# A General Approach to Visualizing Uncertainty in Statistical Graphics

arXiv: https://arxiv.org/abs/2508.00937 · HTML: https://mlsanity.com/paper/2508.00937/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 97 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [Surfacing Visualization Mirages](https://mlsanity.com/paper/2001.02316/) (2020)
- [Uncertainty-aware Unsupervised Multi-Object Tracking](https://mlsanity.com/paper/2307.15409/) (2023)
- [UncTrack: Reliable Visual Object Tracking with Uncertainty-Aware Prototype Memory Network](https://mlsanity.com/paper/2503.12888/) (2025)
- [MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation](https://mlsanity.com/paper/2111.14646/) (2021)
- [Uncertainty-Oriented Ensemble Data Visualization and Exploration using Variable Spatial Spreading](https://mlsanity.com/paper/2011.01497/) (2020)
- [Why Authors Don't Visualize Uncertainty](https://mlsanity.com/paper/1908.01697/) (2019)
- [Glyph-Based Uncertainty Visualization and Analysis of Time-Varying Vector Fields](https://mlsanity.com/paper/2409.00042/) (2024)
- [Visual Model Validation via Inline Replication](https://mlsanity.com/paper/1605.08749/) (2016)
- [Visualizing Uncertainty in Sets](https://mlsanity.com/paper/2302.11575/) (2023)
- [Quantifying Uncertainty: All We Need is the Bootstrap?](https://mlsanity.com/paper/2403.20182/) (2024)
- [The Noisy Work of Uncertainty Visualisation Research: A Review](https://mlsanity.com/paper/2411.10482/) (2024)

## Abstract (arXiv)

We present a general approach to visualizing uncertainty in static 2-D statistical graphics. If we treat a visualization as a function of its underlying quantities, uncertainty in those quantities induces a distribution over images. We show how to aggregate these images into a single visualization that represents the uncertainty. The approach can be viewed as a generalization of sample-based approaches that use overlay. Notably, standard representations, such as confidence intervals and bands, emerge with their usual coverage guarantees without being explicitly quantified or visualized. As a proof of concept, we implement our approach in the IID setting using resampling, provided as an open-source Python library. Because the approach operates directly on images, the user needs only to supply the data and the code for visualizing the quantities of interest without uncertainty. Through several examples, we show how both familiar and novel forms of uncertainty visualization can be created. The implementation is not only a practical validation of the underlying theory but also an immediately usable tool that can complement existing uncertainty-visualization libraries.