STATISTICAL METHODS

Statistical Methods

Statistical Methods

Week 3: Correlation

Relationships between Correlation and Causation

To identify the distinction between correlation and causation, it is imperative and important to look at the definitions. In this context, correlation shows a relationship between two variables, usually found in statistics. The most general form is the Pearson's correlation which shows a correlation coefficient represented by r that shows the direction and strength of a linear relationship between the correlation and causation. For case in point, there may be a positive and strong relationship between ice cream and crime as a result of correlation coefficient of 0.84.

Besides it, cause is an act which takes place in a manner that something occurs as an outcome. For instance, the act of clicking and opening on an email link which caused to go to a website. In relation to this, an assumption might be made that because of receiving the email, and going to the website; it is difficult to know that email caused me to go to the website without asking me (Crealock, 2008). In addition to this, it is comparatively easy to make out associations between the data points, though the existence of an association does not involve that the events or data points caused each other. It is a common fallacy that causation is implied by correlation which is not only occurs with analytics, although also in media stories, academic research and business decisions (Chen & Popovich, 2002).

With reference to ice cream and crime, past researchers found positive and strong relationship between sales of ice cream with the crime. The reason of such was that as the sales of ice cream increases then the crime also increases and vice versa. This statement raises important questions that include are factors like time or weather affects ice cream and crime? And also does this mean that buying ice cream causes crime in the society?

A correlation exists when two selected variables reflects to have a statistical relationship with each other. For case in point, ice cream sales and crime rates is an appropriate example which presents clear trend that is crime rates get greater than before when there is an increase in the sales of ice cream (Crealock, 2008). These two variables are highly correlated with each other; however, we can easily see in this case that the correlation does not signify that due to the increase in the sales of ice cream causes increase in the crime rate or that crime is inspired by the consumption of ice cream by the individuals. The most important reasons for correlations are:

There could be an impenetrable effect involved that include the placebo effect in medical testing.

The variables that are ice cream sales and crime rates could be totally unrelated, or so slightly related as to make the relationship insignificant, as in the case of the of ice cream and crime.

There is a possibility of a third variable which can make its effect on both of incorporated variables that are ice cream sales and crime ...