  In this paper, a new modification of ranked set sampling (RSS) is suggested,
namely; unified ranked set sampling (URSS) for estimating the population mean
and variance. The performance of the empirical mean and variance estimators
based on URSS are compared with their counterparts in ranked set sampling and
simple random sampling (SRS) via Monte Carlo simulation. Simulation results
indicate that the URSS estimators perform better than their counterparts using
RSS and SRS designs when the ranking is perfect. When the ranking is imperfect,
the URSS estimators still are superior than their counterparts in ranked set
sampling and simple random sampling methods. Finally, an illustrative example
is provided to show the efficiency of the new method in practice.
