Truncating the Y-Axis: Threat or Menace?
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Abstract (arXiv)
Bar charts with y-axes that don't begin at zero can visually exaggerate effect sizes. However, advice for whether or not to truncate the y-axis can be equivocal for other visualization types. In this paper we present examples of visualizations where this y-axis truncation can be beneficial as well as harmful, depending on the communicative and analytic intent. We also present the results of a series of crowd-sourced experiments in which we examine how y-axis truncation impacts subjective effect size across visualization types, and we explore alternative designs that more directly alert viewers to this truncation. We find that the subjective impact of axis truncation is persistent across visualizations designs, even for designs with explicit visual cues that indicate truncation has taken place. We suggest that designers consider the scale of the meaningful effect sizes and variation they intend to communicate, regardless of the visual encoding.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jul 2019.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2024How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?cited by 27
- 2025The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Modelscited by 5
- 2025VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inferencecited by 3
- 2026Chart Deception in Vision-Language Models: From Vulnerability to Mitigationcited by 0
- 2026Chart Deception in Vision-Language Models: From Vulnerability to Mitigation12 Jun 2026
- 2025VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference21 Dec 2025
- 2025The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Models13 Aug 2025
- 2024How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?24 Jul 2024