Guidelines
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Use a pie chart instead of an unscaled stacked bar for exact part-to-whole estimation

For exact part-to-whole estimation, prefer a pie chart over an unscaled stacked bar on two-segment part-to-whole displays to improve accuracy and mitigate error-prone share judgments for readers estimating a highlighted segment.

  • purpose:select
  • basis:empirical
  • chart:pie-donut:use
  • chart:bar:avoid
  • quality:fidelity:use
  • lever:chart-family
  • operator:part-whole
  • reading-mode:exact

advice

Choose the pie chart over the unscaled stacked bar

Use a pie chart when readers must estimate the exact size of one highlighted share in a two-part whole and the alternative is an unscaled stacked bar. For example, use a baseline pie chart instead of a baseline two-segment stacked bar when the reader must enter the darker segment as a whole-number percentage.

reason

Why the pie chart works better here

This design choice changes the chart family, not the data. In this specific two-segment exact-reading setup, the pie chart produced smaller estimation error than the unscaled stacked bar.

Mechanism: The pie chart gives readers a more accurate basis for estimating a single highlighted share in a two-part whole than the baseline stacked bar used in the comparison.

Evidence: In the baseline chart comparison, the pie chart ranked above the stacked bar for accuracy, and the difference was reported as significant. The review records this result as an empirical chart-selection finding for part-to-whole estimation (Redmond, 2019; Zeng & Battle, 2023).

Notes: This guideline applies to the baseline bar contrast tested in the source, not to all bar-chart variants.

context

Use when the chart matches the tested contrast

  • User Goal: Read the value of one highlighted share as accurately as possible.
  • Data: Two segments that sum to 100%.
  • Chart Setting: A single part-to-whole chart with no external quantitative scale and no added internal cue marks.
  • Success Criterion: Lower absolute error in estimated segment size.

exceptions

Do not use when the bar is no longer the baseline bar

Break it when: The alternative stacked bar includes an external quantitative scale or stronger internal cue marks. Why: The tested pie advantage was against the baseline unscaled bar, and the source also found more accurate refined bar variants.

costs

Costs of switching chart family

Sacrifice: You give up the bar chart version that can carry an external quantitative scale. Risk: You can overgeneralize this result if you apply it to every bar variant instead of the unscaled baseline bar. Mitigation: If the bar chart must stay, add a quantitative scale or stronger internal cues rather than leaving it as the baseline bar.

mistakes

Common selection mistake

Mistake: Keep an unscaled stacked bar because bar charts are assumed to be more accurate for part-to-whole estimation. Why it fails: In this tested two-segment exact-estimation setup, the baseline bar had higher estimation error than the baseline pie.

check

Check the chart choice with a direct A/B test

Failure Sign: Readers give noticeably inconsistent or inaccurate percentage estimates from the unscaled stacked bar. Quick Check: Build a matched pie-chart version and a matched unscaled stacked-bar version of the same two-part split, then ask reviewers to estimate the highlighted share as a whole number. Stronger Test: Compare absolute error across several share sizes rather than judging the choice from one example only.

fix

Fix the chart choice

  • Replace the unscaled stacked bar with a pie chart for the same two-part whole.
  • Keep the highlighted segment styling consistent across both versions when you compare them.
  • If the stacked bar must remain, add an external quantitative scale or stronger internal cues instead of leaving it unscaled.

References

Redmond, S. (2019). Visual Cues in Estimation of Part-To-Whole Comparisons. 2019 IEEE Visualization Conference (VIS), 1–5. https://doi.org/10.1109/VISUAL.2019.8933718
Zeng, Z., & Battle, L. (2023). A Review and Collation of Graphical Perception Knowledge for Visualization Recommendation. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3544548.3581349