1. Correlation :
It is a statistical term which depicts the degree of association between two random variables. In data analysis it is often used to determine the amount to which they relate to one another.
Three types of correlation-
- Positive correlation –
If with increase in random variable A, random variable B increases too, or vice versa.
- Negative correlation –
If increase in random variable A leads to a decrease in B, or vice versa.
- No correlation –
When both the variables are completely unrelated and change in one leads to no change in other.
2. Causation :
Causation between random variables A and B implies that A and B have a cause-and-effect relationship with one another. Or we can say existence of one gives birth to other, and we say A causes B or vice versa. Causation is also termed as causality.
Correlation does not imply Causation.
Correlation and Causation can exist at the same time also, so definitely correlation doesn’t imply causation. Below example is to show this difference more clearly-
No battery in computer causes computer to shut and also causes video player to stop shows causality of battery over laptop and video player. The moment computer shuts, video player also shuts shows both are correlated. More specifically positively correlated.
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