When evaluating data visualizations, what should you consider about data scope?

• A. Am I limiting the scope of the data?
• B. Is the data overrepresented?
• C. Should I ignore data sources?
• D. Should I add unrelated data?

Answer: (B)

When evaluating data visualizations, think about how the data scope is represented and whether some parts are given too much weight. The key issue is overrepresentation: if a subset dominates the visualization, it can distort the message and make you believe patterns apply to the whole population when they don’t. For example, a chart that shows most of its data from a single region or a narrow time period may mislead you into thinking that trend holds broadly, even though other regions or times differ.

Limiting the scope is a design choice, but the main concern with scope bias is overrepresentation, which creates misleading impressions about the broader data. Ignoring data sources and adding unrelated data are poor practices that undermine credibility rather than address how the scope is represented.