In this paper we consider the finite-sample properties of Realized Range estimators of integrated variance. We show that the combination of irregular sampling and missing observations induces a bias in the Realized Range measures. We propose a simple correction in order to reduce this bias. A Monte Carlo experiment compares the range-based estimators of integrated variance with popular realized variance estimators. Simulated data are obtained from different generating mechanisms for the instantaneous volatility process, e.g. Ornstein-Uhlenbeck, long memory and jump processes. We also evaluate the robustness of the different approaches considered when high-frequency prices are affected by bid-ask bounce and price discreteness. Simulation results confirm that realized range corrected for irregular sampling has lower bias while not increasing the estimator variance. A brief empirical application with high-frequency IBM data is also included.