Welcome back! You've learned to spot patterns in economic data. But here's a crucial question: when two variables move together, does one cause the other?
This relationship puzzle is at the heart of economic analysis. Let's explore correlation versus causation.
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Can you think of two things that often move together in economics?
Correlation simply means two variables tend to move in the same direction. When ice cream sales rise, drowning incidents also rise. They're correlated.
But correlation doesn't mean one causes the other. Both actually relate to a third factor: hot weather.
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What third factor might link coffee sales and umbrella sales?
Positive correlation means variables move together - as one increases, the other increases too. Think education levels and income: generally, more education correlates with higher earnings.
Negative correlation means they move oppositely - unemployment and consumer spending typically show this pattern.
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Which type of correlation would you expect between gas prices and car sales?
Here's where economists get excited: just because variables correlate doesn't prove causation. Ice cream doesn't cause drowning. Higher education might lead to higher income, or maybe motivated people pursue both.
This distinction prevents costly policy mistakes based on misleading patterns.
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Why might this correlation-causation confusion lead to bad economic policies?
Spurious correlations are statistical coincidences that fool us into seeing relationships that don't exist. Divorce rates once correlated strongly with margarine consumption - pure coincidence!
In economics, we see spurious correlations between stock prices and random events all the time.
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What absurd correlation might you find if you looked hard enough?
