Correlation for AP Statistics (page 2)

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By — McGraw-Hill Professional
Updated on Feb 5, 2011

Correlation and Causation

Two variables, x and y, may have a strong correlation, but you need to take care not to interpret that as causation. That is, just because two things seems to go together does not mean that one caused the other—some third variable may be influencing them both. Seeing a fire truck at almost every fire doesn't mean that fire trucks cause fires.

Example: Consider the following dataset that shows the increase in the number of Methodist ministers and the increase in the amount of imported Cuban rum from 1860 to 1940.


For these data, it turns out that r = .999986.

Is the increase in number of ministers responsible for the increase in imported rum? Some cynics might want to believe so, but the real reason is that the population was increasing from 1860 to 1940, so the area needed more ministers, and more people drank more rum.

In this example, there was a lurking variable, increasing population—one we didn't consider when we did the correlation—that caused both of these variables to change the way they did. We will look more at lurking variables in the next chapter, but in the meantime remember, always remember, that correlation is not causation.

Practice problems for these concepts can be found at:

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