  In the United States, the parties to a lawsuit are required to search through
their electronically stored information to find documents that are relevant to
the specific case and produce them to their opposing party. Negotiations over
the scope of these searches often reflect a fear that something will be missed
(Fear of Missing Out: FOMO). A Recall level of 80%, for example, means that 20%
of the relevant documents will be left unproduced. This paper makes the
argument that eDiscovery is the process of identifying responsive information,
not identifying documents. Documents are the carriers of the information; they
are not the direct targets of the process. A given document may contain one or
more topics or factoids and a factoid may appear in more than one document. The
coupon collector's problem, Heaps law, and other analyses provide ways to model
the problem of finding information from among documents. In eDiscovery,
however, the parties do not know how many factoids there might be in a
collection or their probabilities. This paper describes a simple model that
estimates the confidence that a fact will be omitted from the produced set (the
identified set), while being contained in the missed set. Two data sets are
then analyzed, a small set involving microaggressions and larger set involving
classification of web pages. Both show that it is possible to discover at least
one example of each available topic within a relatively small number of
documents, meaning the further effort will not return additional novel
information. The smaller data set is also used to investigate whether the
non-random order of searching for responsive documents commonly used in
eDiscovery (called continuous active learning) affects the distribution of
topics-it does not.
