TY - GEN
T1 - Truncating the Y-Axis
T2 - 2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020
AU - Correll, Michael
AU - Bertini, Enrico
AU - Franconeri, Steven
N1 - Funding Information:
This work was supported by NSF awards CHS-1901485 and CHS-1900941. Thanks to Elsie Lee and Evan Anderson for assistance in qualitative coding.
Publisher Copyright:
© 2020 ACM.
PY - 2020/4/21
Y1 - 2020/4/21
N2 - 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.
AB - 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.
KW - deceptive visualization
KW - information visualization
UR - http://www.scopus.com/inward/record.url?scp=85091315727&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091315727&partnerID=8YFLogxK
U2 - 10.1145/3313831.3376222
DO - 10.1145/3313831.3376222
M3 - Conference contribution
AN - SCOPUS:85091315727
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 25 April 2020 through 30 April 2020
ER -