2. Introduction to nonparametric tests Aa Aa Determine whether each of the follo

Question

2. Introduction to nonparametric tests Aa Aa Determine whether each of the following statements is true or false. Statement True False Parametric tests usually require more assumptions about the underlying population distribution O of the data on which they are used than nonparametric tests Most parametric tests require the use of nominal or ordinal data. Particularly when the data consist of numerical scores, it is easy to tell whether a parametric or OO nonparametric test is more appropriate. Why is the chi-square test for goodness of fit a nonparametric test? Check all that apply. It has no assumptions or restrictions. It does not require any assumptions about the shape of the population distribution. It does not use the normal distribution to determine a critical value. The test is used with nominal or ordinal data.

Explanation / Answer

Ans:

1)Non-parametric tests are “distribution-free”. They do not assume that the scores under analysis are drawn from a population distributed in a certain way, e.g., from a normally distributed population.(First statement is true)

2)Unlike parametric tests, there are non-parametric tests that may be applied appropriately to data measured in an ordinal scale, and others to data in a nominal or categorical scale.(second statement is false)

3)Nonparametric statistics refer to a statistical method wherein the data is not required to fit a normal distribution. Nonparametric statistics uses data that is often ordinal, meaning it does not rely on numbers, but rather a ranking or order of sorts.(third statement is true)

The chi- square test X2 test, for example, is a non-parametric technique. The significance of X2 depends only upon the degrees of freedom in the table; no assumption need be made as to form of distribution for the variables classified into the categories of the X2 table.

2nd and 4th options are correct.

It does not require any asssumptions about the shape of the population distribution.

The test is used with nominal or ordinal data.

Explanation:

Certain assumptions are associated with most non- parametric statistical tests, namely:

1. That the observations are independent;

2. The variable under study has underlying continuity;

3. Non-parametric procedures lest different hypothesis about population than do parametric procedures;

4. Unlike parametric tests, there are non-parametric tests that may be applied appropriately to data measured in an ordinal scale, and others to data in a nominal or categorical scale

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