Which of the following is a common pitfall in data visualization that can mislead readers?

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When reading a chart, a common way to mislead is cherry-picking data—showing only the pieces that support a desired takeaway while omitting information that might contradict it. This narrows the evidence, making trends or effects appear stronger, more consistent, or more certain than the full dataset actually supports. For example, displaying only the peak months of sales or a subset of experiments with positive results can give the impression that the pattern holds universally, even when it doesn’t. The risk is that readers form biased conclusions based on incomplete information. To avoid this, present the full range of data, be explicit about what is included or excluded, and consider showing uncertainty, variability, or negative results so readers can judge how robust the claim is. Practices like using consistent scales, providing clear labeling, and including context or uncertainty help readers understand the data accurately and reduce the chance of being misled.