Abstract: Many works have been carried out to compare the
efficiency of several goodness of fit procedures for identifying
whether or not a particular distribution could adequately explain a
data set. In this paper a study is conducted to investigate the power
of several goodness of fit tests such as Kolmogorov Smirnov (KS),
Anderson-Darling(AD), Cramer- von- Mises (CV) and a proposed
modification of Kolmogorov-Smirnov goodness of fit test which
incorporates a variance stabilizing transformation (FKS). The
performances of these selected tests are studied under simple
random sampling (SRS) and Ranked Set Sampling (RSS). This
study shows that, in general, the Anderson-Darling (AD) test
performs better than other GOF tests. However, there are some
cases where the proposed test can perform as equally good as the
AD test.