In probability sampling, each member of the population has a known, non-zero chance of being selected to a study. In nonprobability sampling, however, population members are selected using a non-random process; therefore, not all members of the population have a chance of being in the sample. As such, it is difficult to know if the sample reflects the distribution of the larger population, creating the issue of non-generalizability of research findings. Especially in epidemiologic studies, there are circumstances where probability sampling techniques may not be feasible. In such situations, inevitably nonprobability sampling methods are implemented which are cost- and time-efficient. In this study, we evaluate the bias and mean square error (MSE) of the sample mean obtained from convenience sampling and compare them with the ones obtained from simple random sampling using a simulation study under various scenarios. We show that although both bias and MSE of the sample mean from convenience sampling is much higher than that from simple random sampling for most of the scenarios; there are rare cases where relative efficiencies might achieve to one.