Critical Analysis Framework

Critical Analysis Framework

This skill guides rigorous critical evaluation of claims, arguments, and research.

Phase 1: Content Mapping

Claim Extraction

Identify all claims in the material:

1. Central claim: The main argument or thesis
2. Supporting claims: Claims used to support the central claim
3. Implicit claims: Unstated assumptions
4. Hedged claims: Qualified or conditional statements

Argument Structure Mapping

Conclusion (Central Claim)
     ↑
Premise 1 + Premise 2 + Premise 3
     ↑           ↑           ↑
[Evidence]  [Evidence]  [Evidence]

Stakeholder Context

• Who created this content?
• What are their credentials?
• What are potential motivations/​interests?
• Who funded the work?

CHECKPOINT: Confirm scope of analysis with user.

Phase 2: Evidence Assessment

Evidence Inventory

Evidence Types Hierarchy

(Strongest to weakest)

1. Systematic reviews/​meta-analyses
2. Randomized controlled trials
3. Cohort studies
4. Case-control studies
5. Cross-sectional studies
6. Case reports
7. Expert opinion
8. Anecdote

Evidence Quality Markers

Strong evidence:

• Peer-reviewed
• Replicable methodology
• Adequate sample size
• Appropriate controls
• Transparent reporting

Weak evidence:

• Not peer-reviewed
• Vague methodology
• Small sample
• No controls
• Selective reporting

Phase 3: Logical Analysis

Deductive Validity Check

For deductive arguments:

• Are premises true?
• Does conclusion follow necessarily from premises?
• Is the logical form valid?

Inductive Strength Check

For inductive arguments:

• Is the sample representative?
• Is the sample large enough?
• Are there counterexamples?
• How strong is the correlation?

Common Fallacy Scan

Relevance Fallacies:

• Ad hominem (attacking person, not argument)
• Appeal to authority (authority as only evidence)
• Appeal to emotion (emotions instead of logic)
• Red herring (irrelevant distraction)

Presumption Fallacies:

• Begging the question (conclusion in premise)
• False dichotomy (only two options presented)
• Hasty generalization (insufficient sample)
• Slippery slope (unsupported chain)

Ambiguity Fallacies:

• Equivocation (shifting word meaning)
• Amphiboly (grammatical ambiguity)
• Composition (part → whole error)
• Division (whole → part error)

Causal Fallacies:

• Post hoc (sequence ≠ causation)
• Correlation/​causation confusion
• Single cause (ignoring multiple factors)
• Wrong direction (reversed causality)

Phase 4: Bias Detection

Cognitive Bias Scan

• Confirmation bias: Only supporting evidence cited
• Anchoring: Over-reliance on initial information
• Availability: Overweighting recent/​memorable
• Hindsight: "Knew it all along" framing
• Survivorship: Ignoring failures

Research Bias Scan

• Selection bias: Non-representative sampling
• Publication bias: Missing negative results
• Funding bias: Results favor funder
• Allegiance bias: Theory commitment
• Spin: Misleading presentation

Conflict of Interest Check

• Financial relationships?
• Ideological commitments?
• Career incentives?
• Institutional pressures?

CHECKPOINT: Present initial concerns for user input.

Phase 5: Methodology Critique

For Empirical Research

Design Assessment:

• Appropriate for research question?
• Adequate controls?
• Randomization where possible?
• Blinding implemented?

Internal Validity Threats:

• Selection: Non-equivalent groups
• History: External events
• Maturation: Natural changes
• Testing: Prior test effects
• Instrumentation: Measurement changes
• Regression: Extreme scores normalizing
• Attrition: Differential dropout

External Validity Threats:

• Population: Sample ≠ target population
• Setting: Lab ≠ real world
• Time: Results time-bound
• Treatment variation: Inconsistent implementation

Statistical Issues:

• Appropriate tests used?
• Assumptions checked?
• Multiple comparison corrections?
• Effect sizes reported?
• Power adequate?

Phase 6: Alternative Explanations

Alternative Hypothesis Generation

For each major finding, consider:

1. Could confounds explain this?
2. Could reverse causation explain this?
3. Could third variables explain this?
4. Could measurement artifacts explain this?
5. Could chance explain this?

Parsimony Assessment

• Are simpler explanations available?
• Does the complexity of the explanation match the evidence?
• Are extraordinary claims supported by extraordinary evidence?

Phase 7: Strength Assessment

Overall Quality Rating

Confidence Classification

• Strong: High-quality evidence, valid logic, minimal bias
• Moderate: Good evidence with some limitations
• Weak: Significant issues but some merit
• Very weak: Major flaws, unreliable conclusions
• Invalid: Fundamental errors, reject conclusions

Phase 8: Documentation

Output Structure

# Critical Analysis: [Title/​Topic]

## Summary
[Brief overview of what was analyzed]

## Central Claims
1. [Main claim]
2. [Supporting claims]

## Evidence Assessment
| Claim | Evidence | Type | Quality |
|-------|----------|------|---------|
| [Claim] | [Evidence] | [Type] | [Rating] |

## Logical Issues
1. [Issue]: [Explanation]
2. [Issue]: [Explanation]

## Bias Concerns
- [Bias type]: [How it manifests]

## Methodology Critique
- [Issue]: [Impact on validity]

## Alternative Explanations
1. [Alternative]: [Why plausible]
2. [Alternative]: [Why plausible]

## Strengths
- [Strength 1]
- [Strength 2]

## Weaknesses
- [Weakness 1]
- [Weakness 2]

## Overall Assessment
**Rating**: [Strong/​Moderate/​Weak/​Very Weak]
**Key Concern**: [Most significant issue]
**Recommendation**: [Accept/​Accept with caveats/​Reject/​Need more information]

CHECKPOINT: Review analysis completeness with user.