Cognitive fallacies guard

Cognitive Fallacies Guard

Table of Contents

• Read This First
• Fallacy Audit Workflow
• Path Selection Menu
  • Path 1: Visual Misleads Scan
  • Path 2: Cognitive Bias Check
  • Path 3: Data Integrity Verification
• Quick Reference
• Guardrails

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Read This First

What This Skill Does

This skill helps you detect and prevent visual misleads, cognitive biases, and data integrity violations in visualizations, dashboards, reports, and presentations.

Core principle: Visualizations are persuasive — designers have an ethical obligation to communicate honestly. Common mistakes aren't just aesthetic failures; they cause systematic misinterpretation.

Why It Matters

Problems caused by fallacies:

• Chartjunk consumes working memory without conveying data
• Truncated axes exaggerate differences and mislead comparisons
• 3D effects distort perception through volume illusions
• Cherry-picking misleads by omitting contradictory context
• Spurious correlations imply false causation

Why designers commit fallacies:

• Aesthetic appeal prioritized over clarity
• Unaware of cognitive impacts
• Following bad examples
• Intentional manipulation (sometimes)

When to Use This Skill

Use this skill when:

• ✓ Auditing visualizations for honesty before publication
• ✓ Reviewing charts and dashboards for misleading patterns
• ✓ Diagnosing why users misinterpret data
• ✓ Preventing common visualization mistakes during design
• ✓ Verifying data integrity in reports and presentations

Do NOT use for:

• ✗ General design evaluation (use design-evaluation-audit)
• ✗ Learning cognitive foundations (use cognitive-design)
• ✗ Creating new visualizations (use d3-visualization)
• ✗ Building data stories (use visual-storytelling-design)

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Fallacy Audit Workflow

Time: 15-30 minutes

Copy this checklist and track your progress:

Fallacy Audit Progress:
- [ ] Step 1: Scan for Visual Misleads
- [ ] Step 2: Check for Cognitive Biases
- [ ] Step 3: Verify Data Integrity

Step 1: Scan for Visual Misleads

Check for chartjunk, 3D effects, truncated axes, volume illusions, and inappropriate chart types. These are the most common and visible fallacies.

Resource: Fallacies Catalog — Sections 1-2 (Visual Noise, Perceptual Distortion)

Step 2: Check for Cognitive Biases

Look for confirmation bias reinforcement, anchoring effects, and framing manipulation. These are subtler but can significantly influence interpretation.

Resource: Fallacies Catalog — Section 3 (Cognitive Bias Exploitation)

Step 3: Verify Data Integrity

Confirm honest axes, complete data, fair comparisons, proper context, and no spurious correlations. This is the most critical layer.

Resource: Detection Patterns — Integrity Principles and Quick Scan Checklist

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Path Selection Menu

Path 1: Visual Misleads Scan

Choose this when: Checking for chartjunk, 3D effects, truncated axes, and encoding problems.

→ Go to Fallacies Catalog — Sections 1-2

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Path 2: Cognitive Bias Check

Choose this when: Looking for bias reinforcement in dashboard design, presentation framing, or data selection.

→ Go to Fallacies Catalog — Section 3

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Path 3: Data Integrity Verification

Choose this when: Verifying completeness, honesty, and context of data presentation.

→ Go to Detection Patterns

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Quick Reference

5 Integrity Principles

1. Honest Axes — Bar charts start at zero; uniform scale intervals; clear labels
2. Fair Comparisons — Same scale for compared items; no dual-axis manipulation
3. Complete Context — Full time period shown; baselines provided; denominators clarified
4. Accurate Encoding — Visual proportional to numerical; no volume illusions; 2D design
5. Transparency — Data sources cited; limitations acknowledged; methodology stated

Quick Severity Guide

• CRITICAL: Integrity violations (truncated bars without disclosure, cherry-picked data, implied causation)
• HIGH: Perceptual distortions (3D effects, volume illusions, missing denominators)
• MEDIUM: Bias reinforcement (one-sided framing, anchoring order, confirmation bias layout)
• LOW: Visual noise (excessive gridlines, decorative elements, ornamental borders)

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Guardrails

This skill does NOT: Create designs, evaluate general usability, teach cognitive theory, or assess aesthetic quality.

This skill DOES: Detect visual misleads, identify cognitive bias exploitation, verify data integrity, and provide specific fixes for each fallacy found.