To Cut or Not To Cut: A Systematic Exploration of Y-Axis Truncation
Sheng Long, Matthew Kay
ACM Human Factors in Computing Systems (CHI) 2024
Brief explanation of what a monotonic truncation is.
Abstract
Y-axis truncation is a well-known, much-debated visualization practice. Our work complements existing empirical work by providing a systematic analysis of y-axis truncation on grouped bar charts. Drawing upon theoretical frameworks such as Algebraic Visualization Design, we examine how structure-preserving modifications to visualization affect user performance by systematically dividing the space of possible truncations according to their monotonicity and the type of relations in the underlying data. Our results demonstrate that for comparing and estimating the difference between the lengths of two bars, truncating the y-axis does not affect task performance. For comparing or estimating the relative growth between two bars, truncating monotonically has similar performance to no truncation, while truncating non-monotonically is very likely to impair performance. We discuss possible extensions of our work and recommendations for y-axis truncation. All supplementary materials are available at https://osf.io/k4hjd/?view_only=008b087fc3d94be7ba0ce7aea95012a7.