Naranjo-Zolotov, Mijail JuanovichGraff, Simen Markveien2023-02-092023-02-092023-01-24http://hdl.handle.net/10362/148906Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementRecent times have seen a huge increase in data driven decision making. With the emergence of Big Data and Business Intelligence, user reports contain more data than ever before creating challenges in visualizing uncertainty in data. We present a comparison study where the traditional uncertainty visualizations error bars and violin plots compare to Hypothetical Outcome Plots. Hypothetical Outcome Plots is a visualization technique drawing animated samples from a distribution, visualizing uncertainty throughout the distribution of samples. Using neuroscience practices, we have conducted an eye-tracking experiment tracing the comparison between Hypothetical Outcome Plots and traditional uncertainty visualizations. We test Hypothetical Outcome Plots in different transitions across multiple visualization designs. The results show that static visualizations were easier for the participants to use as decision aid. While HOP had a lower total score, HOP worked better when visualized in a bar chart state and with bigger transitions between each draw. Moreover, eye-tracking metrics exhibit the difference in difficulty between the visualizations, indicating that participants familiarity with the visualization highly affected their ability to make the best decision.engData VisualizationUncertaintyHypothetical Outcome PlotsNeuroscienceEye trackingSDG 4 - Quality educationSDG 9 - Industry, innovation and infrastructuremaster thesis203219074