Examples of causation in the following topics:
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- In the scientific pursuit of quantitative prediction and explanation, two relationships between variables are often confused: correlation and causation.
- Causation refers to a relationship between two (or more) variables where one variable causes the other.
- It is at this point that a simple yet noteworthy phrase should be introduced: correlation is not causation.
- If you look back at the three criteria of causation above, you will notice that the relationship between ice cream consumption and crime meets only one of the three criteria (they change together).
- This diagram illustrates the difference between correlation and causation, as ice cream consumption is correlated with crime, but both are dependent on temperature.
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- But they must be cautious not to mistake correlation for causation.
- To better understand the difference between correlation and causation, consider this example.
- This relationship is a correlation and it does not necessarily imply causation.
- It is important to remember, however, that correlation does not imply causation; in other words, just because variables change at a proportional rate, it does not follow that one variable influences the other .
- This mock newscast gives three competing interpretations of the same survey findings and demonstrates the dangers of assuming that correlation implies causation.
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- Of course, this is not to imply pure causation, but rather tha individuals with similar voting preferences choose to live in the same area.
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- Further, quantitative sociologists typically believe in the possibility of scientifically demonstrating causation, and typically utilize analytic deduction (e.g., explore existing findings and deduce potential hypotheses that may be tested in new data).
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- Statistics demonstrating correlations are typically misinterpreted in public discussion as demonstrating causation, a fallacy known as the spurious relationship.