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DISTINGUISH BETWEEN PARAMETRIC AND NONPARAMETRIC STATISTICS AND DISCUSS WHEN TO USE EACH METHOD IN ANALYSIS OF DATA

The word parametric comes from “metric” meaning to measure, and “para” meaning beside or closely related. The combined term refers to the assumptions about the population from which the measurements were obtained.

The two classes of statistical tests are:

Parametric Statistics

Nonparametric Statistics

i. Parametric Statistics:

Parametric statistics are statistical tests for population parameters such as means, variances and proportions that involve assumptions about the populations from which the samples were selected. These assumptions include:

Observations must ...view middle of the document...

Use of Nonparametric Statistics in Data Analysis:

Nonparametric tests are used when assumptions of nonparametric tests cannot be met, when very small numbers of data are used and when no basis exists for assuming certain types or shapes of distribution.

They can also be used for nominal and ordinal levels of measurement. Nominal data is a set of data in which values or observations belonging to it can be assigned a code or a label, e.g. In a data set, males could be coded as 0, females as1; marital status of an individual could be coded as ‘Y’ if married and ‘N’ if single. Ordinal data on the other hand is a set of data in which the values or observations belonging to it can be ranked or have a rating scale attached e.g. a set of survey answers can be listed as very satisfactory, satisfactory, neutral, unsatisfactory, very unsatisfactory. As a result, nonparametric tests can be used if data can only be classified, counted or ordered.

Examples of nonparametric tests include Wilcoxon Mann-Whitney Test, Sign Test and Ruskal-Wallis Test.

Advantages of nonparametric statistics:

Nonparametric tests are simple and easy to understand

They are designed for small numbers of data

Nonparametric statistics do not involve complicated sampling theories

No assumption is made regarding the parent population

Nonparametric statistics can be used when data is nominal or ordinal

They can be used to test population parameters when the variable is not normally distributed

They can be used effectively for determining...

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