It has become quite common nowadays to perform multiple tests simultaneously in order to detect differences of a certain trait among groups. This often leads to an inflated probability of at least one Type I Error, a rejection of a null hypothesis when it is in fact true. This inflation generally leads to a loss of power of the test especially in multiple testing and multiple comparisons. The aim of the research is to use simulation to address what a researcher should do to determine which treatments are significantly different from the control when there is a large number of treatments and the number of replicates in each treatment is small. We examine two situations in this simulation study: when the number of replicates per treatment is 3 and also when it is 5 and in each of these situations, we simulated from a normal distribution and in mixture of normal distributions. The total number of simulated treatments was progressively increased from 50 to 100 then 150 and finally 300. The goal is to measure the change in the performances of the False Discovery Rate method and Dunnett’s test in terms of type I error and power as the total number of treatments increases. We reported two ways of examining type I error and power: first, we look at the performances of the two tests in relation to all other comparisons in our simulation study, and secondly per simulated sample. In the first assessment, the False Discovery Rate method appears to have a higher power while keeping its type I error in the same neighborhood as Dunnett’s test and in the latter, both tests have similar powers and the False Discovery Rate method has a higher type I error. Overall, the results show that when the objective of the researcher is to detect as many of the differences as possible, then FDR method is preferred. However if error is more detrimental to the outcomes of the research, Dunnett’s test offers a better alternative.