I have to disagree. It repeats the same pseudoscientific presentation of correlation, as having to do with the semantics of the data.
Generically, correlation has nothing to do with whether data are related, dependent or independent.
It only is an indication of this *if you already believe* they are dependent. There has to be a prior semantic model of what the data means (what it is a measure of, how reliable its a measure of it, etc.) before correlation measures anything at all.
The only reason we would suppose correlated data to be related, is just that we design experiments to already contain possible dependencies. We do not, as a habit, measure say, the fall of rain and the beat of a song playing. But these would, given suitable measurements, be correlated.
This becomes absolutely vital to understand when experiments do take this "hoover up everything, arbitrarily" approach. As often they do in the social and psychological sciences.
Generically, correlation has nothing to do with whether data are related, dependent or independent.
It only is an indication of this *if you already believe* they are dependent. There has to be a prior semantic model of what the data means (what it is a measure of, how reliable its a measure of it, etc.) before correlation measures anything at all.
The only reason we would suppose correlated data to be related, is just that we design experiments to already contain possible dependencies. We do not, as a habit, measure say, the fall of rain and the beat of a song playing. But these would, given suitable measurements, be correlated.
This becomes absolutely vital to understand when experiments do take this "hoover up everything, arbitrarily" approach. As often they do in the social and psychological sciences.