Can it detect statistical errors and p-hacking?

Yes, it evaluates sample sizes, power, p-value interpretations, and common pitfalls such as multiple comparison errors or outcome switching.

How does this skill evaluate research quality?

It uses standardized frameworks like GRADE and Cochrane Risk of Bias to systematically assess study methodology, internal/external validity, and the overall strength of evidence.

What types of bias can it identify in a study?

The skill screens for a wide range of distortions, including researcher confirmation bias, selection bias, attrition bias, and measurement inaccuracies.

Is this skill useful for non-scientists?

Yes, it is highly effective for journalists, students, and analysts who need to verify the rigor of scientific claims found in media or business reports.

Does it support specific evidence hierarchies?

Yes, it follows the standard hierarchy of evidence, from expert opinion up to systematic reviews and meta-analyses, while adjusting for design-specific quality.

Scientific Critical Thinking

Name: Scientific Critical Thinking
Author: ovachiever

byovachiever

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Data Science & ML

Evaluates research rigor by assessing methodology, experimental design, and statistical validity using frameworks like GRADE and Cochrane.

About

The Scientific Critical Thinking skill empowers Claude to perform high-level analysis of research papers and scientific claims. By applying systematic frameworks like GRADE and Cochrane Risk of Bias (ROB), it helps users identify methodology flaws, detect cognitive or sampling biases, and verify the soundness of statistical conclusions. This skill is essential for researchers, students, and data scientists who need to objectively weigh evidence, distinguish between correlation and causation, and ensure their work adheres to the highest standards of scientific integrity.

Key Features

Comprehensive methodology and experimental design critique
Detection of logical fallacies within scientific arguments
Deep statistical analysis including p-value and power assessment
Evidence quality ranking using GRADE and Cochrane ROB standards
5 GitHub stars
Systematic detection of cognitive, selection, and measurement biases

Use Cases

Identifying p-hacking or publication bias in research datasets
Assessing the clinical validity and strength of medical study claims
Critiquing peer-reviewed literature for systematic reviews

About

Key Features

Comprehensive methodology and experimental design critique
Detection of logical fallacies within scientific arguments
Deep statistical analysis including p-value and power assessment
Evidence quality ranking using GRADE and Cochrane ROB standards
5 GitHub stars
Systematic detection of cognitive, selection, and measurement biases

Use Cases

Identifying p-hacking or publication bias in research datasets
Assessing the clinical validity and strength of medical study claims
Critiquing peer-reviewed literature for systematic reviews