Which practice helps ensure data visuals are not misleading?

• A. Using consistent axis scales and clearly showing data ranges
• B. Removing axis labels to save space
• C. Ignoring data variability
• D. Emphasizing only favorable data points

Answer: (A)

Using consistent axis scales and clearly showing the range of data is essential because it preserves the true size of differences and makes it possible to compare values accurately across visuals. When the same metric is displayed with different y-axis scales in different panels or charts, a small difference can look huge or a large difference can look tiny, which misleads the viewer about the real magnitude of change or disparity. Showing the full data range helps people see both the spread and the actual values, so interpretations reflect reality and not an optical illusion. In contrast, removing axis labels strips away the context needed to understand what the numbers mean, ignoring data variability hides important uncertainty about the data, and emphasizing only favorable points biases the message. Together, clear ranges and consistent scaling keep visuals honest and interpretable.