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| Content Provider | ACM Digital Library |
|---|---|
| Author | Upfal, Eli De Stefani, Lorenzo Kraska, Tim Zgraggen, Emanuel Zhao, Zheguang Binnig, Carsten |
| Abstract | Exploring data via visualization has become a popular way to understand complex data. Features or patterns in visualization can be perceived as relevant insights by users, even though they may actually arise from random noise. Moreover, interactive data exploration and visualization recommendation tools can examine a large number of observations, and therefore result in further increasing chance of spurious insights. Thus without proper statistical control, the risk of false discovery renders visual data exploration unsafe and makes users susceptible to questionable inference.To address these problems, we present QUDE, a visual data exploration system that interacts with users to formulate hypotheses based on visualizations and provides interactive control of false discoveries. |
| Starting Page | 1671 |
| Ending Page | 1674 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450341974 |
| DOI | 10.1145/3035918.3058749 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-05-09 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Hypothesis testing Visualization Multiple comparison problem Visual analytics False discovery control Progressive computation Interactive data exploration |
| Content Type | Text |
| Resource Type | Article |
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