Core Capabilities

The seven capability areas in full: methodology critique, bias detection, statistical analysis evaluation, evidence quality assessment, logical fallacy identification, research design guidance, and claim evaluation — each with the questions to ask and what the answers imply.

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:

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
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
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?
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:

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
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
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
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
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:

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?
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)?
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?
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?
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?
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?
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?
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:

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.

Quality Within Design Type
Risk of bias assessment (use appropriate tool: Cochrane RoB 2 for RCTs, ROBINS-I for non-randomized studies, Newcastle-Ottawa, etc.)
Methodological rigor
Transparency and reporting completeness
Conflicts of interest
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
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
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:

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
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
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"
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)
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
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.

6. Research Design Guidance

Provide constructive guidance for planning rigorous studies.

Apply when: - Helping design new experiments - Planning research projects - Reviewing research proposals - Improving study protocols

Design process:

Research Question Refinement
Ensure question is specific, answerable, and falsifiable
Verify it addresses a gap or contradiction in literature
Confirm feasibility (resources, ethics, time)
Define variables operationally
Design Selection
Match design to question (causal → experimental; associational → observational)
Consider feasibility and ethical constraints
Choose between-subjects, within-subjects, or mixed designs
Plan factorial designs if testing multiple factors
Bias Minimization Strategy
Implement randomization when possible
Plan blinding at all feasible levels (participants, providers, assessors)
Identify and plan to control confounds (randomization, matching, stratification, statistical adjustment)
Standardize all procedures
Plan to minimize attrition
Sample Planning
Conduct a priori power analysis (specify expected effect, desired power, alpha)
Account for attrition in sample size
Define clear inclusion/exclusion criteria
Consider recruitment strategy and feasibility
Plan for sample representativeness
Measurement Strategy
Select validated, reliable instruments
Use objective measures when possible
Plan multiple measures of key constructs (triangulation)
Ensure measures are sensitive to expected changes
Establish inter-rater reliability procedures
Analysis Planning
Prespecify all hypotheses and analyses
Designate primary outcome clearly
Plan statistical tests with assumption checks
Specify how missing data will be handled
Plan to report effect sizes and confidence intervals
Consider multiple comparison corrections
Transparency and Rigor
Preregister study and analysis plan
Use reporting guidelines (CONSORT, STROBE, PRISMA)
Plan to report all outcomes, not just significant ones
Distinguish confirmatory from exploratory analyses
Commit to data/code sharing

Reference: See references/experimental_design.md for comprehensive design checklist covering all stages from question to dissemination.

7. Claim Evaluation

Systematically evaluate scientific claims for validity and support.

Apply when: - Assessing conclusions in papers - Evaluating media reports of research - Reviewing abstract or introduction claims - Checking if data support conclusions

Claim evaluation process:

Identify the Claim
What exactly is being claimed?
Is it a causal claim, associational claim, or descriptive claim?
How strong is the claim (proven, likely, suggested, possible)?
Assess the Evidence
What evidence is provided?
Is evidence direct or indirect?
Is evidence sufficient for the strength of claim?
Are alternative explanations ruled out?
Check Logical Connection
Do conclusions follow from the data?
Are there logical leaps?
Is correlational data used to support causal claims?
Are limitations acknowledged?
Evaluate Proportionality
Is confidence proportional to evidence strength?
Are hedging words used appropriately?
Are limitations downplayed?
Is speculation clearly labeled?
Check for Overgeneralization
Do claims extend beyond the sample studied?
Are population restrictions acknowledged?
Is context-dependence recognized?
Are caveats about generalization included?
Red Flags
Causal language from correlational studies
"Proves" or absolute certainty
Cherry-picked citations
Ignoring contradictory evidence
Dismissing limitations
Extrapolation beyond data

Provide specific feedback: - Quote the problematic claim - Explain what evidence would be needed to support it - Suggest appropriate hedging language if warranted - Distinguish between data (what was found) and interpretation (what it means)