  Understanding and evaluating uncertainty play a key role in decision-making.
When a viewer studies a visualization that demands inference, it is necessary
that uncertainty is portrayed in it. This paper showcases the importance of
representing uncertainty in visualizations. It provides an overview of
uncertainty visualization and the challenges authors and viewers face when
working with such charts. I divide the visualization pipeline into four parts,
namely data collection, preprocessing, visualization, and inference, to
evaluate how uncertainty impacts them. Next, I investigate the authors'
methodologies to process and design uncertainty. Finally, I contribute by
exploring future paths for uncertainty visualization.
