The characteristics of samples collected from
a lot are used to make estimates of the characteristics of that lot. Thus,
samples are used to infer properties about the lot in order to make correct
decisions concerning that lot. Therefore, for sampling to be meaningful, it
is imperative that a sample is as representative as possible of the lot, and
more generally, each subsample must be as representative as possible of the
parent sample from which it is derived. Subsampling errors propagate down
the chain from the largest primary sample to the smallest laboratory analytical
subsample. If a collection of samples does not represent the population from
which they are drawn, then the statistical analyses of the generated data
may lead to misinformed conclusions and perhaps costly decisions.
