quantitative-analysis

Guide statistical test selection, effect size interpretation, and power analysis.

Updated Aug 27, 2026

Search Tags:#quantitative analysis #statistics #data interpretation #power analysis #effect size #statistical tests

Ready to run

npx skills add https://github.com/DaRipper91/gemini-termux-migration --skill quantitative-analysis-daripper91

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: quantitative-analysis
Source: https://github.com/DaRipper91/gemini-termux-migration/tree/main/AI_Knowledge_Base/Research_and_Analysis/quantitative-analysis
Command: npx skills add https://github.com/DaRipper91/gemini-termux-migration --skill quantitative-analysis-daripper91

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the need for accurate statistical analysis, interpretation of numerical data, and the prevention of common analytical errors like p-hacking.

Core Features & Use Cases

Statistical Test Selection: Guides users to the appropriate statistical tests based on data type and research question.
Power and Effect Size Analysis: Facilitates the calculation of sample sizes and interpretation of effect magnitudes.
Advanced Modeling: Supports complex analyses like Multilevel Modeling and SEM.
Use Case: A researcher needs to determine the correct statistical test to compare two groups with continuous, normally distributed data and wants to understand the practical significance of their findings.

Quick Start

Use the quantitative-analysis skill to select the appropriate statistical test for comparing two groups with continuous data.

Frequently Asked Questions about quantitative-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I select the right statistical test for comparing two groups with continuous data?▼

To select the right statistical test for continuous data, you must evaluate your data distribution and research question. This Skill guides you to the appropriate frequentist or Bayesian test while ensuring mathematical rigor and preventing analytical errors.

What is the difference between practical significance and statistical significance in effect size analysis?▼

Effect size analysis measures the magnitude of an effect, distinguishing practical significance from mere statistical significance. This Skill helps you interpret effect magnitudes and calculate necessary sample sizes to ensure your research findings are both valid and meaningful.

Can I use multilevel modeling and structural equation modeling for complex research data?▼

Yes, you can use multilevel modeling (HLM) and structural equation modeling (SEM) for complex research data. This Skill supports these advanced modeling techniques alongside non-parametric alternatives to handle intricate data structures accurately.

How do I conduct a power analysis to determine sample size for my study?▼

To conduct a power analysis for sample size determination, you need to define your expected effect size and desired significance level. This Skill facilitates precise power and effect size calculations to prevent misinterpretation of null results and ensure statistical rigor.

How do I prevent p-hacking and avoid misinterpreting null results in statistical inference?▼

Preventing p-hacking and avoiding misinterpretation of null results requires strict adherence to proper statistical inference methods. This Skill provides expert guidance on both frequentist and Bayesian approaches to ensure mathematical rigor throughout your data interpretation process.

When do I need non-parametric alternatives instead of standard frequentist statistical tests?▼

You need non-parametric alternatives when your research data violates the normal distribution assumptions required by standard frequentist tests. This Skill guides you through selecting appropriate non-parametric methods to maintain valid insights for your specific data structure.