• DocumentCode
    1364669
  • Title

    Graphical inference for infovis

  • Author

    Wickham, Hadley ; Cook, Dianne ; Hofmann, Heike ; Buja, Andreas

  • Author_Institution
    Rice Univ., Houston, TX, USA
  • Volume
    16
  • Issue
    6
  • fYear
    2010
  • Firstpage
    973
  • Lastpage
    979
  • 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.
  • Keywords
    data visualisation; statistics; data visualization; graphical inference; infovis; statistical inference; Accuracy; Histograms; Protocols; Tag clouds; Testing; Visualization; Statistics; data plot; null hypotheses; permutation tests; visual testing; Computer Graphics; Data Interpretation, Statistical; Databases, Factual; Humans; Models, Statistical; Neoplasms;
  • fLanguage
    English
  • Journal_Title
    Visualization and Computer Graphics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1077-2626
  • Type

    jour

  • DOI
    10.1109/TVCG.2010.161
  • Filename
    5613434