Define correlation vs. causation.

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Correlation

Correlation means a statistical relationship or association between two variables. If one variable changes, the other tends to change too, either in the same direction (positive correlation) or opposite direction (negative correlation).

👉 Example: Ice cream sales and temperature are positively correlated — as temperature rises, ice cream sales go up.

Causation

Causation means that one variable directly influences or produces a change in another variable. In other words, it’s a cause-and-effect relationship.

👉 Example: Exercising regularly (cause) leads to improved physical fitness (effect).

Key Difference

• Correlation ≠ Causation → Just because two things happen together does not mean one causes the other.

• Correlation only shows a relationship; causation proves one variable is the reason behind changes in the other.

Why It Matters

Mistaking correlation for causation can lead to false conclusions. For instance, both drowning incidents and ice cream sales increase in summer — but eating ice cream does not cause drowning. The real cause is hot weather influencing both variables.

✅ In short:

• Correlation = Variables move together, but not necessarily due to cause-and-effect.

• Causation = One variable directly causes a change in the other.

Read More :

What is the Central Limit Theorem, and why is it important?

Explain confidence intervals.

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