Correlation Versus Causation
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Correlation does not imply causation
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In the world of data analysis, the phrase "correlation does not imply causation" is fairly ubiquitous as a disclaimer on the interpretation of a lot of evidence about how phenomena are related to one another. For example, when looking at a graph of SAT scores versus parental income, you would be very likely to see this "correlation does not imply causation" warning. so what are these concepts and why does this warning matter?
Correlation measures the degree to which two phenomena tend to happen together- for example, rain and carrying an umbrella are correlated. In fact, rain and carrying an umbrella are positively correlated because a higher likelihood of rain tends to be paired with a higher likelihood of carrying an umbrella, and vice versa. In contrast, snow and wearing flip flops are negatively correlated because a higher likelihood of snow tends to be paired with a lower likelihood of wearing flip flops, and vice versa.
Causation, on the other hand, indicates that one phenomenon actually causes the other phenomenon to happen. In the weather examples above, it seems at least intuitively plausible that rain would cause people to carry umbrellas and snow would cause people to not wear flip flops. So where's the problem? Let's examine.
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