Scientific Critical Thinking

Scientific Critical Thinking

Overview

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.

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.

How to generate schematics:

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

• Create publication-quality images with proper formatting
• Review and refine through multiple iterations
• Ensure accessibility (colorblind-friendly, high contrast)
• Save outputs in the figures/ directory

When to add schematics:

• Critical thinking framework diagrams
• Bias identification decision trees
• Evidence quality assessment flowcharts
• GRADE assessment methodology diagrams
• Risk of bias evaluation frameworks
• Validity assessment visualizations
• Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

Core Capabilities

1. Methodology Critique

Evaluate research methodology for rigor, validity, and potential flaws.

Apply when:

• Reviewing research papers
• Assessing experimental designs
• Evaluating study protocols
• Planning new research

Evaluation framework:

1. Study Design Assessment

   • Is the design appropriate for the research question?
   • Can the design support causal claims being made?
   • Are comparison groups appropriate and adequate?
   • Consider whether experimental, quasi-experimental, or observational design is justified

2. Validity Analysis

   • Internal validity: Can we trust the causal inference?
     • Check randomization quality
     • Evaluate confounding control
     • Assess selection bias
     • Review attrition/dropout patterns
   • External validity: Do results generalize?
     • Evaluate sample representativeness
     • Consider ecological validity of setting
     • Assess whether conditions match target application
   • Construct validity: Do measures capture intended constructs?
     • Review measurement validation
     • Check operational definitions
     • Assess whether measures are direct or proxy
   • Statistical conclusion validity: Are statistical inferences sound?
     • Verify adequate power/sample size
     • Check assumption compliance
     • Evaluate test appropriateness

3. Control and Blinding

   • Was randomization properly implemented (sequence generation, allocation concealment)?
   • Was blinding feasible and implemented (participants, providers, assessors)?
   • Are control conditions appropriate (placebo, active control, no treatment)?
   • Could performance or detection bias affect results?

4. Measurement Quality

   • Are instruments validated and reliable?
   • Are measures objective when possible, or subjective with acknowledged limitations?
   • Is outcome assessment standardized?
   • Are multiple measures used to triangulate findings?

Reference: See references/scientific_method.md for detailed principles and references/experimental_design.md for comprehensive design checklist.

2. Bias Detection

Identify and evaluate potential sources of bias that could distort findings.

Apply when:

• Reviewing published research
• Designing new studies
• Interpreting conflicting evidence
• Assessing research quality

Systematic bias review:

1. Cognitive Biases (Researcher)

   • Confirmation bias: Are only supporting findings highlighted?
   • HARKing: Were hypotheses stated a priori or formed after seeing results?
   • Publication bias: Are negative results missing from literature?
   • Cherry-picking: Is evidence selectively reported?
   • Check for preregistration and analysis plan transparency

2. Selection Biases

   • Sampling bias: Is sample representative of target population?
   • Volunteer bias: Do participants self-select in systematic ways?
   • Attrition bias: Is dropout differential between groups?
   • Survivorship bias: Are only "survivors" visible in sample?
   • Examine participant flow diagrams and compare baseline characteristics

3. Measurement Biases

   • Observer bias: Could expectations influence observations?
   • Recall bias: Are retrospective reports systematically inaccurate?
   • Social desirability: Are responses biased toward acceptability?
   • Instrument bias: Do measurement tools systematically err?
   • Evaluate blinding, validation, and measurement objectivity

4. Analysis Biases

   • P-hacking: Were multiple analyses conducted until significance emerged?
   • Outcome switching: Were non-significant outcomes replaced with significant ones?
   • Selective reporting: Are all planned analyses reported?
   • Subgroup fishing: Were subgroup analyses conducted without correction?
   • Check for study registration and compare to published outcomes

5. Confounding

   • What variables could affect both exposure and outcome?
   • Were confounders measured and controlled (statistically or by design)?
   • Could unmeasured confounding explain findings?
   • Are there plausible alternative explanations?

Reference: See references/common_biases.md for comprehensive bias taxonomy with detection and mitigation strategies.

3. Statistical Analysis Evaluation

Critically assess statistical methods, interpretation, and reporting.

Apply when:

• Reviewing quantitative research
• Evaluating data-driven claims
• Assessing clinical trial results
• Reviewing meta-analyses

Statistical review checklist:

1. Sample Size and Power

   • Was a priori power analysis conducted?
   • Is sample adequate for detecting meaningful effects?
   • Is the study underpowered (common problem)?
   • Do significant results from small samples raise flags for inflated effect sizes?

2. Statistical Tests

   • Are tests appropriate for data type and distribution?
   • Were test assumptions checked and met?
   • Are parametric tests justified, or should non-parametric alternatives be used?
   • Is the analysis matched to study design (e.g., paired vs. independent)?

3. Multiple Comparisons

   • Were multiple hypotheses tested?
   • Was correction applied (Bonferroni, FDR, other)?
   • Are primary outcomes distinguished from secondary/exploratory?
   • Could findings be false positives from multiple testing?

4. P-Value Interpretation

   • Are p-values interpreted correctly (probability of data if null is true)?
   • Is non-significance incorrectly interpreted as "no effect"?
   • Is statistical significance conflated with practical importance?
   • Are exact p-values reported, or only "p < .05"?
   • Is there suspicious clustering just below .05?

5. Effect Sizes and Confidence Intervals

   • Are effect sizes reported alongside significance?
   • Are confidence intervals provided to show precision?
   • Is the effect size meaningful in practical terms?
   • Are standardized effect sizes interpreted with field-specific context?

6. Missing Data

   • How much data is missing?
   • Is missing data mechanism considered (MCAR, MAR, MNAR)?
   • How is missing data handled (deletion, imputation, maximum likelihood)?
   • Could missing data bias results?

7. Regression and Modeling

   • Is the model overfitted (too many predictors, no cross-validation)?
   • Are predictions made outside the data range (extrapolation)?
   • Are multicollinearity issues addressed?
   • Are model assumptions checked?

8. Common Pitfalls

   • Correlation treated as causation
   • Ignoring regression to the mean
   • Base rate neglect
   • Texas sharpshooter fallacy (pattern finding in noise)
   • Simpson's paradox (confounding by subgroups)

Reference: See references/statistical_pitfalls.md for detailed pitfalls and correct practices.

4. Evidence Quality Assessment

Evaluate the strength and quality of evidence systematically.

Apply when:

• Weighing evidence for decisions
• Conducting literature reviews
• Comparing conflicting findings
• Determining confidence in conclusions

Evidence evaluation framework:

1. Study Design Hierarchy

   • Systematic reviews/meta-analyses (highest for intervention effects)
   • Randomized controlled trials
   • Cohort studies
   • Case-control studies
   • Cross-sectional studies
   • Case series/reports
   • Expert opinion (lowest)

   Important: Higher-level designs aren't always better quality. A well-designed observational study can be stronger than a poorly-conducted RCT.

2. Quality Within Design Type

   • Risk of bias assessment (use appropriate tool: Cochrane ROB, Newcastle-Ottawa, etc.)
   • Methodological rigor
   • Transparency and reporting completeness
   • Conflicts of interest

3. GRADE Considerations (if applicable)

   • Start with design type (RCT = high, observational = low)
   • Downgrade for:
     • Risk of bias
     • Inconsistency across studies
     • Indirectness (wrong population/intervention/outcome)
     • Imprecision (wide confidence intervals, small samples)
     • Publication bias
   • Upgrade for:
     • Large effect sizes
     • Dose-response relationships
     • Confounders would reduce (not increase) effect

4. Convergence of Evidence

   • Stronger when:
     • Multiple independent replications
     • Different research groups and settings
     • Different methodologies converge on same conclusion
     • Mechanistic and empirical evidence align
   • Weaker when:
     • Single study or research group
     • Contradictory findings in literature
     • Publication bias evident
     • No replication attempts

5. Contextual Factors

   • Biological/theoretical plausibility
   • Consistency with established knowledge
   • Temporality (cause precedes effect)
   • Specificity of relationship
   • Strength of association

Reference: See references/evidence_hierarchy.md for detailed hierarchy, GRADE system, and quality assessment tools.

5. Logical Fallacy Identification

Detect and name logical errors in scientific arguments and claims.

Apply when:

• Evaluating scientific claims
• Reviewing discussion/conclusion sections
• Assessing popular science communication
• Identifying flawed reasoning

Common fallacies in science:

1. Causation Fallacies

   • Post hoc ergo propter hoc: "B followed A, so A caused B"
   • Correlation = causation: Confusing association with causality
   • Reverse causation: Mistaking cause for effect
   • Single cause fallacy: Attributing complex outcomes to one factor

2. Generalization Fallacies

   • Hasty generalization: Broad conclusions from small samples
   • Anecdotal fallacy: Personal stories as proof
   • Cherry-picking: Selecting only supporting evidence
   • Ecological fallacy: Group patterns applied to individuals

3. Authority and Source Fallacies

   • Appeal to authority: "Expert said it, so it's true" (without evidence)
   • Ad hominem: Attacking person, not argument
   • Genetic fallacy: Judging by origin, not merits
   • Appeal to nature: "Natural = good/safe"

4. Statistical Fallacies

   • Base rate neglect: Ignoring prior probability
   • Texas sharpshooter: Finding patterns in random data
   • Multiple comparisons: Not correcting for multiple tests
   • Prosecutor's fallacy: Confusing P(E|H) with P(H|E)

5. Structural Fallacies

   • False dichotomy: "Either A or B" when more options exist
   • Moving goalposts: Changing evidence standards after they're met
   • Begging the question: Circular reasoning
   • Straw man: Misrepresenting arguments to attack them

6. Science-Specific Fallacies

   • Galileo gambit: "They laughed at Galileo, so my fringe idea is correct"
   • Argument from ignorance: "Not proven false, so true"
   • Nirvana fallacy: Rejecting imperfect solutions
   • Unfalsifiability: Making untestable claims

When identifying fallacies:

• Name the specific fallacy
• Explain why the reasoning is flawed
• Identify what evidence would be needed for valid inference
• Note that fallacious reasoning doesn't prove the conclusion false—just that this argument doesn't support it

Reference: See references/logical_fallacies.md for comprehensive fallacy catalog with examples and detection strategies.