Visualizing Data for Impact: Analyzing Misleading Visualizations

One of the challenges of data visualization is recognizing and avoiding misleading visuals. These and other common mistakes make data visualization less effective and can lead to incorrect conclusions. Through this course learn about misleading statistics and visual distortions. Examine some common data visualization mistakes including data overload interchanging charts and the use of color as well as how to recognize and correct them. Next explore examples of deceiving statistics visual distortions and graphs and how to avoid being misleading. Finally learn about omitting data improper extraction and correlating causation. After course completion youll be able to avoid mistakes when visualizing your data.