Why correlation does not imply causation?
Correlation and causation are terms which are mostly misunderstood and often used interchangeably. Understanding both the statistical terms is very important not only to make conclusions but more importantly, making correct conclusion at the end. In this blogpost we will understand why correlation does not imply causation.
A lot of times we have heard “correlation does not cause causation” or “correlation does not imply causation” or “correlation is not causation”. But what they mean actually by saying this?
You will get a clear idea once we go through this blogpost. So let’s start!
Getting the basics right
Correlation is a statistical technique which tells us how strongly the pair of variables are linearly related and change together. It does not tell us why and how behind the relationship but it just says the relationship exists.
Example: Correlation between Ice cream sales and sunglasses sold.
As the sales of ice creams is increasing so do the sales of sunglasses.
Causation takes a step further than correlation. It says any change in the value of one variable will cause a change in the value of another variable, which means one variable makes other to happen. It is also referred as cause and effect.
Example: When a person is exercising then the amount of calories burning goes up every minute. Former is causing latter to happen.
So now we know what correlation and causation is, it’s time to understand “Correlation does not imply causation!” with a famous example.
Ice cream sales is correlated with homicides in New York (Study)
As the sales of ice cream rise and fall, so do the number of homicides. Does the consumption of ice cream causing the death of the people?
No. Two things are correlated doesn’t mean one causes other.
Correlation does not mean causality or in our example, ice cream is not causing the death of people.
When 2 unrelated things tied together, so these can be either bound by causality or correlation.
In Majority of the cases correlation, are just because of the coincidences. Just because it seems like one factor is influencing the other, it doesn’t mean that it’s actually does.
Correlation is something which we think, when we can’t see under the covers. So the less the information we have the more we are forced to observe correlations. Similarly the more information we have the more transparent things will become and the more we will be able to see the actual casual relationships.
Consider underlying factors before conclusion
In some cases there are some hidden factors which are related on some level. Like in our example of ice cream sales and homicide rates , weather is the hidden factor which is causing both the things.Weather is actually causing the rise in ice cream sales and homicides. As in summer people usually go out, enjoy nice sunny day and chill themselves with ice creams. So when it’s sunny, wide range of people are outside and there is a wider selection of victims for predators.
There is no causal relationship between the ice cream and rate of homicide, sunny weather is bringing both the factors together. And yes, ice cream sales and homicide has a causal relationship with weather.
Don’t conclude too fast!
Just after finding correlation, don’t draw the conclusion too quickly. Take time to find other underlying factors as correlation is just the first step. Find the hidden factors, verify if they are correct and then conclude.
Hope this post cleared your doubts!
Thanks for reading!!
