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
Link To Document