| dc.description.abstract | This paper is aimed to compare the powers and significance levels of two well known
nonparametric tests: the Ansari-Bradley test and the Moses test in both situations where
the equal-median assumption is satisfied and where the equal-median assumption is
violated. R-code is used to generate the random data from several distributions: the
normal distribution, the exponential distribution, and the t-distribution with three
degrees of freedom. The power and significance level of each test was estimated for a
given situation based on 10,000 iterations. Situations with the equal samples of size 10, 20,
and 30, and unequal samples of size 10 and 20, 20 and 10, and 20 and 30 were considered
for a variety of different location parameter shifts. The study shows that when two
location parameters are equal, generally the Ansari-Bradley test is more powerful than
the Moses test regardless ofthe underlying distribution; when two location parameters
are different, the Moses is generally preferred. The study also shows that when the
underlying distribution is symmetric, the Moses test with large subset size k generally has
higher power than the test with smaller k; when the underlying distribution is not
symmetric, the Moses test with larger k is more powerful for relatively small sample sizes
and the Moses test with medium k has higher power for relatively large sample sizes. | en_US |
