Explore how we create, interact with, and are influenced by data in all aspects of our lives — and learn how to evaluate the accuracy of data-based claims and visualizations.

News literacy topics

Algorithms and personalization; Evaluating evidence; Science and data reporting

Learning objectives

• I can explain why data isn’t objective or infallible.
• I can differentiate between credible and questionable sources of data.
• I can explain some of the primary ways data can be distorted in misleading ways.
• I can interpret different ways data is presented.
• I can identify charts and other data visualizations that are misleading and explain why.

Essential questions

• What is data?
• When can you trust data?
• Can data ever be objective?
• Who collects data and for what purposes?
• How can people use and misuse data?
• How can we tell when data is reliable?
• What are some common problems in the ways people represent and communicate data?

Excerpt

“Data about the real world is generated and collected in the real world. And when real-world things are measured and calculated by human beings, that data is subject to human imperfections – including flaws in the systems and structures that people use to generate and collect those numbers. Even data generated by machines is fallible. For example, your phone probably calculates your average screen time by adding up all the time it thinks you’ve spent looking at your phone. Most phones do this to help you better understand your phone habits. But a human had to decide what counts as “screen time” and how to calculate it and your phone might undercount or overcount your screen time based on those definitions. These kinds of machine-generated measurements might be useful, and they might even be mostly accurate but they’re not “objective” or unquestionable. So while I can use my own screen time reports to see that I’m probably spending waaay too many minutes on Instagram, the exact number of minutes isn’t something I take too seriously.”