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Practice Problems

Here we discuss correlation, causality, spurious correlations, and misrepresentations

Here we discuss correlation, causality, spurious correlations, and misrepresentations of correlation in the news.


Please use the following Instructions to write your reply.

Part 1 - Explaining Correlation:  

Please write out how you would explain correlation to an interested friend.  What information do we need to tell people if they are going to understand what correlation is, what it can tell us, how it achieves this and what we need to watch out for?  

Part 2 - Nonsense Correlations and Misuses of Correlation:

1. Please look at a few examples of spurious correlations on this website Spurious Correlations.  Explain what this tells you about correlation that you didn't already know.   

2. Next, please explain a time in your life where you mistakenly attributed cause and later found out that something else was the true cause of that event.  What led you to mistakenly attribute cause?

3. What is an example of a correlation that you KNOW is just a correlation and not a cause, but in spite of that, you always worry that there is causality there?

Part 3 - Misrepresentation of correlation in the news or other media:

1. Find a news story which falsely claims or implies causation based only on correlational evidence, or find a journal article that studies the misrepresentation of correlation in the media.  List the reference, and explain what is misrepresented, what agenda is being supported, and explain if you think the misrepresentation is accidental or deliberate.

2. Even if you know that correlation is not a guarantee of causation and it does not necessarily imply a causation, on a scale of 0-10 where 0 is no persuasive effect at all and 10 is maximal persuasive power, when you hear that two things are correlated, how strongly do you suspect that there IS causation?  On that same scale, how strongly do you think OTHER people suspect that there IS causation?

Hint
StatisticsA spurious correlation is a connection between two variables that look casual but are not. It will originally appear to show that one variable affects the other variable but that is not the case, correlation is mostly caused by a third factor that was not originally there....

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