Question

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1. Name TWO (2) inadequacies in the dataset that might cause you to prefer a nonparametric test over parametric test to analyze the data.

2. Write a relevant study objective in the field of finance or economics which Wilcoxon Signed-Rank test will be the most appropriate test to solve the problem. Brief the characteristics of the data which make the test is the most appropriate for the analysis.

Answer 1

Answer: 1) For using any test for testing hypothesis, we have to check assumptions. Most of the times, if following assumptions are satisfied then parametric test is used.

a) Data have normal distribution. b)  Data from multiple groups have the same variance. c) Data points are independent of each other. d) Sample size should be greater than 30 (or nearby 30).

If data fails to satisfy above assumptions, then non-parametric tests are performed.

2) The Wilcoxon signed rank test (also called the Wilcoxon signed rank sum test) is a non-parametric test. When the word “non-parametric” is used in stats, it doesn’t quite mean that you know nothing about the population.The Wilcoxon signed rank test should be used if the differences between pairs of data are non-normally distributed.

Use in finance or economics: Wilcoxon sign rank test is alternative to Dependent sample t test. Example: If bank manager wants to know "Is there any increase or decrease in average yield of farmers after providing loans to farmers?"

For testing above hypothesis, we have to use "Dependent sample t test". But, if assumptions for test not satisfied by data then we have to use "Wilcoxon sign rank sum test".

If we want to compare change between before and after some condition, the in statistics there is only one parametric method known as "Dependent sample t test". If assumptions are not satisfied then we have to use Wilcoxon sign rank sum test to test above hypothesis.