statistics-verifier

Verify statistical claims by auditing methodology, significance testing, and bias.

88|22|Updated Dec 17, 2025

Search Tags:#data analysis #statistics #bias detection #research validation #methodology review #significance testing

Ready to run

npx skills add https://github.com/travisjneuman/.claude --skill statistics-verifier

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statistics-verifier
Source: https://github.com/travisjneuman/.claude/tree/main/skills/statistics-verifier
Command: npx skills add https://github.com/travisjneuman/.claude --skill statistics-verifier

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps users critically evaluate statistical claims, research findings, and data analysis by providing structured checklists and frameworks for methodology checking, significance testing, and bias detection.

Core Features & Use Cases

Claim Validation: Assess the validity of statistical claims using a systematic checklist.
Methodology Auditing: Review research designs, sample sizes, and statistical tests for appropriateness and potential errors.
Bias Detection: Identify common cognitive and reporting biases that can skew results.
Use Case: A researcher wants to verify the statistical rigor of a published study before citing it. They can use this Skill to systematically check the study's methodology, significance testing, and potential biases.

Quick Start

Use the statistics-verifier skill to check the methodology of a statistical claim.

Frequently Asked Questions about statistics-verifier

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

FAQPage Schema

How do I verify statistical claims and check research methodology?▼

You verify statistical claims by applying structured frameworks for methodology review, significance testing, and bias detection. This process involves systematically checking sample size validation, assessing error detection, and reviewing survey methodology to ensure research findings are valid.

What is the best way to detect bias in data analysis and research findings?▼

Detecting bias in data analysis requires structured checklists to identify common cognitive and reporting biases that skew results. You can audit research designs and perform correlation versus causation checks to find methodological flaws.

How do I audit a research study for significance testing and reproducibility?▼

Auditing a research study for significance testing involves checking the appropriateness of statistical tests, validating sample sizes, and performing a reproducibility assessment. You systematically review the survey methodology to ensure the results can be replicated.

Can I fact-check statistical claims and validate data visualization integrity?▼

Yes, you can fact-check statistical claims by evaluating data visualization integrity and verifying the underlying raw data. This involves checking for errors in the presentation of statistical findings and applying a systematic claim assessment framework.

When do I need a methodology review for survey data and correlation analysis?▼

You need a methodology review for survey data when validating research findings or auditing data analysis to ensure statistical rigor. It is essential for checking correlation versus causation, detecting reporting bias, and confirming sample size adequacy.

Does research validation require structured frameworks for error detection?▼

Research validation requires structured frameworks for error detection to systematically assess statistical claims and methodology. These frameworks guide sample size validation, significance testing, and bias detection to ensure fact-checking is thorough and accurate.