Distinguish correlation from causation.

Distinguish correlation from causation.

• A. Correlation indicates association; causation implies one variable causes changes in another.
• B. Correlation implies causation.
• C. Causation can be inferred from correlation with a large p-value.
• D. They are the same concept.

Answer: (A)

Correlation measures how two variables move together, an association, and it can be positive or negative and vary in strength. But this kind of relationship does not prove that one variable causes changes in the other. There can be a lurking variable that affects both, the relationship could be coincidental, or the direction of causality could be reversed. To claim causation, you need evidence that manipulating one variable leads to a change in the other, which typically comes from well-designed experiments with randomization, proper timing (the cause comes before the effect), and control of confounding factors. So, while two things may move together, that alone does not establish that changing one causes the other. For example, ice cream sales and drowning incidents rise together in the summer, but that doesn’t mean ice cream causes drowning—it’s driven by a third factor: hot weather. Conversely, smoking has a well-supported causal link to lung cancer based on consistent evidence and biological mechanisms. Statements that correlation implies causation, that a large p-value proves causation, or that both terms are the same, aren’t correct.

Correlation measures how two variables move together, an association, and it can be positive or negative and vary in strength. But this kind of relationship does not prove that one variable causes changes in the other. There can be a lurking variable that affects both, the relationship could be coincidental, or the direction of causality could be reversed. To claim causation, you need evidence that manipulating one variable leads to a change in the other, which typically comes from well-designed experiments with randomization, proper timing (the cause comes before the effect), and control of confounding factors.

So, while two things may move together, that alone does not establish that changing one causes the other. For example, ice cream sales and drowning incidents rise together in the summer, but that doesn’t mean ice cream causes drowning—it’s driven by a third factor: hot weather. Conversely, smoking has a well-supported causal link to lung cancer based on consistent evidence and biological mechanisms. Statements that correlation implies causation, that a large p-value proves causation, or that both terms are the same, aren’t correct.