Difference between Correlation and Association

In statistics, there is a distinction between correlation and association. Correlation measures the strength of the relationship between two variables, while association measures how likely it is that one variable is caused by another. In many cases, these two concepts are confused with each other. However, it is important to understand the difference so that proper conclusions can be drawn from the data.

Contents

What is Correlation?

Correlation is a statistical measure that indicates how closely two variables are related. Positive correlation occurs when the values of one variable increase as the values of the other variable increase.

• A negative correlation occurs when the values of one variable decrease as the values of another variable increase. Correlation can range from -1.0 to 1.0, with -1.0 indicating perfect negative correlation and 1.0 indicating perfect positive correlation.
• A correlation of 0 indicates that there is no relationship between the two variables. Correlation is often used to determine whether two variables are linearly related. However, it is important to note that correlation does not necessarily imply causation.
• In other words, just because two variables are correlated does not mean that one variable is causing the other to change. Correlation is just a measure of how closely two variables are related and does not indicate causation.

What is Association?

Association is a statistical concept that measures the relationship between two variables. In other words, it quantifies how strongly those variables are related. Association can be positive or negative, depending on the direction of the relationship.

• A positive association means that as one variable increases, so does the other. For example, there would be a positive association between hours spent studying and grades received. A negative association means that as one variable increases, the other decreases.
• There would be a negative association between hours spent watching television and grades received. Association is usually measured using correlation coefficients, which range from -1 to 1. A value of 0 indicates no association, while a value of 1 or -1 indicates a perfect positive or negative association, respectively. Values close to 1 or -1 indicate a strong association, while values closer to 0 indicate a weak association.
• Association is an important concept in statistics because it allows us to quantify the strength of relationships between variables. This information can be used to make predictions and draw conclusions about those variables.

Difference between Correlation and Association

• Correlation and association are two important concepts in statistics that are often confused. Correlation is a measure of how two variables are related.
• For example, there may be a positive correlation between the number of hours of television people watch and the amount of junk food they eat. In other words, as one variable increases, the other tends to increase as well.
• Association, on the other hand, simply describes the relationship between two variables. It does not attempt to measure the strength of that relationship.
• For instance, we might say that there is an association between watching television and eating junk food, but we cannot say that one causes the other. Correlation and association are both important concepts that help us to understand the relationships between different variables.

Conclusion

Correlation and association are two different ways of looking at data. Correlation looks at how two variables change together, while association looks at whether one variable is associated with another. In order to understand the difference between correlation and association, you need to first understand what a correlation coefficient is. The correlation coefficient measures the strength of the relationship between two variables.

A positive correlation means that as one variable increases, so does the other, while a negative correlation means that as one variable increases, the other decreases. An example of a strong positive correlation would be height and weight – as someone gets taller, they usually weigh more. An example of a weak (or nonexistent) positive correlation might be the number of hours spent studying per week and grade point average – there may be some sort of relationship between these two variables, but it’s not very strong.