Looks Good to Me: Visualizations As Sanity Checks

Michael Correll, Mingwei Li, Gordon Kindlmann, Carlos Scheidegger

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


Famous examples such as Anscombe's Quartet highlight that one of the core benefits of visualizations is allowing people to discover visual patterns that might otherwise be hidden by summary statistics. This visual inspection is particularly important in exploratory data analysis, where analysts can use visualizations such as histograms and dot plots to identify data quality issues. Yet, these visualizations are driven by parameters such as histogram bin size or mark opacity that have a great deal of impact on the final visual appearance of the chart, but are rarely optimized to make important features visible. In this paper, we show that data flaws have varying impact on the visual features of visualizations, and that the adversarial or merely uncritical setting of design parameters of visualizations can obscure the visual signatures of these flaws. Drawing on the framework of Algebraic Visualization Design, we present the results of a crowdsourced study showing that common visualization types can appear to reasonably summarize distributional data while hiding large and important flaws such as missing data and extraneous modes. We make use of these results to propose additional best practices for visualizations of distributions for data quality tasks.

Original languageEnglish (US)
Article number8440818
Pages (from-to)830-839
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number1
StatePublished - Jan 2019


  • Graphical perception
  • data quality
  • univariate visualizations

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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