Many tests for the analysis of continuous data have the underlying assumption that the data in question follows a normal distribution (ex. ANOVA, regression, etc.). Within certain research topics, it is common to end up with a dataset that has a disproportionately high number of zero-values but is otherwise relatively normal. These datasets are often referred to as ‘zero-inflated’ and their analysis can be challenging. An example of where these zero-inflated datasets arise is in plant science. We conducted a simulation study to compare the performance of zero-inflated models to a standard ANOVA model on different types of zero-inflated data. Underlying distributions, experimental design scenario, sample sizes, and percentages of zeros were variables of consideration. In this study, we conduct a Type I error assessment followed by a power comparison between the models.