Module 2: How to Critically Analyze and Interpret Data Visualizations
Data Visualizations and Misinformation
We’ve all probably encountered a data visualization that was confusing and difficult to understand. It probably wasn’t intentional, but, rather, the consequence of poor design choices, not fully understanding the dataset, using the wrong dataset for their purposes, or just rushing and making sloppy errors.
Usually misleading data visualizations fall into the realm of rather than , but both can cause harm.
Because data visualizations can be very persuasive and invoke strong emotional reactions, it is important to go through the steps outlined earlier in the module to make sure you fully understand what it is saying before you decide to share it with someone else.
Exercises
Take a moment to test your ability to interpret a data visualization. In the activity below, you’ll see an example of a data visualization. Following the steps outlined earlier in the module, analyze this visualization and answer the questions that follow.
Reflection
After analyzing this data visualization, would you feel confident sharing it with your friends and colleagues?
False information that is spread, regardless of intent to mislead.
Intentionally biased or misleading information.
