Nonrepresentative data are bad for out-of-sample generalizations,
but can be quite useful for within-sample comparisons.
In my experience, government data tends to be less Nonrepresentative, less algorithmically confounded, and less drifting.
Additional human error-- including poor facilitator training,
recruitment of Nonrepresentative samples, and rushed interpretation of results--
create the potential for ambiguous or unusable research results.
Rothman, Gallacher, and Hatch(2013), which has the provocative title“Why representativeness should be avoided,” make a more
general argument for the value of intentionally creating Nonrepresentative data.
As Snow's work illustrates, there are some scientific questions for which Nonrepresentative data can be quite effective
and there are others for which it is not well suited.
One thing that is clear, however,
is that if you are forced to work with non-probability samples or Nonrepresentative big data sources(think back to Chapter 2),
then there is a strong reason to believe that estimates made using post-stratification and related techniques will be better than unadjusted, raw estimates.