SL 4.1—Sampling and data

Sampling asks who was observed and how they were chosen.

A sample can describe a population only when the selection method limits systematic bias; random, stratified and quota designs answer different practical constraints.

Example

To estimate a school’s mean travel time, sample students from each year group rather than surveying only the nearest classroom.

Name the population, sampling frame and possible bias before calculating a statistic.

A large convenience sample can still be biased; sample size does not repair a distorted frame.

Boundary checklist: a population is the full group of interest and a sample is the observed subset; discrete data take countable values while continuous data can vary across an interval. An outlier lies more than $1.5\times IQR$ below $Q_1$ or above $Q_3$, but context decides whether it is valid or an error. Distinguish simple random, convenience, systematic, quota and stratified sampling, and inspect missing data and the sampling frame before generalising.