For many of the reasons discussed by Frick (1998), we have come to prefer
thinking of samples (and populations, when they exist) as outputs of processes.4 One
reason for this preference is that a process view better covers the range of statistical
situations in which we are interested, many of which have no real population (e.g.,
weighing an object repeatedly). Another reason for preferring the process view is
that when we begin thinking, for example, about how to draw samples, or why two
samples might differ, we typically focus on factors that play a role in producing the
data. That is, we think about the causal processes underlying the phenomena we are
studying. Biehler (1994) offered a similar analysis of the advantages of viewing data
as being produced by a probabilistic mechanism—a mechanism that could be altered
to produce predictable changes in the resultant distribution. Finally, viewing data as
output from a process highlights the reason that we are willing to view a collection
of individual values as in some sense “the same” and thus to reason about them as a
unity: We consider them as having been generated by the same process.