A topic that has been in the news a lot is the use of the term “association.” It has typically been used in the context of, “If you use this or take this or get that, there has been an association with something else,” usually something that is perceived as negative. However, when we are talking about medical conditions or scientific studies, the word association doesn’t carry much weight. Given the widespread use of the term, I think it merits a discussion.
The scenario we need to look at when there is a claim that something causes something is whether there is a real effect or more correctly a causation. In science, we often substitute the word correlation for the word association and the phrase cause and effect for when there is a well-defined causation. The difference is important to know and understand to avoid being misled.
I can state that there is an association or correlation between eating ice cream and the number of drownings. You could do a lot of speculation as to why the two might be related, and you’d probably miss the truth. In reality, more people eat ice cream in the summer, and more people swim in the summer. That is the extent of the relationship.
Are they correlated or associated? Yes, indeed they are, but there is no cause and effect. In other words, eating ice cream does not contribute to drownings.
It is important to define these two terms to help distinguish between them and enhance our critical thinking skills. Correlation, or association, is defined in the Merriam-Webster Dictionary as “a relation existing between phenomena or things or between mathematical or statistical variables which tend to vary, be associated or occur together in a way not expected on the basis of chance alone.” Correlation does not imply causation. Just because one thing often happens frequently in the presence of another does not mean there is cause and effect. Correlations can be positive or negative.
As humans, we are wired to look for patterns, and we often try to make these connections help our understanding of a situation or support our opinion. A correlation analysis is considered one of the weakest analyses in statistics. They simply don’t carry much weight. We may use correlations in science if a pattern is highly consistent or if it helps guide us toward a better understanding of a situation through further research.
Cause and effect or causation, on the other hand, demands an actual relationship where one variable “causes” a response in the other. They are linked. In a cause-and-effect relationship, things happen as a result of something else. A cause brings about an action or an effect: “I ate way too much food, and now I feel sick.” Cause and effect. You can plan a picnic, storms develop, and you have to cancel the picnic. Planning the picnic did not cause it to storm. The storm caused you to cancel the picnic.
This is an important difference. I know growing up in a rural environment, many people tried to create a lot of cause-and-effect scenarios from simple things like, “I just washed my car, so I know it’s going to rain now.”
In some instances, it would elevate to conspiracy theory approaches, such as, “My corn crop looks really good right now, so I am certain the government will mess with the commodity markets, and I’ll end up with a low price.” Because correlations and association are common in everyday life, we need to have the skill to separate them from causation or cause and effect.
In medical science, cause and effect is critical in all decisions. Unless a direct relationship can be established, few decisions are made in medicine. There is simply too much at stake (lives) to move forward based solely on associations or correlations.
All science follows this policy at some level, depending on the consequences of being incorrect in the assumption.
So when you read or hear about correlations or associations, understand that there might or there might not be a causation. A correlation does not rule out a relationship of cause and effect. However, when one does exist, the relationship typically moves from a correlation to a causation.
Rick Brandenburg, Ph.D., is a turfgrass entomology professor at North Carolina State University in Raleigh, a post he’s held since 1985. The 28-year GCSAA member is also a frequent presenter for GCSAA, both in webinars and at the annual GCSAA Conference and Trade Show.