Data (Mis)representation and COVID-19: Leveraging Misleading Data Visualizations For Developing Statistical Literacy Across Grades 6–16. Issue 2 (6th August 2021)

• Record Type: Journal Article
• Authors: Engledowl, Christopher; Weiland, Travis
• Is Part Of: Journal of Statistics and Data Science Education. Volume 29:Issue 2(2021)
• Page Start: 160
• Page End: 164
• Publication Date: 2021-08-06
• Subjects: Data science -- Data visualization -- Education -- Statistical literacy
• DOI: Https://www.tandfonline.com/doi/10.1080/26939169.2021.1915215
• Languages: English
• ISSNs: 2693-9169

Abstract

Abstract: The novel coronavirus has forced the world to interact with data visualizations in order to make decisions at the individual level that have, sometimes, grave consequences. As a result, the lack of statistical literacy among the general public, as well as organizations that have a responsibility to share accurate, clear, and timely information with the general public, has resulted in widespread (mis)representations and (mis)interpretations. In this article, we showcase examples of how data related to the COVID-19 pandemic has been (mis)represented in the media and by governmental agencies and discuss plausible reasons why it has been (mis)represented. We then build on these examples to draw connections to how they could be used to enhance statistics teaching and learning, especially as it relates to secondary and introductory tertiary statistics and quantitative reasoning coursework.