AP CSP Day 42: Data Visualization & Limitations | Cycle 2
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A truncated y-axis on a bar chart starts the axis above zero, making small differences appear much larger than they are. A misleading scatter plot may show a strong visual correlation that disappears when an outlier is removed or when the scale is adjusted. AP CSP Cycle 2 data visualization questions present a graph with a subtle design choice that distorts interpretation and ask students to identify what false conclusion a casual reader might draw. Evaluating whether a visualization accurately represents the underlying data is a critical data literacy skill tested on the AP exam.
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Misleading Visualizations: Spotting the Trick
Truncated Axis
A bar chart with a y-axis starting at 950 instead of 0 makes a difference of 10 units look like a massive gap. The visual impression is entirely determined by the axis range, not the actual data difference.
Cherry-Picked Time Range
A line chart showing only the best 3-month period of a 5-year trend can make a declining metric look like it is rising. The choice of time range shown is a powerful way to distort interpretation.
Practice Question
A scatter plot shows the relationship between hours studied and exam scores for 200 students. The data shows a general upward trend. Which of the following conclusions is supported by this visualization?
I. There is a positive correlation between hours studied and exam scores.
II. Studying more hours directly causes higher exam scores.
III. Every student who studied more than 10 hours scored above 85.
Statement I is supported: an upward trend in a scatter plot indicates positive correlation. Statement II is not supported: correlation does not prove causation (other factors like prior knowledge could contribute). Statement III is not supported: a general trend does not mean every individual point follows the pattern; scatter plots typically have variation.
B) Statement II attributes causation to a correlation. C) Statement III makes an absolute claim about every data point, which is not supported by a general trend. D) Only statement I is directly supported.
Students leap from observing a trend (correlation) to concluding a cause-and-effect relationship. They also assume trends apply uniformly to every data point, ignoring natural variation in scatter plot data.
Scatter plots show correlation (or lack thereof) but never prove causation. Also, a trend describes the overall pattern — individual data points may deviate from it.
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