Wiggum: Interactive visual analytics for examining mix effects

The importance of data-driven decision-making is rapidly increasing thanks in part to the growing availability and accessibility of data sets and analysis tools. Yet, applicable insight can be difficult due to biases and anomalies in data. An often overlooked phenomenon is mix effects , in which subgroups of data exhibit patterns opposite to the data as a whole. This phenomenon is widespread and often leads inexperienced analysts to draw incorrect statistical conclusions. In this paper we present Wiggum, an interactive visual analysis system for uncovering both mix effects and special cases known as Simpson’s paradox . A Python-based web implementation of Wiggum lets users interactively analyze multidimensional data sets to reveal various forms of mix effects. Through use cases, we describe how Wiggum supports the examination of mix effects in three real data sets and demonstrate how a combination of visualization techniques—heatmaps, trend plots, small multiples, coordinated multiple views, and dynamic queries for multi-attribute drill-down—are effective for analyzing mix effects. We conducted a user study to evaluate Wiggum, focusing on users’ ability to comprehend the statistical concepts, identify the corresponding visual patterns, and perform common analysis tasks correctly and efficiently. We discuss usability issues, utility limitations, and outline future directions to improve Wiggum.