Part A. Questions about non-parametric procedures What are the most common reasons you would...

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Part A. Questions about non-parametric procedures
1. What are the most common reasons you would select a non-parametric test over the
parametric alternative?
When data is ranked or scores or measured on nominal scale of measurement, I
prefer non-parametric tests. The main advantage of this test is that they don’t assume
normality of population from which sample is drawn. Non parametric are
comprehensible, simple and easy to apply. They have great applications in Psychology,
sociology, and educational statistics.
2. Discuss the issue of statistical power in non-parametric tests (as compared to their
parametric counterparts). Which type tends to be more powerful? Why?
The statistical power of nonparametric tests is lower compared to parametric
tests.. In the non-parametric test, the value of p is very high, if the sample size is small
and the data is extracted from a population of Gauss. It takes time and money to get more
samples. If a larger sample size is used, then hypothesis test will prove weak.
3. For each of the following parametric tests, identify the appropriate non-parametric
counterpart:
1. Dependent t test:
Dependent t test
Non parametric Parametric
Wilcoxon Rank sum test Paired sample t test
Wilcoxon signed rank test one sample t test
2. Independent samples t test
Independent samples t test
Wald-Wolfowitz runs test
Mann-Whitney U test...

Robert · Accepted Answer

Part A. Questions about non-parametric procedures
1. What are the most common reasons you would select a non-parametric test over the 
parametric alternative? 
When data is ranked or scores or measured on nominal scale of measurement, I 
prefer non-parametric tests. The main advantage of this test is that they don’t assume 
normality of population from which sample is drawn. Non parametric are 
comprehensible, simple and easy to apply. They have great applications in Psychology, 
sociology, and educational statistics.
2. Discuss the issue of statistical power in non-parametric tests (as compared to their 
parametric counterparts). Which type tends to be more powerful? Why? 
The statistical power of nonparametric tests is lower compared to parametric 
tests.. In the non-parametric test, the value of p is very high, if the sample size is small 
and the data is extracted from a population of Gauss. It takes time and money to get more 
samples. If a larger sample size is used, then hypothesis test will prove weak.
3. For each of the following parametric tests,

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...

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