True or false: If the correlation between two variables is strong, then changes in one variable cause changes in the other (i.e., correlation implies causation).

• A. False, unless the sample size is large
• B. True
• C. False
• D. True, but only when the correlation is negative

Answer: (C)

Verified step by step guidance

1. Understand the meaning of correlation: Correlation measures the strength and direction of a linear relationship between two variables, but it does not imply that one variable causes the other to change.
2. Recall the phrase 'correlation does not imply causation,' which means that even if two variables move together strongly, it does not mean that one causes the other; there could be other factors or it could be coincidental.
3. Consider that causation requires additional evidence beyond correlation, such as controlled experiments or establishing a logical mechanism linking the variables.
4. Evaluate the given options: The statement 'False' is correct because a strong correlation alone is not sufficient to conclude causation, regardless of sample size or correlation sign.
5. Conclude that the correct answer is 'False' because correlation indicates association, not causation.