Same Data, Diverging Perspectives: The Power of Visualizations to Elicit Competing Interpretations

TL;DR

This study demonstrates that visual representations of data significantly influence viewers' interpretations and decisions, leading to diverging conclusions based on what patterns are salient or emphasized.

Contribution

It reveals how visualization design and salience affect data interpretation, highlighting the subjective nature of understanding data.

Findings

Visualizations can bias decision-making based on pattern salience.

Different viewers interpret the same data differently due to visual emphasis.

Presentation style influences which data aspects are perceived as salient.

Abstract

People routinely rely on data to make decisions, but the process can be riddled with biases. We show that patterns in data might be noticed first or more strongly, depending on how the data is visually represented or what the viewer finds salient. We also demonstrate that viewer interpretation of data is similar to that of 'ambiguous figures' such that two people looking at the same data can come to different decisions. In our studies, participants read visualizations depicting competitions between two entities, where one has a historical lead (A) but the other has been gaining momentum (B) and predicted a winner, across two chart types and three annotation approaches. They either saw the historical lead as salient and predicted that A would win, or saw the increasing momentum as salient and predicted B to win. These results suggest that decisions can be influenced by both how data are presented and what patterns people find visually salient.