3.6 Summary

In this chapter, we have presented one of the most important concepts to learn from this book: hierarchical models. We can build hierarchical models every time we can identify subgroups in our data. In such cases, instead of treating the subgroups as separate entities or ignoring the subgroups and treating them as a single group, we can build a model to partially pool information among groups. The main effect of this partial pooling is that the estimates of each subgroup will be biased by the estimates of the rest of the subgroups. This effect is known as shrinkage and, in general, is a very useful trick that helps to improve inferences by making them more conservative (as each subgroup informs the others by pulling estimates toward it) and more informative. We get estimates at the subgroup level and the group level.

Paraphrasing the Zen of Python, we can certainly say hierarchical models are one honking great idea, let’s do more of those! In the following chapters...