ADVANTAGES OF PARAMETRIC AND NON-PARAMATRIC STATISTICS

Bradly has enumerated several advantages and disadvantages of parametric statistics and non-parametric statistics. The advantages of non-parametric over parametric can be postulated as follows:

1.   Similarity and facilitation in derivation- most of the non-parametric statistics can be derived by using simple computational formulas. This advantage does not lie with most of the parametric statistics. The derivation of which require an advanced knowledge of mathematics.

2.   Wider scope of application- non-parametric statistics as compared to parametric statistics are based upon fewer assumptions regarding the form of population distribution. They can be easily applied to much wider situations.

3.   Speed of application-when the sample size is small, calculation of non-parametric statistics is faster than parametric statistics.

The advantages of parametric statistics associated with them may be given as below:

1.   Non-parametric statistics have low statistics efficiency than parametric statistics, when sample size is large, preferably above 30.

2.   If all assumptions of parametric statistics are fulfilled the use of more non-parametric statistics are simply wasting of data(Seagull and Seagull,1988)

3.   It is also said that the probability tables for testing the significance of non-parametric statistics are widely scattered in different publications which for a behavioral scientists difficult to locate and interpret.