Association vs. Correlation — What's the Difference?

"Association" and "Correlation" are two terms that, at a glance, might seem synonymous, but they have distinct uses, especially in scientific and statistical contexts. Association broadly refers to any kind of linkage or connection between two entities or ideas. This linkage can be strong, weak, direct, or indirect, and doesn't always imply a cause-and-effect relationship. For example, an association might be made between healthy eating and well-being, but this doesn't mean one directly causes the other.

In contrast, "Correlation" is more specific and is often used within statistics to describe the relationship between two quantitative variables. It measures the strength and direction of a linear relationship, with values ranging from -1 to 1. A value close to 1 implies a strong positive correlation: as one variable increases, the other does too. A value close to -1 implies the opposite. If the value is close to 0, it suggests there's little to no relationship. This term doesn't necessarily mean causation; just because two variables are correlated doesn't mean one causes the other.

It's essential to recognize the breadth of "Association." The term can be employed in various fields, from psychology to business, and doesn't have the quantitative connotations that "Correlation" carries. For instance, an association in business could be a professional organization or a connection between brands and consumer feelings.

"Correlation," being more specialized, is predominantly used when dealing with data analysis. It's a metric that provides insights into how two datasets move in relation to one another. In finance, for instance, correlation might be used to see how two stocks move in relation to one another, but it's important to remember that correlation doesn't imply causation. Just because two stocks move together doesn't mean one's movement is causing the other's.