Research Methods, Evidence Evaluation & Critical Thinking (Non-Metrics-Systems)
Research Methods, Evidence Evaluation & Critical Thinking (Non-Metrics-Systems)
- Claim decomposition: Identify what exactly is asserted (scope, causal direction, conditions).
- Evidence type classification: Observational vs experimental vs mechanistic vs expert consensus vs case evidence.
- Fit-for-claim evaluation: Whether the evidence type can support the strength of the claim.
- Study design integrity: Selection bias, confounding, randomization, blinding, measurement validity.
- Causal inference discipline: Distinguish correlation from causation; identify plausible causal graphs and alternative paths.
- Replication and robustness: Convergence across studies, methods, populations, and time.
- Uncertainty quantification: Confidence, error sources, and what remains unknown (without pretending certainty).
- Alternative hypotheses and refutation: Whether rival explanations were tested and what evidence would falsify the claim.
- Base-rate and prior sensitivity: How priors affect interpretation; avoid base-rate neglect.
- Incentive and conflict-of-interest analysis: Publication bias, funding bias, career incentives, ideology.
- Argument validity: Logical coherence, hidden premises, fallacies, category errors.
- Generalizability boundaries: Who/where/when findings apply; external validity.
- Decision relevance: What evidence changes the decision; marginal value of more information.
- Misuse risk: How “critical thinking” becomes a rhetorical weapon or cynicism engine.
- Operational outputs: Whether the method yields actionable judgments (confidence levels, conditional adoption, stop rules).