🤖 AI Summary
This study addresses how to effectively visualize both quantifiable statistical uncertainty and expert-derived qualitative confidence in predictive contexts to support decision-making by non-expert users. Through three preregistered human-subject experiments (N=923), it systematically compares the impact of juxtaposing versus integrating qualitative confidence indicators (e.g., textual labels, icons) with statistical confidence intervals (encoded via color, transparency, or blurred outlines) in time-series line charts. This work presents the first systematic evaluation in time-series forecasting of multiple non-textual visual encodings for jointly representing dual forms of uncertainty. Findings demonstrate that designs employing color and blurred outlines successfully convey qualitative confidence and significantly alter users’ judgment patterns, offering empirical validation and actionable design guidelines for visualizing multidimensional uncertainty.
📝 Abstract
Forecasting involves multiple forms of uncertainty, including both uncertainties that can be quantified directly (quantitative uncertainty) and those that must be expressed through experts' subjective judgments about the forecast and its context (qualitative confidence). Past work has established that conveying both quantitative uncertainty and qualitative confidence in forecasts can alter readers' decision making, but little research investigates the impact of how these forms of uncertainty are presented. In this work, we present three preregistered human-subjects studies (total n = 923) on how different methods of visualizing qualitative uncertainty alongside line charts' confidence intervals affects non-experts' decision making. In particular, we investigate representing qualitative uncertainty separately via text and icons, and integrated into quantitative confidence intervals via color, transparency, and a blurred stroke design. In Experiment 1, we confirm that showing qualitative confidence alongside statistical variability can change patterns of decision making, replicating findings from previous work in the new context of time-series line charts. In Experiments 2 and 3, we find several non-textual encoding techniques that produce similar effects in participants' incorporation of qualitative confidence into their judgments. Our findings suggest actionable guidelines for visualization designers who seek to represent multiple forms of uncertainty for a single line chart forecast. A free copy of this paper and all supplemental materials are available at https://osf.io/7ya2c/overview.