametric or non-parametric methods are required before execution.

Can I generate APA-style statistical reports automatically after running hypothesis tests?▼

APA-style statistical reporting is generated automatically after executing hypothesis testing and calculating effect sizes. The process formats your statistical results, including test statistics and p-values, into publication-ready reports and tables following standard academic guidelines.

How do I check statistical assumptions like normality and homogeneity of variance before running tests?▼

Assumption checking validates normality, homogeneity of variance, and other key statistical prerequisites before running tests. This ensures your data meets the required conditions for parametric testing, preventing invalid conclusions from assumption violations.

Does this statistical analysis workflow support Bayesian analysis and effect size calculation?▼

Bayesian analysis and effect size calculation are fully supported alongside frequentist methods using libraries like pymc. The workflow computes effect sizes to distinguish statistical significance from practical significance, providing comprehensive interpretation for academic research.

What Python libraries do I need to run hypothesis testing and power analysis?▼

Hypothesis testing and power analysis require Python libraries including scipy, statsmodels, pingouin, and pymc. These dependencies provide the computational foundation for executing statistical tests, validating assumptions, and performing advanced analytical procedures.

What's the best way to conduct a t-test and interpret the results for academic research?▼

Conducting a t-test for academic research involves selecting the test, validating assumptions, executing the analysis, and calculating effect sizes. This guided process ensures proper interpretation of statistical versus practical significance, culminating in an APA-formatted report.

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