  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.
