  The goal of this paper is to provide a way for statisticians to answer the
question posed in the title of this article using any Bayesian hierarchical
model of their choosing and without imposing additional restrictive model
assumptions. We are motivated by the fact that the rise of ``big data'' has
created difficulties for statisticians to directly apply their methods to big
datasets. We introduce a ``data subset model'' to the popular ``data model,
process model, and parameter model'' framework used to summarize Bayesian
hierarchical models. The hyperparameters of the data subset model are specified
constructively in that they are chosen such that the implied size of the subset
satisfies pre-defined computational constraints. Thus, these hyperparameters
effectively calibrates the statistical model to the computer itself to obtain
predictions/estimations in a pre-specified amount of time. Several properties
of the data subset model are provided including: propriety, partial
sufficiency, and semi-parametric properties. Furthermore, we show that subsets
of normally distributed data are asymptotically partially sufficient under
reasonable constraints. Results from a simulated dataset will be presented
across different computers, to show the effect of the computer on the
statistical analysis. Additionally, we provide a joint spatial analysis of two
different environmental datasets.
