  Preprocessing forms an oft-neglected foundation for a wide range of
statistical and scientific analyses. However, it is rife with subtleties and
pitfalls. Decisions made in preprocessing constrain all later analyses and are
typically irreversible. Hence, data analysis becomes a collaborative endeavor
by all parties involved in data collection, preprocessing and curation, and
downstream inference. Even if each party has done its best given the
information and resources available to them, the final result may still fall
short of the best possible in the traditional single-phase inference framework.
This is particularly relevant as we enter the era of "big data". The
technologies driving this data explosion are subject to complex new forms of
measurement error. Simultaneously, we are accumulating increasingly massive
databases of scientific analyses. As a result, preprocessing has become more
vital (and potentially more dangerous) than ever before.
