2014/08/11 by Zan Armstrong, Martin Wattenberg · 2 citations
Computer Science · Mathematics · #Data Visualization and Analytics #Data Analysis with R #Statistics Education and Methodologies
paper · pdf · doi:10.1109/tvcg.2014.2346297
openalex publication_date 2014/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/26
We discuss how "mix effects" can surprise users of visualizations and potentially lead them to incorrect conclusions. This statistical issue (also known as "omitted variable bias" or, in extreme cases, as "Simpson's paradox") is widespread and can affect any visualization in which the quantity of interest is an aggregated value such as a weighted sum or average. Our first contribution is to document how mix effects can be a serious issue for visualizations, and we analyze how mix effects can cause problems in a variety of popular visualization techniques, from bar charts to treemaps. Our second contribution is a new technique, the "comet chart," that is meant to ameliorate some of these issues.