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Distinguishability

As explained in Everyday Data Visualization

Distinguishability is the ease with which visually similar characters can be told apart and identified.

Distinguishability is how easily visually similar characters can be told apart. In data visualization, it matters especially for small axis labels, data labels, annotations, and captions, where letter shape becomes part of readability. section 5.2.1

Use distinguishability as a practical check when choosing fonts for compact visualizations:

  • Prefer a regular weight for most small text; very thin and very heavy weights can lose the balance between ink and white space. section 5.2.1
  • Reserve extreme weights for larger titles or headers, or use heavier weight sparingly for emphasis. section 5.2.1

How Everyday Data Visualization covers it

How deep it goes
Treats distinguishability as a qualitative font-choice check alongside weight, x-height, and letter shape.
Examples
Uses a figure comparing three typefaces and three frequently confused characters.
What you can do after
Evaluate fonts by comparing potentially confused characters at the small sizes used in a visualization.
How it connects
Connects distinguishability with readability, font weight, x-height, letter shape, and white space.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Everyday Data Visualization. The section links point to the parts of the book it draws on.