Graphical Inference for Infovis

被引：76

作者：

Wickham, Hadley ^{[1
]}

Cook, Dianne ^{[2
]}

Hofmann, Heike ^{[2
]}

Buja, Andreas ^{[3
]}

机构：

[1] Rice Univ, Houston, TX 77251 USA

[2] Iowa State Univ, Ames, IA 50011 USA

[3] Univ Penn, Wharton Sch, Philadelphia, PA 19104 USA

来源：

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS | 2010年 / 16卷 / 06期

基金：

美国国家科学基金会;

关键词：

Statistics; visual testing; permutation tests; null hypotheses; data plots;

D O I：

10.1109/TVCG.2010.161

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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 the "line-up" provides a protocol for assessing the significance of visual discoveries, protecting against the discovery of spurious structure.

引用

页码：973 / 979

页数：7

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