What is correlation vs causation?

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Correlation vs Causation 

Correlation and causation are key concepts in statistics, but they represent very different relationships between variables.

Correlation:

Correlation means that two variables change together — as one increases or decreases, the other tends to do the same (or the opposite). It shows a statistical association, not a cause-effect relationship.

• Example: Ice cream sales and drowning cases may rise in summer. These two are correlated, but one does not cause the other.

• Measured by the correlation coefficient (r), ranging from -1 to 1:

  • +1: Perfect positive correlation

  • 0: No correlation

  • -1: Perfect negative correlation

Causation:

Causation means that one variable directly affects another — a cause-and-effect relationship.

• Example: Smoking causes lung disease. Here, smoking is not just correlated but causes health issues.

Key Differences:

Aspect	Correlation	Causation
Definition	Variables move together	One variable affects another
Direction	No direction of influence	Clear cause and effect
Proof Need	Just statistical link	Experimental or logical proof

Conclusion:

Just because two things are correlated doesn't mean one causes the other. "Correlation does not imply causation" is a crucial principle to avoid false conclusions in data analysis.T

Read More:

What are Type I and Type II errors?

What is a confidence interval?

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