Causal vs. Correlation

I began writing a post today that ended up quite lengthy. So I decided to split it up. I will begin today by differentiating two similar, yet significantly different key terms in scientific research: causal and correlation.

When research indicates two events have a causal relationship, it means that event A will directly lead to event B. This is fairly straight forward. It is called a cause and effect.

Now if event C and event D is found to be correlated, it does not imply that one causes the other. It simply means that there is a high incidence of the two events happening together. They share some commonalities, some indirect relationship, but one event does not necessarily lead to another. A classic example is that as ice cream consumption rises, so does the incidence of drowning. Of course you know that eating ice cream does not cause drownings. The relationship they share is that both are popular summer activities. When the weather becomes hot, people will play in the water more, hence, the increase in drownings. At the same time, people are consuming more ice cream due to the high temperatures. Now that you understand the difference, you realize the significance whether two events are classified as causal or correlated. After all, it would be unfair to all of us if ice cream was permanently banned in order to prevent future drownings. Does that last sentence sound eerily familiar? Yep, that's the argument tobacco companies took up for years, alleging that the relationship between smoking and cancer was merely correlational.

Back for more soon.