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Correlation vs Causation: All you Need to Know About

In this blog, we will going to share with you the difference between correlation vs Causation. Let’s get started:-

Information or data in the correct hands can be immensely powerful. It is an important factor for any decision. The famous American statistician W.Edward Deming said the famous quote, “In God we trust. Everyone else brings data.”    

Most of the time, data or information can be misconstruction or misunderstood. One of the major misunderstandings is that correlation and causation are similar.   

Our world becomes more scientific day by day. Every subject or topic can measure by analysis of the data. For example, the measurement of the population of a particular country is by collecting the data by people who do surveys.

The statistics subject help in collecting the data and also help in arranging or managing the data. It helps in finding out the reasons, causes, or effects behind the changing conditions in the population. Statistics also help you in explaining correlation vs. causation. Through this blog, you will understand the difference between both.

First of all, we understand both concepts; then, we will discuss the difference between correlation vs causation: 

Correlation vs Causation

Correlation

A correlation is a statistical measure that we use to describe the linear relationship between two continuous variables. For example, height and weight. Generally, the correlation is used when there is no identified response variable. It estimates the strength or direction between two or more variables that have a linear relationship.         

The Pearson correlation measures the linear relationship between two variables. We can estimate the the population correlation by using it.  

Types of correlation

1 Positive Correlation

 A positive correlation is a relationship between two variables. The value of these two variables increases or decreases together. For example, Time spent studying and grade point averages, Education and income levels, Poverty and crime levels.

2 Negative correlation

A negative correlation is a relationship between two variables that the value of one variable increases, the other decreases. For example, Yellow cars and accident rates, Commodity supply, and demand, Pages printed and printer ink supply, Education, and religiosity.

3 No correlation

When two variables are entirely unrelated, then is the case of no correlation. For example, change in A leads to no changes in B, or vice versa.

Causation

If the capacity of one variable to influence others, then it comes under causation or causality. The first variable is the reason to bring the second one into existence. The second variable can fluctuate because of the first variable.

Causation is also known as causality.

From the above explanation, you can get clarity on both. Now we understand the difference between Correlation vs Causation.

Correlation vs Causation: help in telling something is a coincidence or causality

The main difference is that if two variables are correlated. That does not mean that one causes the reason for happening. 

The basic example to demonstrate the difference between correlation and causation is ice cream and car thefts.  

Ice cream sales or stolen cars have a highly positive correlation. When the sale of ice cream rises, then the number of cars stolen also rises.  

It is not the valid reason that ice cream eating behind the reason to steal cars. This is not a casual relationship between cars stolen and ice cream. Behind it, there is a third reason that explains the correlation between sales of ice cream and car thefts. The third reason is the weather.  

In the summer, both are increasing that is ice cream sales get an increase. Or cars get stolen in the more numbers. 

Therefore, ice cream and car thefts do not have a casual relationship. But they are correlated.

One of the examples of a causal relationship is the link between smoking and cancer. There are higher chances of correlation between people who smoke and people who contract disease.

Further explanation is that the data has shown the conclusion that there is a causal relationship between smoking and contracting diseases (cancer).   

 To conclude, correlation does not imply causation.

Final words

From the above discussion, you can get the knowledge of both correlation and causation. Theoretically, it is easy to identify the distinction between both. Don’t conclude too quickly. After studying the correlation, take time to understand the causation. Find the hidden factor behind both and then conclude. 

The above explanation explains the difference between both. If you are facing difficulty in understanding the difference or looking for the best math assignment help. Then we are here to provide you the best help with maths assignment. We are the best math assignment helper in the world.

Our experts are available 24*7 with professional experiences regarding this writing. So do not worry and communicate with our team whenever you need professional help. Utilize your time in other work and prepare for your exams.

What is correlation?

A correlation is a statistical measure that we use to describe the linear relationship between two continuous variables. For example, height and weight. Generally, the correlation is used when there is no identified response variable. It estimates the strength or direction between two or more variables that have a linear relationship.

What is causation?

If the capacity of one variable to influence others, then it comes under causation or causality. The first variable is the reason to bring the second one into existence. The second variable can fluctuate because of the first variable.

Title
Correlation vs Causation: All you Need to Know About
Article
Correlation vs Causation: All you Need to Know About
Description
The statistics subject help in collecting the data and also help in arranging or managing the data. It helps in finding out the reasons, causes, or effects behind the changing conditions in the population. Statistics also help you in explaining correlation vs. causation. Through this blog, you will understand the difference between both.
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Statanalytica
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