METHODOLOGY
How we identify, classify, and document data visualization crimes. Our standards ensure every case is thoroughly investigated.
CRIME CLASSIFICATION SYSTEM
Egregious violations that fundamentally distort interpretation
- Deliberately misleading core claims
- Truncated axes that distort comparison by >50%
- Cherry-picked datasets without disclosure
Significant violations that materially affect understanding
- Misleading scales or aspect ratios
- Unlabeled axes or missing units
- Inconsistent intervals or categories
Minor issues that reduce clarity without major distortion
- Minor presentation issues
- Suboptimal color choices
- Missing but inferable context
EVIDENCE STANDARDS
Primary Source Required
Original announcement, blog post, or presentation
Screenshot Evidence
Unaltered capture with visible URL/date
Context Documentation
Surrounding claims and methodology stated
Comparison Baseline
What accurate representation would look like
REVIEW PROCESS
WHAT GOOD LOOKS LIKE
The principles we use to evaluate data visualizations. These aren't arbitrary rules—they're based on decades of research in perception and communication.
Start axes at zero
Unless there's a valid reason not to, clearly disclosed
Use consistent scales
Don't manipulate perception through aspect ratio
Label everything
Axes, units, time periods, data sources
Show uncertainty
Error bars, confidence intervals, sample sizes
Provide context
Baselines, comparisons, industry standards
Disclose limitations
What the data can and cannot show
HELP US DOCUMENT CHART CRIMES
Spotted a misleading chart? Submit evidence and help hold companies accountable for their data visualization choices.
RESOURCES
Recommended Reading
- How to Lie with Statistics — Darrell Huff
- The Visual Display of Quantitative Information — Edward Tufte
- Calling Bullshit — Carl Bergstrom & Jevin West
Related Projects
- WTF Visualizations
- Junk Charts
- r/dataisugly