Nikolaus Piccolotto, Daniel Pahr, Laura Lotteraner, Fatih Oztank, Markus Bögl · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.31050 · cited by 0 works (OpenAlex) · open-access PDF
An important task of uncertainty visualizations is to communicate how the number of available observations influences the precision of estimates, such as a Normal distribution's mean. In this paper, we propose an adaptive approach to visualizing univariate data that produces salient representations while highlighting the uncertainty introduced by the sample size. We introduce jk-plots, inspired by educational material of Bayesian statistics, that combine visualizations of plausible reference models (emulating popular uncertainty visualization idioms) and the observations. We describe a visualization pipeline to create jk-plots, which we implemented in an R package. We demonstrate jk-plots using synthetic datasets of varying size and discuss possible usage scenarios in domain expert interviews.
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Topics: Visualization, Computer science, Data mining, Univariate, Domain (mathematical analysis), Salient
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