How do you define and differentiate between a correlation and causal findings in research?
Correlation means that 2 variables are related in a linear pattern.
Causation means that one causes another.
The main difference between the two is how the treatments in the research are delegated.
Let's define the concepts mentioned in this question.
Correlation basically describes a linear relationship. All this says is that the two variables are RELATED.
Causation, on the other hand, states that one variable CAUSES the other one.
If you don't understand how correlation and causation are different, here's an example (if you do understand, then skip this paragraph): data has shown that as air conditioning machine sales rise, drowning rates also increase. The two variables are correlated because they are related. However, just by common sense, we know that one does not cause the other.
To differentiate whether the research has produced correlational or causal findings, we need to examine how the research was conducted.
If the subjects you studied were allowed to choose their own treatments, then only correlation can be concluded, and NOT causation.
If the researcher forcefully assigns treatments to the subjects, then causation can be proved.
Why? Because if you let your subjects choose their own treatments, there might be another underlying reason (a confounding variable) that causes both your subject to choose one treatment over another and the results you see in your experiment.