Analysis Scientific Critical Thinking | Skills Pool
技能档案
Analysis Scientific Critical Thinking
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
Critical thinking is a systematic process for evaluating scientific rigor. Assess methodology, experimental design, statistical validity, biases, confounding, and evidence quality using GRADE and Cochrane ROB frameworks. Apply this skill for critical analysis of scientific claims.
When to Use This Skill
This skill should be used when:
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
Visual Enhancement with Scientific Schematics
When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.
相关技能
If your document does not already contain schematics or diagrams:
Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
Simply describe your desired diagram in natural language
Nano Banana Pro will automatically generate, review, and refine the schematic
For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.
Don't apply stricter standards to findings you dislike
Acknowledge your own potential biases
Base judgments on methodology, not results
Consider Context
Acknowledge practical and ethical constraints
Consider field-specific norms for effect sizes and methods
Recognize exploratory vs. confirmatory contexts
Account for resource limitations in evaluating studies
When Providing Critique
Structure feedback as:
Summary: Brief overview of what was evaluated
Strengths: What was done well (important for credibility and learning)
Concerns: Issues organized by severity
Critical issues (threaten validity of main conclusions)
Important issues (affect interpretation but not fatally)
Minor issues (worth noting but don't change conclusions)
Specific Recommendations: Actionable suggestions for improvement
Overall Assessment: Balanced conclusion about evidence quality and what can be concluded
Use precise terminology:
Name specific biases, fallacies, and methodological issues
Reference established standards and guidelines
Cite principles from scientific methodology
Use technical terms accurately
When Uncertain
Acknowledge uncertainty: "This could be X or Y; additional information needed is Z"
Ask clarifying questions: "Was [methodological detail] done? This affects interpretation."
Provide conditional assessments: "If X was done, then Y follows; if not, then Z is concern"
Note what additional information would resolve uncertainty
Reference Materials
This skill includes comprehensive reference materials that provide detailed frameworks for critical evaluation:
references/scientific_method.md - Core principles of scientific methodology, the scientific process, critical evaluation criteria, red flags in scientific claims, causal inference standards, peer review, and open science principles
references/common_biases.md - Comprehensive taxonomy of cognitive, experimental, methodological, statistical, and analysis biases with detection and mitigation strategies
references/statistical_pitfalls.md - Common statistical errors and misinterpretations including p-value misunderstandings, multiple comparisons problems, sample size issues, effect size mistakes, correlation/causation confusion, regression pitfalls, and meta-analysis issues
references/evidence_hierarchy.md - Traditional evidence hierarchy, GRADE system, study quality assessment criteria, domain-specific considerations, evidence synthesis principles, and practical decision frameworks
references/logical_fallacies.md - Logical fallacies common in scientific discourse organized by type (causation, generalization, authority, relevance, structure, statistical) with examples and detection strategies
references/experimental_design.md - Comprehensive experimental design checklist covering research questions, hypotheses, study design selection, variables, sampling, blinding, randomization, control groups, procedures, measurement, bias minimization, data management, statistical planning, ethical considerations, validity threats, and reporting standards
When to consult references:
Load references into context when detailed frameworks are needed
Use grep to search references for specific topics: grep -r "pattern" references/
References provide depth; SKILL.md provides procedural guidance
Consult references for comprehensive lists, detailed criteria, and specific examples
Remember
Scientific critical thinking is about:
Systematic evaluation using established principles
Constructive critique that improves science
Proportional confidence to evidence strength
Transparency about uncertainty and limitations
Consistent application of standards
Recognition that all research has limitations
Balance between skepticism and openness to evidence
Always distinguish between:
Data (what was observed) and interpretation (what it means)
Correlation and causation
Statistical significance and practical importance
Exploratory and confirmatory findings
What is known and what is uncertain
Evidence against a claim and evidence for the null