Distinguish Between Parametric and Nonparametric Statistics and Discuss When to Use Each Method in Analysis of Data

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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 be independent i.e. when values in one set are different and unrelated from another set
 Observations must be drawn from normally distributed populations
 The populations must have the same variances
 The sample must be random
Use of Parametric Statistics in Data Analysis:
Parametric tests are used when the above parametric assumptions are met.
Parametric tests are also used to analyze interval and ratio data. Interval data are numerical data in which we not only know the order but also the exact differences between the values e.g. the time interval between the starts of years 1981 and 1982 is the same as that between 1983 and 1984 which is 365 days. Ratio data on the other hand describe measurements with attributes that have the qualities of nominal, ordinal and interval data and a true zero point can be defined e.g. height and weight.
Examples of parametric tests include t-test, f test and z test. ii. Nonparametric Statistics:
Nonparametric statistics are used when the population from which the samples are selected is not normally distributed. These statistics are also known as distribution free statistics. Nonparametric statistics can also be used to test…...

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