Between 1980 and 2010 , the number of adults in the United...

Between 1980 and 2010 , the number of adults in the United States who were overweight more than doubled from 15\% to 37\%. Source: Centers for Disease Control and Prevention (a) Construct a graphic that is not misleading to depict this situation. (b) Construct a misleading graphic that makes it appear that the percent of overweight adults has more than quadrupled between 1980 and 2010 .

Between 1980 and 2010 , the number of adults in the United States who were overweight more than doubled from
15\% to 37\%. Source: Centers for Disease Control and Prevention
(a) Construct a graphic that is not misleading to depict this situation.
(b) Construct a misleading graphic that makes it appear that the percent of overweight adults has more than quadrupled between 1980 and 2010 .

Key Concepts

Misleading Graphics

Misleading graphics are intentional or unintentional visual representations that distort the perception of data. This concept highlights the unethical or flawed practices in chart construction, where techniques like selective scaling, missing data points, or disproportionate design elements are used to exaggerate or minimize differences, skewing the true message of the data.

Axis Scaling and Manipulation

Axis scaling and manipulation is a key concept in data visualization that involves setting the range and increments of chart axes appropriately. Correct scaling allows the audience to accurately interpret differences and trends in data, while inappropriate scaling or manipulation (such as truncation or disproportionate illustrations) can lead to misunderstandings or deliberate misrepresentation of the data.

Data Visualization

Data visualization involves the graphical representation of data to communicate information clearly and efficiently. It is a fundamental aspect of statistical communication, where proper visual representation can help reveal important trends and patterns while ensuring that the audience interprets the data accurately.

Graphical Integrity

Graphical integrity refers to the principle that visual representations of data should accurately reflect the underlying data without distortion. This means using appropriate scales, labels, and design choices to ensure that the visual information is not misleading or exaggerative, thereby preserving the truthfulness of the data.

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