🔗 Introduction
One of the most common errors in data analysis is confusing correlation with causation. Understanding the difference is crucial for drawing valid conclusions from your analyses.
📊 What is Correlation?
Correlation measures the strength and direction of a linear relationship between two variables. It is represented by the correlation coefficient r, ranging from -1 to +1.
⚠️ Warning: A strong correlation does NOT imply a cause-and-effect relationship!
🎯 What is Causation?
Causation means that one variable directly causes a change in another.
🍦 Famous Examples of Misleading Correlations
Ice cream sales and drownings: Both increase in summer due to hot weather (confounding variable).