Difference between Correlation and Causation in Statistics:

The term “correlation” should not be confused with “causation.” If two variables have a correlation, it cannot be assumed that a change in one variable is the cause of a change in the other.

To put it the simple way, a change in one variable may be associated with a change in another variable, but this change does not have to be necessarily the cause of the other variable’s change.

When there is no cause and effect relationship between two variables but a correlation is found between the two variables such correlation is known as “spurious correlation” or “nonsense correlation”.

Correlation may exist as a result of the following factors:

• Pure change correlation:

Pure change correlation occurs with a small sample size. Correlation may exist between incomes and weights of four persons although there may be no cause and effect relationship between incomes and weights of people. This type of correlation may arise due to pure random sampling variation or because of the bias of the investigator in selecting the sample.

• When one or more variables influence the correlated variables:

A high degree of correlation between the variables may exist, where the same cause is affecting each variable or a different cause affecting each with the same effect.

For example, there is a degree of correlation between rice production per acre and tea yield per acre because both are related to rainfall, but neither of the two variables is the cause of the other.

• When the variable mutually influence each other so that neither can be called the cause of the other.

Because both variables may be responding to each other, it might be hard to say which is the cause and which is the effect.

(Source – Various books from the college library)

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