Effects of outlier and familiar context in trend-line estimates in scatterplots.

Abstract

Lately, there has been a growing fascination with blending research on visualizing data and understanding how our basic visual perception works. Taking this path, this research delved into the connection between ensemble perception, which involves quickly and accurately grasping essential information from sets of visually similar objects, and how we process scatterplots. Across two experiments, we aimed to answer a couple of connected questions. First, we investigated whether having an outlier in a scatterplot affects how people draw trend-line estimates. Second, we explored whether what we are familiar with and the presence of outliers that match the trend affect how we draw trend-line estimates in scatterplots. In both experiments, we showed participants scatterplots for a short time, manipulating whether there were outliers or not. Then, using a computer mouse, participants drew their trend-line estimates. By comparing what they drew with possible trend-line solutions, we discovered that when there is no context, the outlier and the other points in a scatterplot are seen as equally important in drawing the trend-line estimate. But when the scatterplot depicted a familiar context and the outlier fitted the trend, people tended to give more weight to those outlier points in their drawings. This suggested that what we already believe can sway how we draw trend-line estimates even from quickly shown scatterplots.