Does it detect p-hacking or data manipulation?

Yes, it includes specialized checklists for auditing statistical interpretations, including identifying signs of p-hacking, outcome switching, and underpowered samples.

Is this skill useful for experimental design?

Absolutely; it can be used to review study protocols and identify potential confounding variables or validity issues before research begins.

Can this skill help with writing systematic reviews?

Yes, it is specifically designed to identify bias, evaluate evidence hierarchies, and assess the convergence of evidence across multiple studies.

How does this skill assess research quality?

It uses established frameworks like GRADE and Cochrane Risk of Bias (ROB) to systematically evaluate methodology, experimental design, and statistical soundness.

Scientific Critical Thinking

Name: Scientific Critical Thinking
Author: kjgarza

bykjgarza

データサイエンスとML

Evaluates the rigor of scientific research by assessing methodology, statistical validity, and evidence quality using industry-standard frameworks.

概要

This skill empowers Claude to perform high-level critical analysis of scientific claims and research papers. It provides a systematic framework for evaluating experimental designs, detecting cognitive and selection biases, auditing statistical interpretations, and weighing evidence according to the GRADE and Cochrane standards. Ideal for researchers, academics, and data scientists, it ensures that scientific conclusions are scrutinized for internal validity, external generalizability, and logical soundness before being accepted or integrated into larger projects.

主な機能

Systematic detection of cognitive, selection, and measurement biases
Comprehensive audit of statistical power, p-values, and effect size interpretations
Identification of logical fallacies in scientific arguments and conclusions
Methodological rigor assessment for internal and external validity
Evidence quality grading using GRADE and Cochrane risk of bias frameworks
0 GitHub stars

ユースケース

Vetting scientific claims in research papers, technical reports, or whitepapers
Conducting systematic reviews and meta-analyses of existing scientific literature
Designing and refining experimental protocols to minimize confounding and bias

概要

主な機能

Systematic detection of cognitive, selection, and measurement biases
Comprehensive audit of statistical power, p-values, and effect size interpretations
Identification of logical fallacies in scientific arguments and conclusions
Methodological rigor assessment for internal and external validity
Evidence quality grading using GRADE and Cochrane risk of bias frameworks
0 GitHub stars

ユースケース

Vetting scientific claims in research papers, technical reports, or whitepapers
Conducting systematic reviews and meta-analyses of existing scientific literature
Designing and refining experimental protocols to minimize confounding and bias