Watson-Glaser Critical Thinking Appraisal Interpreting Evidence and Data 2 — Questions and Answers
Question 1: A study reports that cities with more coffee shops per capita have higher average incomes. Which conclusion is best supported by this data?
- Coffee shops cause income to rise
- Higher income and coffee shop density are correlated (Correct answer)
- Wealthy cities attract coffee shop chains, proving causation
- Income determines how many coffee shops a city needs
Correct answer: Higher income and coffee shop density are correlated
The data shows a correlation between two variables but does not establish which causes which.
Question 2: A bar chart shows that Product A outsold Product B in Q1 and Q2 but the chart's y-axis starts at 50,000 units rather than zero. What is the most important interpretive caution?
- The products are incomparable
- The visual gap exaggerates the actual difference in sales (Correct answer)
- Product B is about to overtake Product A
- Starting axes above zero is always fraudulent
Correct answer: The visual gap exaggerates the actual difference in sales
A truncated y-axis makes small absolute differences appear much larger than they are.
Question 3: Researchers find that 80% of people who ate the cafeteria salad got sick, but 60% of those who did not eat it also got sick. What should you conclude?
- The salad is clearly the cause of illness
- The salad is not the sole or primary cause of illness (Correct answer)
- The 60% base rate proves the salad is harmless
- No conclusion can ever be drawn from such data
Correct answer: The salad is not the sole or primary cause of illness
Because many non-salad eaters also got sick, the salad alone cannot explain the outbreak.
Question 4: A line graph shows employee satisfaction rising from January to June, then the data line ends. A manager claims satisfaction will continue rising through December. This claim is best described as:
- A valid deduction from the graph
- An extrapolation beyond the available evidence (Correct answer)
- A contradiction of the data shown
- A restatement of what the graph proves
Correct answer: An extrapolation beyond the available evidence
Extending a trend beyond the observed period is extrapolation, which is not supported by the existing data alone.
Question 5: A dataset shows that students who sleep more than 8 hours score higher on exams. A classmate says, 'So sleeping more makes you smarter.' What flaw exists in this reasoning?
- The sample size is too small to mention
- It confuses correlation with causation (Correct answer)
- The exam scores are irrelevant
- More sleep always lowers scores
Correct answer: It confuses correlation with causation
The data shows an association; it does not prove that additional sleep directly causes higher intelligence or scores.
Question 6: A report states that average household income in a neighborhood rose 15% last year. Which additional piece of information would most change the interpretation of this finding?
- The name of the neighborhood
- Whether the gain was driven by a few very high earners or spread broadly (Correct answer)
- The number of parks in the area
- Whether the report was published in spring or fall
Correct answer: Whether the gain was driven by a few very high earners or spread broadly
Averages can be skewed by outliers, so knowing the distribution reveals whether the gain was widespread or concentrated.
Question 7: Two surveys ask about support for a new transit policy. Survey A finds 72% in favor; Survey B finds 41% in favor. The most likely explanation for this discrepancy is:
- One survey must have been faked
- Differences in question wording, sampling, or timing (Correct answer)
- Transit policy support cannot be measured
- The true support is exactly 56.5%
Correct answer: Differences in question wording, sampling, or timing
Survey results often diverge because of methodological differences such as question framing, sample demographics, or when the survey was conducted.
Watson-Glaser Critical Thinking Appraisal Interpreting Evidence and Data 2
A study reports that cities with more coffee shops per capita have higher average incomes.
Which conclusion is best supported by this data?