Ranked set sampling (RSS) is a method of sampling that provides a more precise estimator of population mean than simple random sampling (SRS) when actual measurements are either difficult, time consuming or expensive in terms of time, money or labour and ranking on the basis of visual inspection or any other rough method, not requiring actual measurement, is relatively easy. Ozturk et al.(2018) developed a model based statistical inference for population mean and total based on RSSsamples in a finite population setting where samples are constructed by using a without replacement sampling design. They showed that the sample mean of RSS is model unbiased and has smaller mean square prediction error (MSPE) than the MSPE of a simple random sample mean. A small scale simulation study showed that estimators are as good as or better than SRS estimators when the quality of ranking information in RSS sampling is low or high, respectively, and the cost ratio of obtaining a unit in RSS and a unit in SRS is not too high.