Correlation does not imply causation is an interesting phrase that you will hear mostly in statistics and when learning about data science in detail. But what does it mean? Well, it merely indicates that just because two things correlate does not always mean that one causes the other. For example, the Norwegian winter is cold, and people tend to spend more money on buying hot foods such as soup than they do in summer. However, this does not mean that cold weather causes people to spend more money on soup. Therefore, although the expenditure of people in Norway is related to cold weather, the spending is not the cause of the cold weather. Hence, correlation is not causation. 

Note that there are two essential terms in this phrase: correlation and causation. Correlation reveals how strongly a pair of variables are related to each other and change together. Causation explains that any change in the value of one variable will cause a difference in the amount of another variable. In this case, one variable makes the other variable happen. This phenomenon is known as cause and effect. For example, when you exercise (X), the amount of calories (Y) you burn is higher every minute. Hence, X causes Y. According to the theory of logic, we can say the following:

The most common example found in any data analysis book is about the sales of ice cream and the rise and fall of homicide. According to this example, as there is an increase in ice cream sales, there is an increase in the number of homicides. Based on the correlation, these two events are highly co-related. However, the consumption of ice cream is not causing the death of people. These two things are not based on the cause and effect theory. Therefore, correlation does not mean causation.

So, what is the takeaway from this critical phrase? Well, first of all, we should not form our conclusions too quickly based on correlation. It is essential to invest some time in finding the underlying factors of the data in order to understand any critical, hidden factors.