Imagining Replications: Graphical Prediction Discrete Visualizations Improve Recall Estimation of Effect Uncertainty

Jessica Hullman, Matthew Kay, Yea Seul Kim, Samana Shrestha

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

People often have erroneous intuitions about the results of uncertain processes, such as scientific experiments. Many uncertainty visualizations assume considerable statistical knowledge, but have been shown to prompt erroneous conclusions even when users possess this knowledge. Active learning approaches been shown to improve statistical reasoning, but are rarely applied in visualizing uncertainty in scientific reports. We present a controlled study to evaluate the impact of an interactive, graphical uncertainty prediction technique for communicating uncertainty in experiment results. Using our technique, users sketch their prediction of the uncertainty in experimental effects prior to viewing the true sampling distribution from an experiment. We find that having a user graphically predict the possible effects from experiment replications is an effective way to improve one's ability to make predictions about replications of new experiments. Additionally, visualizing uncertainty as a set of discrete outcomes, as opposed to a continuous probability distribution, can improve recall of a sampling distribution from a single experiment. Our work has implications for various applications where it is important to elicit peoples' estimates of probability distributions and to communicate uncertainty effectively.

Original languageEnglish (US)
Article number8017624
Pages (from-to)446-456
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume24
Issue number1
DOIs
StatePublished - Jan 2018

Keywords

  • Graphical prediction
  • interactive uncertainty visualization
  • probability distribution
  • replication crisis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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