Visual Elements and Cognitive Biases Influence Interpretations of Trends in Scatter Plots

TL;DR

This study investigates how visual elements like outliers and trend lines, along with cognitive biases, affect the interpretation of scatter plots, providing guidelines to improve visualization accuracy and reduce misinterpretation.

Contribution

It identifies key visual and cognitive factors influencing scatter plot interpretation and offers practical guidelines to mitigate their distortive effects.

Findings

Outliers skew trend perception but are less influential than other points.

Trend lines enhance perceived trend strength and reduce outlier influence.

Beliefs slightly affect perception of weak correlations, not strong ones.

Abstract

Visualizations are common methods to convey information but also increasingly used to spread misinformation. It is therefore important to understand the factors people use to interpret visualizations. In this paper, we focus on factors that influence interpretations of scatter plots, investigating the extent to which common visual aspects of scatter plots (outliers and trend lines) and cognitive biases (people's beliefs) influence perception of correlation trends. We highlight three main findings: outliers skew trend perception but exert less influence than other points; trend lines make trends seem stronger but also mitigate the influence of some outliers; and people's beliefs have a small influence on perceptions of weak, but not strong correlations. From these results we derive guidelines for adjusting visual elements to mitigate the influence of factors that distort interpretations of scatter plots. We explore how these guidelines may generalize to other visualization types and make recommendations for future studies.