  Societal accumulation of knowledge is a complex process. The correctness of
new units of knowledge depends not only on the correctness of new reasoning,
but also on the correctness of old units that the new one builds on. The errors
in such accumulation processes are often remedied by error correction and
detection heuristics.
  Motivating examples include the scientific process based on scientific
publications, and software development based on libraries of code.
  Natural processes that aim to keep errors under control, such as peer review
in scientific publications, and testing and debugging in software development,
would typically check existing pieces of knowledge -- both for the reasoning
that generated them and the previous facts they rely on. In this work, we
present a simple process that models such accumulation of knowledge and study
the persistence (or lack thereof) of errors.
  We consider a simple probabilistic model for the generation of new units of
knowledge based on the preferential attachment growth model, which additionally
allows for errors. Furthermore, the process includes checks aimed at catching
these errors. We investigate when effects of errors persist forever in the
system (with positive probability) and when they get rooted out completely by
the checking process.
  The two basic parameters associated with the checking process are the {\em
probability} of conducting a check and the depth of the check. We show that
errors are rooted out if checks are sufficiently frequent and sufficiently
deep. In contrast, shallow or infrequent checks are insufficient to root out
errors.
