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In statistics, when two or more variables have a statistical relationship, they are said to be correlated. For example, let’s consider the variables “sunscreen sales” and “ice cream sales”; both these variables experience an increase in value in the summers because people purchase more ice cream and sunscreen as the temperature rises. Therefore, both these variables have a statistical relationship as when there is a change in one, there is also a change in the other.
Causation goes beyond defining the extent of the relationship between variables and also defines the type of relationship between them. It implies that a change in one variable causes a change in the other, meaning that the two variables have a causal relationship. In a causal relationship, also referred to as a cause-and-effect relationship, there is an independent variable (the cause) and a dependent variable (the effect); a change in the independent variable influences, or causes, a change in the dependent variable.
For example, let’s consider “temperature” and “ice cream sales”. As the temperature rises, more people purchase ice cream; the change in temperature has caused a change in the sales of ice creams and, therefore, the two variables have a causal relationship. In this relationship, “temperature” is the independent variable (the cause), and “ice cream sales” is the dependent variable (the effect).
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Correlation implies a statistical association between variables. Scatterplots are often used to display variables on an xy-plane to determine the existence and type of correlation between the variables. If the pattern illustrated on the scatterplot can be approximated with a straight line, the correlation between the variables is ‘linear’. If not, the correlation is ‘non-linear’.
There are three main types of correlations:
Causation implies that there is a cause-and-effect relationship between the variables; a change in one causes a change in the other. It is important to keep in mind that correlation does not always imply causation and that a causal relationship can only be established using conclusive and reliable data.
The causal relationship between two variables can be proven using the following approaches:
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‘Correlation’ and ‘causation’ are two distinct concepts that must not be confused. Correlation simply indicates the existence of a statistical relationship, or association, between two variables; it does not say that one variable influences the other. Causation, on the other hand, does indicate that one variable influences the other and that a change in one ‘causes’ a change in the other.
Causation always implies correlation, but correlation does not imply causation. Let’s understand why.
There are two main reasons why correlation does not imply causation:
Correlation is a term used to describe the existence of a statistical association, or relationship, between two variables.
Correlation simply implies a statistical association between variables while causation implies that a change in one variable causes a change in the other.
Correlation does not imply causation for the following reasons;
There are three main types of correlation, namely;
Causation is a term used to describe cause-and-effect relationships that exist when a change in one variable can influence a change in the other.