Correlation is a measure that describes the direction of a relationship or size between the two values or datasets. It also does not suggest causation, like a variable can have correlations but lack causation. While causation is that one sample is the result or cause of the other sample like cause and effect.

Strong Correlation, lack of causation

Independent X: Sales of lemonade

Dependent Y: Death by car crashes

By looking at this we can kind of conclude that the sales of lemonade do not cause death by car crashes. Like something that would correlate with the sales of lemonade could be a hot sunny day. Also, if the sun is very bright and sunny it can cause people to not see while driving, which can cause death by car crashes.

Strong Correlation, Strong Causation

Independent X: Number of hours spent practicing a cheer routine by a cheer team

Dependent Y: How well they score at the competition.

So, in this situation, it all depends on how many hours they put into the cheer routine, which would cause them to score well at the cheer competition.

thought?