The following are the chief points of merit that go in favour of the Karl Pearson’s method of correlation:
- This method not only indicates the presence, or absence of correlation between any two variables but also, determines the exact extent, or degree to which they are correlated.
- Under this method, we can also ascertain the direction of the correlation i.e. whether the correlation between the two variables is positive, or negative.
- This method enables us in estimating the value of a dependent variable with reference to a particular value of an independent variable through regression equations.
- This method has a lot of algebraic properties for which the calculation of co-efficient of correlation, and a host of other related factors viz. co-efficient of determination, are made easy.
Despite the above points of merits, this method also suffers from the following demerits:
- It is comparatively difficult to calculate as its computation involves intricate algebraic methods of calculations.
- It is very much affected by the values of the extreme items.
- It is based on a large number of assumptions viz. linear relationship, cause and effect relationship etc. which may not always hold good.
- It is very much likely to be misinterpreted particularly in case of homogeneous data.
- In comparison to the other methods, it takes much time to arrive at the results.
- It is subject to probable error which its propounder himself admits, and therefore, it is always advisable to compute it probable error while interpreting its results.