The Community for Technology Leaders
Green Image
Issue No. 06 - November/December (2010 vol. 16)
ISSN: 1077-2626
pp: 973-979
Hadley Wickham , Rice University
Dianne Cook , Iowa State University
Heike Hofmann , Iowa State University
Andreas Buja , Wharton School, University of Pennsylvania
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.
Statistics, visual testing, permutation tests, null hypotheses, data plot

H. Hofmann, H. Wickham, D. Cook and A. Buja, "Graphical inference for infovis," in IEEE Transactions on Visualization & Computer Graphics, vol. 16, no. , pp. 973-979, 2010.
95 ms
(Ver 3.3 (11022016))