Correlation

# Correlation

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## Correlation

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##### Presentation Transcript

1. Correlation Causation, Coincidence And Common Cause

2. Correlation When two sets of data are seemingly linked together we say they have a Correlation. In a positive correlation the data is directly related Height and Weight have a Positive Correlation

3. Negative Correlation In a negative correlation the data is inversely related Drinking Alcohol & Dexterity have a negative correlation

4. POST HOC ERGO PROPTER HOC“After this therefore because of this” A correlation is a good indicator that there may be a causal relationship, however…. • Correlation does not imply causation It could be a coincidental correlation 2. Even if there is a causal relationship a correlation tells us nothing about which causes which! It is natural to assume causation from correlation, but this is the logical fallacy of “POST HOC ERGO PROPTER HOC”

5. Is it really Pirates!? Or is this a case of…“POST HOC ERGO PROPTER HOC” Forget carbon emissions… Global Warming is caused by … An decreasing number of … Pirates!

6. And eat more Mozzarella! The data shows that to increase the number of Stem graduates we must: Store more uranium at nuclear power plants

7. Common Cause factor Consider the following correlation: High school students who had high grades also had high scores on the SATs. Is this a Cause-Effect relationship? Probably not. A more likely link is there is a common cause Intelligent students are like to get both higher grades and SAT scores Intelligence is therefore is a common cause factor for good grades and high SAT scores

8. Common cause examples  Eyes Watering? Sneezing? Is one causing the other or are you standing in a hay field?  Revenue from parking fees at the public beach each summer correlates with the sales at local restaurants. Can you think of a possible common cause factor? Good Weather?

9. Refuting Correlation = Causation Show that the correlation is coincidental by showing that: • The effect would have occurred even if the “cause” did not occur (The “cause” was not necessary) and The effect does not always occur when the “cause” does. (The “cause” is not sufficient) • That the effect was caused by something other than the suggested cause.

10. Refuting causation with correlation A lack of a correlation is an indicator that a causal relationship does not exist However it only proves that the “cause” is not sufficient for the effect