Is this suitable for peer-reviewed academic research?

Yes, it is designed for PhD-level rigor, ensuring all findings include detailed data audits, rationale for test selection, and identification of threats to validity.

How does this skill help prevent p-hacking?

It enforces a rigorous protocol that prioritizes effect sizes, confidence intervals, and pre-test assumption checking over simple binary p-value interpretations.

Does it support advanced statistical modeling?

Absolutely. It provides specific guidance for Multilevel Modeling (HLM) for nested data and Structural Equation Modeling (SEM) for latent variables.

Can it handle non-normal data distributions?

Yes, the skill includes logic for identifying non-normality and recommending non-parametric alternatives like Mann-Whitney U or Kruskal-Wallis tests.

Quantitative Analysis & Statistics

Name: Quantitative Analysis & Statistics
Author: poemswe

bypoemswe

•

Data Science & ML

Conducts rigorous statistical analysis, power calculations, and data interpretation using PhD-level methodologies.

This skill transforms Claude into a high-level quantitative researcher capable of performing complex statistical tests, verifying data assumptions, and calculating effect sizes. It ensures mathematical rigor by guiding the selection of frequentist or Bayesian models while prioritizing practical significance over simple p-values. Ideal for researchers and data scientists, it prevents common errors like p-hacking by enforcing a standardized protocol for data auditing, validation, and reporting, ensuring every numerical claim is backed by appropriate statistical evidence.

Key Features

01Automated statistical test selection for various data types and distributions

02Standardized PhD-level reporting with confidence intervals and practical significance

03Advanced modeling support including HLM, SEM, and non-parametric alternatives

0410 GitHub stars

05Rigorous assumption checking for normality, homoscedasticity, and independence

06Comprehensive power analysis and effect size calculations (Cohen's d, η², Pearson's r)

Use Cases

01Analyzing experimental research data to determine statistical significance and effect magnitude

02Performing advanced regression or multilevel modeling on complex, nested datasets

03Conducting power analysis to determine required sample sizes for academic or clinical studies

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add poemswe/co-researcher quantitative-analysis

For use in Claude.ai and ChatGPT

Download Skill