Hierarchical models are statistical log-linear models that record the dependency relations among random variables in statistics. Diaconis and Sturmfels in ’98 propose using ideals to study these models. I will start by introducing hierarchical models and their ideals. In this talk we’ll show that studying an entire family of ideals simultaneously can lead to greater insight than studying them individually. No knowledge in statistics or advanced algebra is assumed.