Practice data visualization best practices vocabulary: chart junk, data-ink ratio, misleading y-axis, annotations, and language for describing and critiquing visualizations.
0 / 5 completed
1 / 5
Edward Tufte coined the term 'chart junk'. What does it mean?
Chart junk (Tufte, 'The Visual Display of Quantitative Information') refers to decorative visual elements that add clutter without adding information — 3D bars, shading, excessive gridlines, clipart. The rule: every pixel should earn its place by encoding data.
2 / 5
What is the 'data-ink ratio' principle?
Data-ink ratio = data ink / total ink used. Tufte argues for maximizing it — remove every element that doesn't encode information. A chart with a high data-ink ratio communicates more clearly with less visual noise.
3 / 5
A chart has a y-axis that starts at 950 instead of 0, making a small difference look dramatic. What is this called?
A truncated y-axis starts above zero, making small percentage differences appear dramatic. This can be intentional manipulation or careless design. When reviewing charts, always check: 'Does this y-axis start at zero? If not, does the context justify the truncation?'
4 / 5
What is the purpose of 'annotations' in a data visualization?
Annotations add meaning to data points. Instead of making viewers interpret why there's a spike, you annotate: 'Launch of Feature X.' Good annotations tell the story within the chart. You'd say: 'I've added an annotation here to explain the October drop — that's when we changed the pricing model.'
5 / 5
Which phrase would you use to critique a visualization that makes a small difference look misleadingly large?
When critiquing visualizations, be specific and technical: 'This truncated y-axis exaggerates the difference.' Or: 'The data-ink ratio here is low — removing the background gridlines and 3D effect would make the actual values much clearer.' This demonstrates visual literacy.