# Common Logic Pitfalls in Economics

Lesson Transcript
Instructor: Kevin Newton

Kevin has edited encyclopedias, taught history, and has an MA in Islamic law/finance. He has since founded his own financial advice firm, Newton Analytical.

In economics, pitfalls can lead to incorrect conclusions, which may be used to make poor economic decisions and policies. Learn about common logic pitfalls in economics, including causation vs. correlation, bias, loaded terminology, and other fallacies. Updated: 10/09/2021

## Causation vs. Correlation

What would you say if I told you that people buying more ice cream causes people to wear swimsuits more often? You'd probably give me a funny look. However, if you look at the numbers, it seems to make sense. When ice cream sales go up, people tend to spend more time in swimsuits. But does that mean that the increased consumption of ice cream causes people to wear swimsuits more often?

In a word, no. What you've just seen here is an example of a logical fallacy that economists often encounter. Again, when you look at the numbers, it makes sense. However, the reason that you see more people in swimsuits during times of increased ice cream sales has nothing to do with the ice cream. In fact, it has everything to do with the time of year people buy ice cream. In the summer, people spend time in swimsuits and people eat ice cream. They do both because the weather is hot. The hot weather causes increased ice cream consumption as well as more people to wear swimsuits.

As a result, we call this sort of relationship causation, because one action clearly causes the other. The relationship between ice cream sales and swimsuit sightings is, instead, a correlation, which means the compared items mirror each other but may not have anything to do with actual causality.

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• 0:01 Causation vs. Correlation
• 1:29 Bias and Loaded Terminology
• 3:09 Other Fallacies
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Okay, that was an easy one. However, economists still have to contend with other logical issues. Much of an economist's work depends on the accurate collection and portrayal of data. That said, it is not always that easy. People sometimes introduce bias, whether knowingly or unknowingly, that can change the data set. A bias occurs when the model in question is somehow shaped by what the economist wants to find.

For example, if I'm trying to model at what temperature water freezes and want to prove that water can't freeze, bias would be blatant if I kept the water boiling and just assumed that cold water doesn't freeze. It's a bit more subtle if I put it in the refrigerator and just assume that if water freezes, then it must freeze in the fridge.

But it's not always through overt design that biases are introduced. One of the biggest fallacies faced by economists is the use of loaded terminology, or words that carry a meaning beyond their mere definition. This is most often used by politicians to get an emotional response, especially when talking about things like 'our jobs,' 'our money,' or 'our healthcare,' but it can be abused by the innocent as well.

For example, if I asked if you had a comfortable lifestyle, you may consider that as shelter, a warm bed, and enough money after all your bills are paid to get pizza and beer on Friday night. However, what if your idea of comfortable involved a large mansion, a private pool, and a butler? Both use the same word but have a world of difference in meaning.

## Other Fallacies

Other stumbling blocks exist as well. Closely related to the difference between causation and correlation is the idea of post hoc ergo propter hoc, a Latin phrase that roughly means since something came first, it caused something else afterwards.

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