We know that correlation and causation talks about the relationship between 2 variables.

Correlation is a statistical measure to know the strength of relationship between 2 variables.

It will be measured using the below equation.

To know more about this equation please check the article Why the Correlation Coefficient r ranges between -1 and +1.?.

This formula states the measure of the strength of linear relationship between 2 variables.

We can think that, relationship means one variable makes some impact in another variable. This is called Causation.

But this does not mean that whenever the correlation coefficient is high, there is always a Causation.

Correlation does not mean Causation.

### Correlation With Causation:

The change in one variable does makes an impact in another variable.

For example height and weight.

### Correlation without Causation:

Sometimes the measure Correlation Coefficient is high but in real world scenario it does not mean anything. It means the data is purely a coincidence.

For example the correlation coefficient of a movie release of a famous actor and raining at the time of release is 0.9. But we know that this is purely a coincidence, and that actor or movie is nothing to do with the weather.

In this case Correlation does not mean any Causation. If we go with the correlation coefficients and make any model based on this statistics, that will fail for the new data set.

Hope you understand that Correlation need not to make a Causation

Great example. Give an example for causation too. It will be nice

Thank you.

Below is the example for Causation:

For example the correlation coefficient of a movie release of a famous actor and raining at the time of release is 0.9. But we know that this is purely a coincidence, and that actor or movie is nothing to do with the weather.

Very well explained, thank you

Thank you

Good Examples 🙂 Thanks for the explanation

Thank you Sri!

Loved that cartoon!

Thank you Hafizul Azeez