Data Visualization Error Patterns Guide

Review my visualization for common errors and anti-patterns:

Visualization Description:
[Describe your visualization in detail or paste code/configuration]

Current Chart Type: [specify]
Data Being Shown: [describe]
Intended Message: [what should viewers understand]
Audience: [who will see this]

Check for These Error Categories:

1. Data Integrity Issues
   - Truncated axes
   - Non-zero baselines
   - Inconsistent scales
   - Cherry-picked data
   - Missing context

2. Visual Encoding Errors
   - Inappropriate chart type
   - Poor color choices
   - 3D distortion
   - Dual axis misuse
   - Area vs length confusion

3. Cognitive Overload
   - Too many categories
   - Excessive decoration
   - Cluttered labels
   - Information overload
   - Unclear hierarchy

4. Misleading Representations
   - Aspect ratio manipulation
   - Inconsistent intervals
   - Cumulative vs discrete confusion
   - Percentage problems
   - Correlation vs causation implied

5. Design Issues
   - Poor contrast
   - Missing legends
   - Unclear titles
   - No data source
   - Accessibility failures

6. Interaction Problems
   - Hidden functionality
   - Unclear affordances
   - Broken states
   - Performance issues

Provide:
- Issues identified with severity
- Before/after recommendations
- Corrected implementation
- Principles to remember