What problem does it solve?
This Skill helps you evaluate whether a scientific claim is supported by rigorous methods and trustworthy evidence, rather than by bias, flawed design, or statistical misinterpretation.
Core Features & Use Cases
- Methodology Critique: Assess research rigor by examining study design fit, validity threats (internal/external/construct/statistical conclusion), control and blinding, and measurement quality.
- Bias Detection: Identify common sources of distortion across cognitive, selection, measurement, analysis, and confounding biases using established taxonomies.
- Statistical Analysis Evaluation: Review sample size/power, test appropriateness, multiple comparisons, p-value and confidence interval interpretation, effect sizes, missing data handling, and modeling pitfalls.
- Evidence Quality Assessment: Grade evidence strength using study-type hierarchies and GRADE-style reasoning, including convergence, context, and risk-of-bias considerations.
- Logical Fallacy Identification: Detect science-specific reasoning errors (e.g., correlation vs causation, cherry-picking, base-rate neglect) and explain what evidence would be needed instead.
- Research Design Guidance: Provide constructive guidance for planning rigorous studies, including preregistration, bias minimization strategies, and analysis planning.
- Claim Evaluation: Critically evaluate what conclusions follow from the presented evidence, flag overgeneralization and red flags, and provide proportionate feedback.
Quick Start
Use the scientific-critical-thinking skill to evaluate a research paper by checking its methodology, biases, statistics, evidence strength, and whether the conclusions logically follow from the data.
