Correlation and causation are terms which are mostly misunderstood and sometimes used interchangeably. Understanding both the statistical terms are extremely important not only to form conclusions but more importantly, making correct conclusion at the top. During this blogpost we’ll understand why correlation doesn’t imply causation.
A lot of times we’ve heard “correlation doesn’t cause causation” or “correlation doesn’t imply causation” or “correlation isn’t causation”. But what they mean actually by saying this?
You will get a transparent idea once we undergo this blogpost. So let’s start!
Getting the fundamentals right
Correlation may be a statistical technique which tells us how strongly the pair of variables are linearly related and alter together. It doesn’t tell us why and the way behind the connection but it just says the connection exists.
Example: Correlation between frozen dessert 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 within the value of 1 variable will cause a change within the value of another variable, which suggests one variable makes other to happen. it’s also referred as cause and effect.
Example: When an individual is exercising then the quantity of calories burning goes up every minute. Former is causing latter to happen.
So now we all know what correlation and causation is, it’s time to know “Correlation doesn’t imply causation!” with a famous example.
Ice cream sales is correlated with homicides in NY
As the sales of frozen dessert rise and fall, so do the amount of homicides. Does the consumption of frozen dessert causing the death of the people?
No. Two things are correlated doesn’t mean one causes other.
Correlation doesn’t mean causality or in our example, frozen dessert isn’t causing the death of individuals .
When 2 unrelated things tied together, so these are often either bound by causality or correlation.
In Majority of the cases correlation, are simply because of the coincidences. Simply because it looks like one factor is influencing the opposite , it doesn’t mean that it’s actually does.
Correlation are some things which we expect , once we can’t see under the covers. Therefore the less the knowledge we’ve the more we are forced to watch correlations. Similarly the more information we’ve the more transparent things will become and therefore the more we’ll be ready to see the particular 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 frozen dessert sales and homicide rates , weather is that the hidden factor which is causing both the items .Weather is really causing the increase in frozen dessert sales and homicides. As in summer people usually leave , enjoy nice sunny day and chill themselves with ice creams. So when it’s sunny, wide selection of individuals are outside and there’s a wider selection of victims for predators.
There is no causal relationship between the frozen dessert and rate of homicide, sunny weather is bringing both the factors together. And yes, frozen dessert sales and homicide features a causal relationship with weather.