In ethical data visualization, which practice best avoids misleading readers?

• A. Use accurate scales
• B. Annotate uncertainty
• C. Show data provenance
• D. All of the above

Answer: (D)

In ethical data visualization, avoiding misinterpretation comes from transparency in how the data is presented. Using accurate scales prevents distortions that can make differences seem bigger or smaller than they truly are. Annotating uncertainty communicates the limits and variability in the data, so readers don’t overstate what the numbers can support. Showing data provenance reveals where the data came from and how it was collected and processed, helping readers assess credibility and potential biases. Because each of these practices tackles a different potential source of misleading impressions, the strongest approach is to apply all of them together.