Correlation and causation are one of the most important but confusing topics of statistics. Correlation gives the relationship between two variables, whereas causation means one event is cause due to another. In this article, we will discuss the difference between Correlation and Causation.
Correlation and Causation are two of the most important concepts to understand when it comes to understanding the world around us. But what’s the difference between the two?
Simply put, Correlation is when two things happen together, while Causation is when one thing causes another thing to happen. So, for example, you might say that there is a correlation between ice cream sales and crime rates because you notice that they both seem to rise and fall together. This doesn’t mean that eating ice cream causes crime rates to go up (Causation), but it could suggest that another factor at play, like temperature, affects both of them.
In this article, we’ll go over the key differences between Correlation and Causation and give you an example to get a better understanding of Correlation and Causation.
Table of Content
- Difference between Correlation and Causation
- Example of Correlation and Causation
- Why doesn’t Correlation mean Causation?
- Ways to test Correlation and Causation
Difference between Correlation and Causation
|Correlation means there is a relationship between the values of two variables.
|Causation means one event causes another event to occur.
|Correlation doesn’t imply Causation.
|Causation always implies Correlation.
|Both Independent and Dependent Variable are needed.
|Both Independent and Dependent Variable needed.
|Tiredness and Bad mood are correlated.
|Tiredness and Bad mood are caused due to traffic jams.
What is Correlation?
Correlation is the degree of association between two random variables.
- It describes the size and direction of the relationship between two variables.
- Correlation does not imply that change in one variable is the cause to change in another variable.
- The value of Correlation Coefficient varies from -1 to 1.
- A Scatter plot is used to determine whether there is any correlation between two variables or not by displaying the data as a set on the XY-plane.
There are three ways to describe the correlation between two variables:
Positive Correlation: When both the variable moves together in the same direction, i.e. As x increases, y tends to increase.
- When the value of correlation coefficient is 1 it is called Perfect positive.
Negative Correlation: When both variables move together in the opposite direction, i.e., As x increases, y tends to decrease.
- When the value of the correlation coefficient is -1, it is called Perfect Negative.
Zero Correlation: When the change in one variable have no action on the other.
Read Also: Skewness in Statistics
What is Causation?
Causation means one event causes the second event to occur.
Firstly, Causation indicates two possibilities occur simultaneously or one after the other. Secondly, it also tells that both events do not occur jointly, but the cause of one drives the second to occur.
- It is also known as Cause and Effect or Causality.
- It can be determined from an approximately designed experiment.
- The two variables are correlated to each other, and there is also a causal link between them.
Example of Correlation and Causation
At this point, you will have an understanding of Correlation and Causation and the difference between them. If not, let’s have some examples from our day-to-day life to get a better understanding.
The above example shows a correlation between tiredness and bad mood because these two events are related. But neither event actually causes the other. Instead, both events are caused due to Traffic Jams.
Consider two events:
A: Eating Ice-Cream
B: Getting Sunburn
Both events are correlated to each other as the events are related. But neither eating ice cream nor getting sunburned actually causes others. Instead, both events are caused due to sunny weather.
Why doesn’t Correlation mean Causation?
Correlation and Causation are the most confusing concepts in statistics.
Let’s take two variables:
- Smoking and Alcoholism may be correlated, but smoking doesn’t cause Alcoholism.
- Smoking increases the risk of lung cancer.
From the above example, you got that whether two variables are correlated does not mean there is a causation between them. The relationship between them may be due to any third variable, or it’s just a coincidence.
Generally, there are two main reasons why Correlation doesn’t mean Causation.
- Directional Problem: When two variables are correlated and have a casual relationship, it is difficult to depict the independent variable (the cause) and the dependent variable ( the effect).
- Third Variable Problem: When the unmeasured variable (the third variable) affects both variables, they seem to have a casual relationship.
Ways to Test Correlation vs. Causation
- Randomized and Experimental Study
- Quasi-Experimental Study
- Correlation Study
- Single Subject Study
In this article, we have briefly discussed the difference between Correlation and Causation with a different example.
I hope you will like the article.