As discussed in Chapter 1, corpus representativeness depends on two sets of considerations: domain considerations and distribution considerations. Domain considerations focus on describing the arena of language use, and operationally specifying a set of texts that could potentially be included in the corpus. The linguistic research goal, which involves both a linguistic feature and a discourse domain of interest, forms the foundation of corpus representativeness. Representativeness cannot be designed for or evaluated outside of the context of a specific linguistic research goal. Linguistic parameter estimation is the use of corpus-based data to approximate quantitative information about linguistic distributions in the domain. Domain considerations focus on what should be included in a corpus, based on qualitative characteristics of the domain. Distribution considerations focus on how many texts should be included in a corpus, relative to the variation of the linguistic features of interest. Corpus representativeness is not a dichotomy (representative or not representative), but rather is a continuous construct. A corpus may be representative to a certain extent, in particular ways, and for particular purposes.

We propose that the representativeness of a corpus directly depends on its suitability for a specific research goal (including the domain and the linguistic feature(s) of interest). Creating a new corpus involves establishing linguistic research question(s), addressing domain considerations, including describing the domain, operationalizing the domain, evaluating the operational domain (relative to the full domain), designing the corpus, and evaluating the corpus (relative to the operational domain), addressing distribution considerations, including defining a linguistic variable and evaluating the required sample size, collecting the corpus, and documenting and reporting corpus design and representativeness. The steps for evaluating an existing corpus are similar: establishing linguistic research question(s), identifying and acquire the corpus and its documentation, addressing domain considerations, including describing the domain and evaluating the operational domain relative to the full domain, and the corpus relative to the operational domain, addressing distribution considerations, including defining a linguistic variable and evaluating the required sample size, and documenting and reporting corpus design and representativeness. We conclude the book by arguing that corpus representativeness is important for both corpus designers/builders, and corpus researchers who need to evaluate whether a corpus is appropriate for their research goals.