What problem does it solve? Researchers, reviewers, and students often struggle to systematically assess whether a study's conclusions are actually supported by its methods and data. This Skill provides structured frameworks for critiquing experimental design, detecting biases, evaluating statistics, and grading evidence quality. ## Core Features & Use Cases - Methodology and Design Critique: Assess internal, external, construct, and statistical conclusion validity, plus randomization, blinding, and control adequacy. - Bias and Fallacy Detection: Identify cognitive, selection, measurement, and analysis biases (p-hacking, HARKing, publication bias) and name specific logical fallacies in scientific arguments. - Evidence Grading: Apply the GRADE system, Cochrane Risk of Bias, and evidence hierarchies to weigh conflicting findings and calibrate confidence in conclusions. - Use Case: When reviewing a manuscript claiming a supplement improves memory, use this Skill to check the randomization procedure, sample size justification, outcome reporting, and whether causal language is justified by the correlational design. ## Quick Start Ask the AI to critically evaluate the methodology and evidence quality of an attached research paper using the scientific-critical-thinking skill.
