Module 5 Survey Sampling Methods
by Patrick Boily; influenced by Patrick Farrell’s STAT 4502 course at Carleton University
Simply put, data analysis requires data. In pedagogical settings, we take for granted that the data at our disposal is “perfect” (or “ideal”): it either consists of the totality of potentially available data, or it is a representative subset thereof. In practice, either of these can be difficult to achieve; it can prove costly (and sometimes impractical) to collect data from which we can infer population trends and characteristics.
While web scraping (and automated methods) are sometimes used to facilitate the data collection process (see Module 17), the samples that they provide often fail to be representative enough to be of use in practice.
In this module, we discuss the principles that underlie statistical sampling methods, and show how to obtain estimates for various sampling plans.