What are two common data visualization pitfalls to avoid?

• A. Using clear scales and simple charts.
• B. Using misleading scales or inappropriate chart types; clutter and overloading with too much data.
• C. Ensuring color contrast and readability.
• D. Avoiding legends entirely.

Answer: (B)

When you visualize data, a key risk is misrepresenting what the numbers show by how the visualization is constructed. Using misleading scales or an inappropriate chart type can distort comparisons or trends, making small differences seem large or hiding real patterns. For example, starting the axis at a non-zero value or using inconsistent intervals can exaggerate or downplay changes, while a chart type that doesn’t fit the data (like a pie chart for many categories or a chart with distorted proportions) can mislead about relationships. Clutter and data overload are another major pitfall because too many data points, series, colors, or decorative elements overwhelm the viewer and obscure meaningful patterns. To avoid these, keep scales clear and appropriate for the data, choose chart types that accurately convey the relationships, and maintain a clean, focused design with only essential information. The other options describe good readability practices (clear scales, color contrast, readability) or suggest avoiding elements that are actually needed for interpretation, so they aren’t pitfalls here.