Discuss issue of statistical power in non-parametric tests
Course:- Basic Statistics
Reference No.:- EM13779034

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Question: You will submit one Word document for this assignment, In the first part, you will provide short answers to the following questions.

Begin by downloading the following SPSS datasets:

. Activity 6a.sav (found on the "additional resources" page)

. Activity 6b.sav (file you created in Week 8)

. Activity 6c.sav (found on the "additional resources" page)

Part A. Questions about non-parametric procedures

What are the most common reasons you would select a non-parametric test over the parametric alternative?

Discuss the issue of statistical power in non-parametric tests (as compared to their parametric counterparts). Which type tends to be more powerful? Why?

For each of the following parametric tests, identify the appropriate non-parametric counterpart:

Dependent t test

Independent samples t test

Repeated measures ANOVA (one-variable)

One-way ANOVA (independent)

Pearson Correlation

Part B. SPSS Assignment

In this part of the assignment, you will perform the non-parametric version of the tests you used previously. In each case, assume that you opted to use the non-parametric equivalent rather than the parametric test. Using the data files from earlier activities, complete the following tests and paste your results into the assignment Word document:

Activity 6A: non-parametric version of the dependent t test

Activity 6B: non-parametric version of the independent t test

Activity 6C: non-parametric version of the single factor ANOVA


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Part A

In order to determine whether the researcher claim is significant or not, he need to perform a statistical test. Choosing an appropriate statistical test is necessary to support the researcher’s claim. The statistical tests can be classified into two categories, namely,
Parametric tests, and

Non Parametric tests

Parametric tests are tests which deal with parameters of the population like, mean, standard deviation, etc. If the parameters of the population are known, then, we can perform the parametric tests; else, we need to proceed with non parametric tests. In order to perform the parametric tests, we should validate the sample data using some specific tests, called, homogeneity, normality tests and independence. If all these assumptions are satisfied, then we can say that the distribution of the parent population follows normal and therefore, the sample data is highly reliable to perform parametric tests like t test, Z tests, F tests etc.

If these assumptions are violated, then it is inappropriate to perform the parametric tests. In these kind of situations, we need to perform non parametric tests. The non parametric tests are also called as distribution free test as it do not depend upon any parameters of the population. Non parametric tests are applicable to both variables and attributes, while parametric tests are applicable only to variables. For variables with nominal and ordinal data, there are no parametric tests available, but non parametric tests can be used for these kinds of data too.

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