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Controlling False Discoveries During Interactive Data Exploration

Zheguang Zhao; Lorenzo De Stefani; Emanuel Zgraggen; Carsten Binnig; Eli Upfal; Tim Kraska
In: Semih Salihoglu; Wenchao Zhou; Rada Chirkova; Jun Yang; Dan Suciu (Hrsg.). Proceedings of the 2017 ACM International Conference on Management of Data. ACM SIGMOD International Conference on Management of Data (SIGMOD-2017), May 14-19, Chicago, IL, USA, Pages 527-540, ACM, 2017.


Recent tools for interactive data exploration significantly increase the chance that users make false discoveries. The crux is that these tools implicitly allow the user to test a large body of different hypotheses with just a few clicks thus incurring in the issue commonly known in statistics as the multiple hypothesis testing error. In this paper, we propose solutions to integrate multiple hypothesis testing control into interactive data exploration tools. A key insight is that existing methods for controlling the false discovery rate (such as FDR) are not directly applicable for interactive data exploration. We therefore discuss a set of new control procedures that are better suited and integrated them in our system called Aware. By means of extensive experiments using both real-world and synthetic data sets we demonstrate how Aware can help experts and novice users alike to efficiently control false discoveries.

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