What is the difference between correlation and causation?

Correlation and causation are two different concepts that are often misunderstood to mean the same thing.

Correlation refers to a statistical relationship between two variables, where a change in one variable is associated with a change in the other variable. A correlation can be positive (both variables increase or decrease together), negative (one variable increases while the other decreases), or no correlation (there is no relationship between the two variables).

On the other hand, causation refers to a relationship between two variables where one variable causes a change in the other variable. In other words, if we can show that changes in one variable actually cause changes in the other variable, then we have established causation.

The key difference between correlation and causation is that correlation does not imply causation. Just because two variables are correlated, it does not necessarily mean that one variable is causing the other to change. There may be other factors at play that are causing both variables to change or they may be completely unrelated.

For example, there may be a positive correlation between the number of ice creams sold and the number of drownings. However, this does not mean that eating ice cream causes people to drown. It is simply a coincidence that both variables happen to increase during the summer months.

Therefore, it is important to be cautious when interpreting correlations and to use other methods, such as experiments, to establish causation.