scientific-critical-thinking

Name: scientific-critical-thinking
Availability: InStock
Author: franklee16

Audit scientific claims for methodology, bias, and evidence strength.

203|27|Updated Apr 19, 2026

Search Tags:#bias detection #peer review #GRADE #statistical validity #evidence grading #methodology critique #scientific claims

Ready to run

npx skills add https://github.com/franklee16/academic-research-skills --skill scientific-critical-thinking-franklee16

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-critical-thinking
Source: https://github.com/franklee16/academic-research-skills/tree/main/peer-review/scientific-critical-thinking
Command: npx skills add https://github.com/franklee16/academic-research-skills --skill scientific-critical-thinking-franklee16

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you critically evaluate scientific claims by checking whether the study design, statistics, and reasoning actually support the conclusions.

Core Features & Use Cases

Methodology Critique: Assess internal/external/construct/statistical validity, including randomization, blinding, measurement quality, and controls.
Bias Detection & Confounding Identification: Systematically identify cognitive, selection, measurement, analysis, and confounding biases that can distort results.
Evidence Quality Grading: Evaluate strength of evidence using GRADE-style considerations and evidence hierarchy, and assess logical/causal overreach.
Logical & Claim Evaluation: Detect common scientific reasoning errors (e.g., correlation vs causation, p-hacking, unfounded generalization) and provide proportional critique.

Quick Start

Paste the research abstract (or key Methods/Results excerpts) and ask for a structured critique focused on validity threats, likely biases, and an evidence-quality assessment.

Frequently Asked Questions about scientific-critical-thinking

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

FAQPage Schema

How do I evaluate the methodology and statistical validity of a research paper?▼

To evaluate methodology and statistical validity, you can audit the study's randomization, blinding, measurement quality, and controls to identify threats to internal, external, construct, and statistical validity. This structured critique distinguishes supported findings from overclaims.

What is the best way to detect bias and confounding in observational studies?▼

Detecting bias and confounding in observational studies requires systematically identifying cognitive, selection, measurement, and analysis biases that distort results. Using criterion-based analysis helps isolate confounding variables and assess their impact on the study's conclusions.

How does GRADE evidence grading work for assessing scientific claims?▼

GRADE evidence grading works by evaluating the strength of evidence using an evidence hierarchy and specific criteria to assess logical or causal overreach. It provides a structured framework to determine whether scientific claims are supported by the presented data.

Can I use this to write constructive peer-review feedback?▼

Yes, you can use this to write constructive peer-review feedback by applying a structured, criterion-based analysis to experimental or observational study designs. It helps detect common reasoning errors like correlation versus causation and p-hacking, producing actionable recommendations.

What do I need to provide to get a structured critique of a scientific abstract?▼

You need to provide the research abstract or key excerpts from the Methods and Results sections to receive a structured critique. The analysis will then focus on validity threats, likely biases, and an overall evidence-quality assessment.

What are common scientific reasoning errors to look for in a methodology critique?▼

Common scientific reasoning errors to look for in a methodology critique include correlation versus causation, p-hacking, and unfounded generalization. Identifying these errors allows for a proportional critique of logical and claim evaluation.