Evaluating Affective Objectives: Statistical Numbing in Data Visualization

2026-07-03

AI Summary

Standard data visualizations can induce “statistical numbing,” diminishing viewers’ empathy and prosocial responses to humanitarian crises. This study addresses this issue by conducting a data-video experiment that empirically tests the phenomenon within a visualization context and systematically compares the effects of three narrative strategies—data-driven, person-driven, and hybrid—on emotional responses and actual donation behavior. The findings reveal that person-driven narratives significantly enhance both empathy and donation amounts, whereas hybrid narratives yield the weakest effects, challenging the common assumption that combining data with personal stories is inherently superior. These results underscore the unique efficacy of individual narratives in motivating humanitarian action.

Abstract

Visualizations can help audiences understand the scale of tragedies, such as the consequences of natural disasters, war, genocide, and pandemics. In these cases, a visualization designer's default behavior may be to focus on communicating quantitative information: numbers, statistics, and trends. However, this may not reflect higher-level affective objectives to inspire their audience to care about an issue, empathize with others, or take action to help those in need. Worse, standard visualizations may conflict with these goals, as statistics can numb emotions and reduce prosocial feelings toward people in need. Designers have developed strategies to increase affective responses through data visualizations, such as blending data narratives and personal narratives about individuals. In this paper, we explore three design strategies for communicating a humanitarian crisis: data-driven, human-driven, or mixed narratives. We conducted an empirical study to explore the effect of statistical numbing in the context of these types of narratives in the format of data videos. In particular, we measure prosocial feelings and behaviors by giving participants the option of donating money as part of the study. We find that human-driven narratives (photographs and stories of individuals) elicited the highest donations and that the mixed narrative combination led to the lowest donations. We discuss the limitations of this study and the implications of pursuing affective objectives and the numbing of empathy in data visualization design.

Problem

• statistical numbing
• data visualization
• affective objectives
• empathy
• prosocial behavior

Innovation

• statistical numbing
• affective visualization
• data storytelling
• prosocial behavior
• humanitarian data