As we know statistics is important in our life. But there are different main topics and concepts in statistics. The two of them are Causation and Correlation. But both might seem to be interchangeable. While there are several points of difference between them. Both are somewhat different from each other. Several students do not know and understand the difference between causation and correlation. If you are also one of them, then you are at an accurate place.
In this blog, we will discuss the main difference between causation and correlation. But first, we discuss the overview of both the terms: Causation And Correlation.
Causation
Causation is a statistical measure of how one variable influences the other. This occurs when the value of one variable rises or falls as a result of a change in the value of the other variable.
Variables are observed and modified in a controlled experiment. That is why causal research has higher internal validity than correlational research. Causative research may not have as high an external validity as correlational research. But there are many extra variables at play in a real-life situation.
Causal research has two primary goals:
- Identifying the factors that are the “cause” and those that are the “effect.”
- The nature of the link between the cause and effect variables must be understood.
Correlation
Correlation is a statistical metric. It depicts the degree to which two variables are linearly connected. It is a research tool for expressing associations between variables without making a cause-and-effect claim.
Correlational research is a type of study in which a researcher evaluates two variables. By this, they understand and assess their relationship without the use of any other factors. The researcher obtains data on the variables without influencing them. It is a distinguishing aspect of correlational research.
Correlations can be divided into three categories:
Positive Correlation
A positive correlation between two variables means that when one variable’s value rises, so does the value of the other. This also happens the other way around. As the value of one variable fall, the value of the other falls as well. As a result, both variables are moving in the same direction.
Negative Correlation
When two variables move in opposite directions, there is a negative correlation between them. That means, when one variable increases, the other one decreases, and vice versa.
No Correlation
When two variables have no association. A change in one may not be caused by a change in the other.
External validity is strong in correlational research. It allows the study’s findings to be used in real-world situations. However, due to the lack of a controlled setting, it has low internal validity. It makes it difficult to discover causal correlations between variables.
Difference Between Causation And Correlation
Causality and correlation can coexist. But, correlation does not always indicate causation. Causation refers to situations in which action A causes result B. On the contrary, Correlation is simply a relationship. Action A is related to Action B. But one event may not every time tend to the occurrence of the other event.
Because the human brain wants to find out patterns even when they do not exist. Correlation and Causation are frequently confused. When two variables appear to be so tightly related that one is dependent on the other. They frequently create these patterns. That implies a cause-and-effect connection, with the dependent event being the outcome of an independent event.
Even if two actions appear to be occurring simultaneously in front of our eyes, we cannot simply conclude causality. One, our views are entirely based on anecdotes. Two, there are several additional options for forming a partnership, including:
- The exact opposite is true: B is the cause of A.
- The two are linked. But there is more to it: A and B are linked. But C causes them.
- There is one more factor to consider: A does cause B—as long as D occurs.
- There is a chain reaction here: A causes E, and E produces B. (But you only notice that A causes B with your own eyes).
5 Ways To Check The Difference Between Causation And Correlation
Experimental And Randomized Study
Here is where you assign people to the experimental group randomly. There is a control group and an experimental group in the experimental design. By allocating persons to the experimental group randomly. You reduce experimental bias,
Quasi-Experimental Study
Such as surveys, interviews, and observational notes.
Correlation Study
This is where you try to figure out if two variables are linked or not.
Single-Subject Study
A single-subject acts as his or her control instead of control or experimental group. Attempting to modify the individual’s behavior or thinking is a concern for the researcher. Because it is difficult to presume that one person’s behaviors reflect those of a vast group. In marketing, this study is rarely used.
Stories
Sometimes the only way to prove causation is to listen to first-hand stories. The difficulty is that, despite this, they may have a true complaint and convey it convincingly. They do not have a good solution. These accounts do not establish beyond possible doubt that A causes B.
Conclusion
Thus, in the above blog, we have discussed the difference between causation and correlation. We also learned the ways to check the difference between causation and correlation. We have provided you with all essential and accurate information regarding causation and correlation. I hope you understand this blog. And this blog helps you in learning the difference between causation and correlation.
