We define a linguistic distribution as the range of values for a quantitative linguistic variable across the texts in a corpus. An accurate parameter estimate means that the measures based on the corpus are close to the actual values of a parameter in the domain. Precision refers to whether or not the corpus is large enough to reliably capture the distribution of a particular linguistic feature. Distribution considerations relate to the question of how many texts are needed. The answer will vary depending on the nature of the linguistic variable of interest. Linguistic variables can be categorized broadly as linguistic tokens (rates of occurrence for a feature) and linguistic types (the number of different items that occur). The distribution considerations for linguistic tokens and linguistic types are fundamentally different. Corpora can be “undersampled” or “oversampled” – neither of which is desirable. Statistical measures can be used to evaluate corpus size relative to research goals – one set of measures enables researchers to determine the required sample size for a new corpus, while another provides a means to determine precision for an existing corpus. The adage “bigger is better” aptly captures our best recommendation for studies of words and other linguistic types.

This chapter starts by first describing techniques to reduce errors. As far as the random ones are concerned, reduction approaches oriented to increase the signal-to-noise ratio on the spectrum domain and their strict relationship with sample averaging are discussed. Following, strategies for limitation of systematic errors are presented, especially based on the feedback concept. However, since the error reduction techniques allow several degrees of freedom, this chapter discusses the tradeoffs in optimizing sensing systems from the resolution, bandwidth, and power consumption point of view. More specifically, the resolution optimization of the sensing process is treated under the information theory point of view and the approach is extended to acquisition chains to understand the role of single building blocks.