Graphical inference for infovis

Hadley Wickham, Dianne Cook, Heike Hofmann, Andreas Buja

Research output: Contribution to journalArticleResearchpeer-review

50 Citations (Scopus)

Abstract

How do we know if what we see is really there? When visualizing data, how do we avoid falling into the trap of apophenia where we see patterns in random noise? Traditionally, infovis has been concerned with discovering new relationships, and statistics with preventing spurious relationships from being reported. We pull these opposing poles closer with two new techniques for rigorous statistical inference of visual discoveries. The "Rorschach" helps the analyst calibrate their understanding of uncertainty and "line-up" provides a protocol for assessing the significance of visual discoveries, protecting against the discovery of spurious structure.

Original languageEnglish
Article number5613434
Pages (from-to)973-979
Number of pages7
JournalIEEE Transactions on Visualization and Computer Graphics
Volume16
Issue number6
DOIs
Publication statusPublished - 2010

Keywords

  • data plot
  • null hypotheses
  • permutation tests
  • Statistics
  • visual testing

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