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Evaluate misleading data displays and statistical claims

The learner evaluates whether a data display represents quantities faithfully by examining axis scales, intervals, truncated axes, unequal categories, pictographs, and visual features such as area or three-dimensional effects that can exaggerate differences. They assess statistical claims in light of sample size, sampling method, omitted context, absolute versus relative change, and the difference between association and causation, recognizing that a graph or summary can be numerically accurate yet misleading. Formal statistical inference, confidence intervals, and advanced causal analysis are outside this scope.

Detailed Explanation: Evaluate misleading data displays and statistical claims

A data display can use correct numbers but still create a misleading impression. To evaluate it, check:

  1. The axis: Does it start at 00? Are the intervals equal and clearly labeled?
  2. The visual size: Do bars, pictures, or 3-D effects make a difference look larger than it really is?
  3. The actual numbers: Find both the absolute change and the relative change.
  4. The claim: Consider the sample size, how people were chosen, and whether the data show causation or only an association.

Worked example

A school surveys 4040 students about how often they recycle.

  • Before a recycling campaign: 1212 students recycled.
  • After the campaign: 1818 students recycled.

A bar graph shows the two numbers, but its vertical axis starts at 1010 instead of 00.

Step 1: Check the numbers

The number of students increased by

1812=6.18-12=6.

So, 66 more students recycled.

The relative increase is

612=0.5=50%.\frac{6}{12}=0.5=50\%.

The percentage increase is 50%50\%, but the number increased from 1212 to only 1818 students.

Step 2: Check the axis

Because the graph starts at 1010, the first bar rises only from 1010 to 1212, while the second rises from 1010 to 1818. The second bar may look about four times as tall as the first:

18101210=82=4.\frac{18-10}{12-10}=\frac{8}{2}=4.

However, the actual number of recyclers is only

1812=1.5\frac{18}{12}=1.5

times as large. The truncated axis exaggerates the visual difference.

Step 3: Evaluate the claim

The school says, “The campaign caused recycling to increase dramatically for all students.”

A better evaluation is:

  • The data show that recycling increased by 66 students, or 50%50\%.
  • The graph is misleading because its axis does not start at 00.
  • Only 4040 students were surveyed, so the results may not represent the entire school.
  • The data show an increase after the campaign, but they do not prove that the campaign caused it. Other changes could have happened at the same time.

A fair conclusion would be: “Among the 4040 surveyed students, the number who recycled increased from 1212 to 18.Thegraphexaggeratesthedifferencebecauseitsaxisstartsat18. The graph exaggerates the difference because its axis starts at 10.”

Learn by doing: Evaluate misleading data displays and statistical claims

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Graphing - Scatter Plot (Linear) - Graph to Prediction From X True/False


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