Scientific Critical Thinking

Systematic process for evaluating scientific rigor through methodology assessment, experimental design review, statistical validity analysis, bias detection, and evidence quality evaluation using GRADE and Cochrane frameworks.

Key Benefits

• Systematic evaluation of research methodology and rigor
• Bias detection across cognitive, selection, measurement, and analysis domains
• Statistical analysis validation and pitfall identification
• Evidence quality assessment with GRADE considerations
• Logical fallacy identification in scientific arguments
• Research design guidance for planning rigorous studies
• Claim evaluation for validity and support

Core Capabilities

• Methodology Critique: Study design assessment, validity analysis (internal/external/construct/statistical), control and blinding, measurement quality
• Bias Detection: Cognitive biases, selection biases, measurement biases, analysis biases, confounding identification
• Statistical Evaluation: Sample size/power, test appropriateness, multiple comparisons, p-value interpretation, effect sizes/CI, missing data, regression modeling
• Evidence Assessment: Study design hierarchy, quality within design type, GRADE framework, evidence convergence, contextual factors
• Fallacy Identification: Causation fallacies, generalization fallacies, authority/source fallacies, statistical fallacies, structural fallacies
• Design Guidance: Question refinement, design selection, bias minimization, sample planning, measurement strategy, analysis planning, transparency

When to Use

• Evaluating research methodology and experimental design
• Assessing statistical validity and evidence quality
• Identifying biases and confounding in studies
• Reviewing scientific claims and conclusions
• Conducting systematic reviews or meta-analyses
• Applying GRADE or Cochrane risk of bias assessments
• Providing critical analysis of research papers
• Planning new research studies

Includes comprehensive reference materials: scientificmethod.md, commonbiases.md, statisticalpitfalls.md, evidencehierarchy.md, logicalfallacies.md, experimentaldesign.md

Source: https://github.com/K-Dense-AI/claude-scientific-writer/tree/main/skills/scientific-critical-thinking License: MIT