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Explain the similarities and differences between the Siegel-Tukey, Ansari-Bradley, and RMD
tests. In what situations would you use each test?
Solution
One solution to the ambiguous ranking mentioned above is use a rank of 1 for both the largest
and the smallest observations, a rank of 2 for the next smallest and largest, and so on. The
Ansari-Bradley test ranks the observations this way. We cannot, however, use the Wilcoxon rank
sum critical values if the ranks are assigned this way.
Suppose there are two groups A and B with n observations for the first group and m observations
for the second (so there are N = n + m total observations). If all N observations are arranged in
ascending order, it can be expected that the values of the two groups will be mixed or sorted
randomly, if there are no differences between the two groups (following the null hypothesis H0).
This would mean that among the ranks of extreme (high and low) scores, there would be similar
values from Group A and Group B.
If, say, Group A were more inclined to extreme values (the alternative hypothesis H1), then there
will be a higher proportion of observations from group A with low or high values, and a reduced
proportion of values at the center.

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Explain the similarities and differences between the Siegel-Tukey, A.pdf

  • 1. Explain the similarities and differences between the Siegel-Tukey, Ansari-Bradley, and RMD tests. In what situations would you use each test? Solution One solution to the ambiguous ranking mentioned above is use a rank of 1 for both the largest and the smallest observations, a rank of 2 for the next smallest and largest, and so on. The Ansari-Bradley test ranks the observations this way. We cannot, however, use the Wilcoxon rank sum critical values if the ranks are assigned this way. Suppose there are two groups A and B with n observations for the first group and m observations for the second (so there are N = n + m total observations). If all N observations are arranged in ascending order, it can be expected that the values of the two groups will be mixed or sorted randomly, if there are no differences between the two groups (following the null hypothesis H0). This would mean that among the ranks of extreme (high and low) scores, there would be similar values from Group A and Group B. If, say, Group A were more inclined to extreme values (the alternative hypothesis H1), then there will be a higher proportion of observations from group A with low or high values, and a reduced proportion of values at the center.