What is correlation vs causation?

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Correlation and causation are both ways to describe relationships between variables, but they mean very different things.

Correlation:

  • Definition: A statistical relationship or pattern between two variables—when one changes, the other tends to change as well.

  • Important Note: Correlation does not imply causation.

  • Measured with correlation coefficients (e.g., Pearson’s r), ranging from -1 to 1:

    • +1 = perfect positive correlation

    • -1 = perfect negative correlation

    • 0 = no correlation

Example: Ice cream sales and drowning incidents may rise together, but ice cream doesn't cause drowning. A third variable—hot weather—influences both.

Causation (Cause and Effect):

  • Definition: One variable directly causes a change in another.

  • Proven through controlled experiments, not just data patterns.

  • Requires ruling out confounding variables and showing temporal precedence (cause happens before effect).

Example: A clinical trial shows that taking a specific drug reduces blood pressure—this is causation, because other variables were controlled.

Summary:

Correlation means two things are related.
Causation means one thing causes the other.
Always investigate further before assuming causation from a correlation.

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