Sampling and Data Collection

Why sampling matters in the real world

Whenever an organization wants to understand a large group — every voter in a country, every battery leaving a factory, every tree in a forest — it is rarely possible to measure all of them. The complete group of interest is called the population, and the smaller collection we actually measure is the sample. Good applied statistics lives or dies on how well the sample represents the population. A brilliant analysis of a biased sample still produces misleading conclusions, so a data scientist begins by asking, "Where did these numbers come from?"

Consider Riverside Coffee, a chain that wants to estimate the average daily spend of its customers. The population is every customer visit in a year — perhaps 400,000 visits. Measuring all of them is impractic