A city reports that neighborhoods with higher average household income tend to have higher average home prices (a positive association). The report used existing census and real-estate data; no neighborhoods were assigned an income level. Which statement is the most reasonable conclusion?
- The report proves that raising a neighborhood's income will raise home prices, since the association is positive.
- The report shows an association between income and home prices, but it does not justify a causal claim because the data are observational and may involve confounding variables. (correct answer)
- The report shows there is no relationship because home prices vary too much to be correlated with income.
- The report shows that higher home prices cause higher household income in a neighborhood.
Explanation: This question examines correlation versus causation in real-world observational data. Correlation means two variables are associated - they tend to vary together in a predictable way. Causation requires that changing one variable directly causes the other to change, which needs experimental control through random assignment. The city analyzed existing census and real-estate data without any experimental manipulation - neighborhoods weren't assigned income levels. While income and home prices show a positive association, we cannot conclude that raising income would cause higher home prices because confounding variables (like school quality, location, or neighborhood age) might influence both. The correct answer recognizes that observational data can show association but cannot justify causal claims due to potential confounders. A common misconception is thinking that obvious real-world relationships must be causal - even strong associations don't prove causation without experimental evidence! Always ask: "Were units randomly assigned to conditions?" Here, neighborhoods naturally vary in income.