Correlation vs. Causation in Observational Data

Steps:

• Examine the table: X (antibiotic use per 1000 people/day) values are Austria 13, Iceland 17, USA 24, Spain 30; Y (penicillin-resistant pneumonia %) values are missing.
• Without Y data, trends between X and Y cannot be assessed.
• Choices A, B, D require observable patterns in Y relative to X, which are absent.
• Choice C might apply by default, but data incompleteness prevents confirmation.

Why B is correct:

• Not enough information to support causation; observational data shows association at best, not cause (per scientific method distinguishing correlation from causation).

Why the others are wrong:

• A: No Y values to verify increase in Y with X.
• C: Incompleteness hides potential relationships.
• D: No Y data to check for decrease.

Final answer: Not enough information.