Fake News, Misinformation and Disinformation - Learning to Critically Evaluate Sources of Information: Critical Data Analysis Resources

EAVI is a non-profit organization based in Brussels. We work i to empower individuals to be active, engaged citizens in today’s increasingly challenging media environment.

Tracking retractions as a window into the scientific process. List of the top 10 most highly cited retracted papers.

This interactive resource and archive aims to help people make sense of data visualizations.

This is what happens when you let the data ramble on and on and on…

Presentation by Jessie Casella, VA Clinical Librarian for Northampton, Central Western Mass Medical Center, on the Edward Tufte One Day course on Presenting Data and Information workshop. Jessie will focus on Mr. Tufte’s evaluation of data visualizations, ethics of data visualization design, and the pitfalls of PowerPoint.

This article published in 2023 shows that, contrary to conventional wisdom, violations of visualization design guidelines are not the dominant way people mislead with charts. We present common reasoning errors and discuss how even faithfully plotted data visualizations can be used to support misinformation. (See section 4.2.1 Reasoning Errors specifically).

This study from 2023 found that participants spent significantly less time looking at both line graph axes for graphs with a rhetorically exaggerated title. Qualitative results contribute further patterns. (The Literature Review and Results Sections discuss specific graphical features and their associated misinterpretations).

This article from 2022 in the Journal of Science Communication discusses the intuitive reading of data, trends, predictions, consequences of violating typical graph design conventions, and investigates the effectiveness of four correction methods as debunking strategies to correct bar charts with manipulated vertical axes. (See "Misleading Graph Design" section).

This article published in 2021 describes how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions.

Qualitative responses illuminate people's perceptions of graph readability and what information they use to read different types of graphs. Recommendations are made to improve data visualization instruction, including critically examining software defaults and the ease with which people give agency over to software when preparing data visualizations (2020).

This study provided a short training session to participants which improved data interpretation quality for all participants. This suggests that the best way to counteract the decision-biasing effects of misleadingly designed graphs is to educate decision makers about the principles of proper graph design. However, such training does not totally eliminate the decision-biasing effects of misleadingly designed graphs (2008).