Interpreting Evidence and Data Questions and Answers Flashcards
7 cards from real Watson-Glaser Critical Thinking Appraisal practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Interpreting Evidence and Data Questions and Answers flashcards as text
A study shows that cities with more libraries have lower crime rates. What is the most justified interpretation of this data?
Answer: There is an association between library density and crime rates, but causation is unproven
The data shows a correlation, but establishing causation requires ruling out confounding variables such as overall city wealth or education spending.
A dataset shows employee productivity rose 15% after a new policy was introduced. A colleague says the policy worked. What additional information is most critical before accepting this conclusion?
Answer: Whether productivity was already trending upward before the policy
Without knowing the pre-existing trend, it is impossible to attribute the productivity increase to the policy rather than a pre-existing trajectory.
A table shows that 80% of surveyed customers rated a product 'excellent.' The sample size was 10 people. What is the primary limitation of this evidence?
Answer: The sample is too small to reliably generalize to all customers
A sample of 10 is far too small to draw reliable conclusions about the broader customer population.
Data shows that runners who stretch before exercise have more injuries than those who do not. Which interpretation is best supported?
Answer: Runners with prior injuries may stretch more as a precaution, explaining the association
Reverse causation is plausible: injured or injury-prone runners may be more likely to stretch, creating a misleading association.
A graph shows ice cream sales and drowning incidents both peak in summer. What should you conclude?
Answer: Both variables share a common cause — summer heat and outdoor activity — rather than a direct link
Both variables are driven by a third factor (summer season), making this a classic example of a confounding variable, not causation.
A report states that 'most experts agree' that a new drug is effective, but does not cite specific studies. How should this evidence be treated in Watson-Glaser reasoning?
Answer: Treat it as weak evidence until specific, verifiable data is provided
Appeal to unnamed authority without supporting data is considered weak evidence; verifiable studies are required for strong interpretation.
A pie chart shows that Department A uses 60% of a company's budget. A manager concludes Department A is wasteful. What is wrong with this interpretation?
Answer: Budget share alone does not indicate waste without knowing the department's size, output, or responsibilities
Budget proportion is meaningless without context — a larger or more critical department may legitimately require more resources.