Correlation Is Not a Causation Statement

Introducing 

A poor judgment prevails for the terms Correlation is not a Causation statement. But we can use them equally. It is significant to understand both the real terms. It makes the right ends toward the end. Above all, the Correlation does not recommend Causation. A ton of times, we have heard “correlation does not cause causation” or “correlation is not causation.” However, what do they mean by saying this? It refers to no cause and effect link between the two variables.  

Correlation is a true strategy. It discloses to us the conventional relation. Also, it reveals the change in the pair of factors. Correlation is something which we figure when we cannot see under the covers. So the less data we have, the more we can notice relationships. Also, the more data we have, the more direct things will become. Having more data will provide us the option to see genuine, relaxed connections. It is a proportion of how confidently related two things are. In most cases, Correlation is a direct result of incidents. Because it seems like one factor is impacting the other, it doesn’t imply that it does. 

Firstly, we consider a number. The number exposes the relative change in one thing with an adjustment in the other. At first, we will consider that 1 and -1 are the solid positive and negative numbers. They make connections between the two arrangements of numbers. After that, we will consider that 0 is without a relationship at all. There should be a reason behind the relationship. Correlation doesn’t disclose it to us. It just says that the relationship exists.

“Correlation is not causation” is an insightful statement. This declaration refers to failure. It makes connections between two factors exclusively based on a noticed affiliation or relationship between them.

Usage of the statement Correlation is not Causation. 

An argument should have a reasoning thing. The reasoning of that argument does not imply a false result. Imply means “is a sufficient condition for’ in logic. Statisticians intend it. They say that Causation is not certain. Implies means suggests rather than requires in casual use. Causation exists with Correlation. But there is an order in time from root to effect. There is also a likely mechanism. We can use Causation for common causes.

On the other hand, we can use Correlation to deduce Causation. Correlation is a basic condition but not a sufficient condition. This statement has a wide usage. We will discuss some examples in the next paragraph.

 

A lot of studies show some phenomena related to medical science. Some women take combined hormone replacement therapy (HRT). As a result, they face a low incidence of coronary heart disease (CHD). The doctors decided by this study that HRT was protective against CHD. On the other hand, later trials showed a different result. The use of HRT pointedly increased the risk of CHD.

Further study showed that the women who take HRT were from a strong economic background. Therefore, they usually took a better diet. The use of HRT and CHD are two factors here. They were coincident effects of a joint cause. There is a link between a good economy and the benefits. Thus, it proves that Correlation is not Causation.

Causal analysis of Correlation is not Causation.

In general, causal analysis forms cause and effect. It is not only the field of statistics but also a tentative scheme. We consider two correlated events. The events are X and Y. As a matter of fact; there might be quite a few relationships caused by these events. Possible relationships include:

• X causes Y. It is direct Causation
• Y causes X. It refers to reverse Causation.
• Z acts as co-founder and causes both X and Y
• X causes Y, and Y causes X. This is cyclic or bidirectional Causation.
• No connection between X and Y. This Correlation refers to a coincidence.

Here, a coincidence is a notable accord of events. It does not have any obvious causal link with one another. The view of it may lead to mystic claims. Likewise, coincidences are certain, as said from a statistical point of view. We can include an example here. Two people can face a problem on the same birthday. The probability of this problem can cross 50% in a group. Here, the group consists of 23 persons.

Most importantly, Causation is more advanced than Correlation. It states any variation in the value of one variable. Moreover, it will cause a change in the value of another variable. Causation implies one variable makes others occur. It denotes positions and logical results.

To sum up, a direct conclusion is not possible for the relationship between Correlation and Causation. The only fact is the Correlation between X and Y. If the relationship between these two factors is a cause and effect, then they need further study. It does not matter if the relationship has a statistical impact. Besides, we can observe a large effect size. Moreover, we get a reason for a huge part of the variance. 

Correlation is not Causation in marketing.

Causation equals cause and effect. On the other hand, Correlation means a relationship exists. But, no one can prove cause and effect. However, a statistical correlation exists between two factors. Firstly, the ages of Miss America winners crowned between 1999 and 2009 is one factor. Secondly, the number of homicides committed with heated objects during the same period. But, if you attempted to regulate the ages of Miss America contestants to reduce the rate of homicides with heated objects, people would laugh at you.

If we see marketing, we can put the example of Walmart. It often bases marketing strategies on correlations between weather conditions and the sales of certain products. The company cannot prove Causation, and the relationships could ultimately be a coincidence. Walmart is reasonable to believe they are not. Walmart knows that, if the correlations are coincidences, there will be a reflection in sales performance. The retailer then may change tactics.

Walmart understands that Correlation is not Causation. On the other hand, many brands do not understand. This leads them to make rubbish digital marketing decisions. Similarly, companies often pay coupon publishers on SEO traffic high commission rates. But when they reduce commissions to test the theory, they see that revenue does not change. Thus, the statement correlation is not Causation becomes true.

A random example of Correlation is not Causation.

We can consider the general public in any European country with a cold winter. They will spend more in the shops when it is cold. They will spend less when it has a burning temperature. But, it does not mean that cold temperature causes furious high spending. We will give a possible clarification. The cold weather will normally concur with Christmas and the New Year deals. The entire world could not catch the statement though they got a significant truth as an example. This is because we doubt it.  

 

We can put some other examples here also. According to a study, the sale of ice cream correlates with murder in a city of America. It shows only the Correlation between these two occurrences. There is no causation between them. The sale of ice cream may go up and down. A similar thing happens for the number of murders. They are correlated. The sale of ice cream is not responsible for the murder. The sale depends on the weather. These two things are unrelated. 

 

In this example, the weather may be a hidden aspect. In summer, the weather is good to go outside. People go outside for a picnic or roaming. The weather is hot. So, many people will buy ice creams, and sales are high at that time.

On the other hand, a lot of people are outside. So, criminals get a friendly atmosphere to commit crimes. More people get victimized. Because of summer and hot weather, both the sale and murder have an increase. So, the murder and the sale of ice cream only have a causal relationship with the weather. In this example, the weather is the co-founder. 

A notable case of Correlation is not Causation.

In this section, we are going to put some remarkable examples to judge the statement. The first example is from a phenomenon from the 1950s. At that time, the rate of lung cancer had a dramatic rise. Smoking causes cancer. No one argued about the Correlation between them.

Moreover, people believed that smoking is the main cause of lung cancer. But, there might be some other factors as a co-founder. Those factors might be liable to correlate these two main factors. Advancement of the diagnosis system might be a co-founder. Better diagnosis means more detection of lung cancer.

Most importantly, industrial pollution or pollution from vehicles might be responsible for getting cancer. The scientists studied more on it and finally found that smoking is the best reason for lung cancer. Above forty thousand doctors performed this study. 

 

Secondly, the level of CO2 in the atmosphere increases for many reasons. It correlates with the level of obesity. Hence, CO2 in the atmosphere causes obesity. These two terms are correlated. Besides, there is a third term here. The richer people are likely to eat more food and use more trees for housing. Thus they produce more CO2 and raise its level. Thus, there is a correlation between the main two factors. The third term is the auxiliary.

 

HDL Cholesterol correlates with the incidence of a heart attack negatively. On the other hand, medication decreases the chance of a heart attack. Also, there are some terms as the third factor. Further research offers proof. The factors are genes, diet, and exercise. They affect both heart attack and HDL level. Medication may directly affect the HDL level. But, it would not affect the chance of a heart attack.

 

The use of Correlation is not Causation as scientific evidence.

The Correlation of variables helps to form most of the scientific proof. Scientists are very cautious about the statement correlation is not Causation. However, people commit the reverse error and dismiss the Correlation. Therefore, their error can dismiss a large number of scientific proof. Maybe there should be a failure to deliver proof for Causation. For instance, there should be a relationship between child abuse and academic performance. The social workers may be keen to know it. Though it is very vital, it is not ethical to examine kids. The examination is about whether people abuse kids or not. The scientists can take a non-experimental correlational plan. A negative correlation can exist among the factors. However, experts might use this information in an accurate relationship.

 

Relation between them 

The statement correlation is not Causation is very valuable in scientific proof. It has a great impact in the fields of medicine, psychology, and sociology. But, the first correlations need confirmation as real. After that, we will explore every possible causative relationship. To sum up, we can decide that we cannot use Correlation alone as proof. The evidence is of a cause and effect relationship between an action and benefit. The proof includes a risk factor and a disease, even though it has various outcomes. Moreover, it is easy and even tempting to come to premature conclusions.

 

Wrapping up with Correlation is not Causation.

Finally, we have to put a conclusion in our discussion. Conclusion about Correlation is not easy. It is a very weak decision to conclude fast as Correlation is just the initial step. Some other factors are also apt, and we have to verify them. Finally, we can consider a decent conclusion that Correlation is not Causation.