cognitive-fallacies-guard

---
name: cognitive-fallacies-guard
description: Use when detecting and preventing visual misleads, cognitive biases, and design failures in data visualizations, dashboards, reports, or presentations. Invoke when user mentions chartjunk, misleading chart, truncated axis, data integrity, visual deception, 3D chart problems, cherry-picking data, or needs to audit visualizations for honesty and accuracy.
license: Unspecified
---
# Cognitive Fallacies Guard

## Table of Contents

- [Read This First](#read-this-first)
- [Fallacy Audit Workflow](#fallacy-audit-workflow)
- [Path Selection Menu](#path-selection-menu)
  - [Path 1: Visual Misleads Scan](#path-1-visual-misleads-scan)
  - [Path 2: Cognitive Bias Check](#path-2-cognitive-bias-check)
  - [Path 3: Data Integrity Verification](#path-3-data-integrity-verification)
- [Quick Reference](#quick-reference)
- [Guardrails](#guardrails)

---

## 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`)

---

## 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](resources/fallacies-catalog.md) — 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](resources/fallacies-catalog.md) — 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](resources/detection-patterns.md) — Integrity Principles and Quick Scan Checklist

---

## 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](resources/fallacies-catalog.md) — Sections 1-2**

---

### Path 2: Cognitive Bias Check

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

**→ [Go to Fallacies Catalog](resources/fallacies-catalog.md) — Section 3**

---

### Path 3: Data Integrity Verification

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

**→ [Go to Detection Patterns](resources/detection-patterns.md)**

---

## 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)

---

## 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.