What frameworks does this skill use for evaluation?

The skill utilizes established scientific standards including the GRADE system for evidence quality and the Cochrane Risk of Bias (ROB) tool for methodology assessment.

Is this skill limited to medical research?

No. While it incorporates medical frameworks, its core principles for experimental design, logical fallacies, and internal validity are applicable across all scientific and social science domains.

How does it detect bias in a study?

It performs a systematic review of researcher cognitive biases, selection biases, measurement biases, and potential confounding variables that could distort findings.

Can this skill help identify p-hacking and statistical errors?

Yes, it includes a comprehensive statistical review checklist that flags common pitfalls like p-hacking, multiple comparison issues, and inadequate sample sizes.

Scientific Critical Thinking

Name: Scientific Critical Thinking
Author: Activer007

byActiver007

Data Science & ML

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

About

This skill empowers Claude to perform rigorous scientific peer review and analysis by providing a systematic framework for evaluating experimental designs, detecting cognitive and measurement biases, and scrutinizing statistical methods. It helps users determine the internal and external validity of studies, identify confounding variables, and assess the strength of evidence across various scientific disciplines. Whether you are conducting a systematic review or validating a research paper's conclusions, this skill ensures a disciplined, evidence-based approach to critical analysis.

Key Features

Systematic Bias Detection (Cognitive, Selection, Measurement)
Methodology Critique & Experimental Design Assessment
Evidence Quality Grading (GRADE & Cochrane ROB)
Statistical Analysis Evaluation (Power, P-values, Effect Sizes)
Logical Fallacy Identification in Scientific Arguments
0 GitHub stars

Use Cases

Conducting systematic literature reviews or meta-analyses
Peer-reviewing research papers for methodological flaws and bias
Evaluating the validity of statistical claims in data-driven reports

About

Key Features

Systematic Bias Detection (Cognitive, Selection, Measurement)
Methodology Critique & Experimental Design Assessment
Evidence Quality Grading (GRADE & Cochrane ROB)
Statistical Analysis Evaluation (Power, P-values, Effect Sizes)
Logical Fallacy Identification in Scientific Arguments
0 GitHub stars

Use Cases

Conducting systematic literature reviews or meta-analyses
Peer-reviewing research papers for methodological flaws and bias
Evaluating the validity of statistical claims in data-driven reports