Mixed models are ideally suited to analyzing nested data from within-persons designs, designs that are advantageous in applied research. Mixed models have the advantage of enabling modeling of random effects, facilitating an accounting of the intra-person variation captured by multiple observations of the same participants and suggesting further lines of control to the researcher. However, the sampling requirements for mixed models are prohibitive to other areas which could greatly benefit from them. This simulation study examines the impact of small sample sizes in both levels of the model on the fixed effect bias, type I error, and power of a simple mixed model analysis. Despite the need for adjustments to control for type I error inflation, findings indicate that smaller samples than previously recognized can be used for mixed models under certain conditions prevalent in applied research. Examination of the marginal benefit of increases in sample subject and observation size provides applied researchers with guidance for developing mixed-model repeated measures designs that maximize power.