Correlation, causation, or association - What does it all mean???
From allaboutaddiction.com: A comment posted by a reader on a recent post reprimanded me for suggesting that marijuana caused relationships to go bad. While in that instance the reader was mistaken, as I had specifically used the word associated, the comment made me think that maybe I should explain the differences here. I'm a scientist studying addiction, and in the field, it's very important to be clear about what each of the words you use means.
So let's get into it:
Correlation - When researchers find a correlation, which can also be called an association, what they are saying is that they found a relationship between two, or more, things. For instance, in the case of the marijuana post, the researchers found an association between using marijuana as a teen, and having more troublesome relationship in mid, to late, twenties.The trouble is, there could be other things affecting this relationship that the researchers don't know about, but read on.
Causation - When an article says that causation was found, this means that the researchers are saying that changes in one thing they measured directly cause changes in the other. An example would be research showing that jumping of a clifff directly causes great physical damage.
Most of the research you read about indicates a correlation between variables, not causation. You can find the key words by carefully reading. If the article says something like "men were found to have," or "women were more likely to," they're talking about associations, not causation.
Why the difference?
The reason is that in order to actually be able to claim causation, the researchers have to split the participants into different groups, have some participants engage in the activity they want to study (like taking a new drug), while the rest don't. This is in fact what happens in clinical trials of medication because the FDA requires proof that the medication actually makes people better. Obviously, it is much more difficult to prove causation than it is to prove an association.
So should we ignore correlations?
No! Correlations are still important and still need to be looked at, especially in some areas of research like addiction. We can't randomly give people drugs like methamphetamine as children and study their brain development to see how the stuff affects them, that would be unethical. So what we're left with is a need to study what meth use (and use of other drugs) is associated with. It's for this reason that researchers use special statistical methods to assess associations, making certain that they are also considering other things that may be interfering with their results.
In the case of the marijuana article, the researchers ruled out a number of other interfering variables known to affect relationships, like aggression, gender, education, closeness with other family members, etc. By doing so, they did their best to assure that the association found between marijuana and relationship status was real. As more researchers assess this relationship in different ways, we'll learn more about its true nature.
This is how we found out that smoking causes cancer. Through endlessly repeated findings showing an association. That turned out pretty well, I think...