Introduction
This topic explains how investigators choose the people they study and why the way they choose them decides whether results can be trusted. It covers the difference between a population and a sample, what makes a sample representative, the two kinds of error that threaten every study, and the main sampling techniques used in medical and public health research.
Population and sample
The population is the whole group about which conclusions are wanted, defined by clear inclusion and exclusion criteria, for example all adults aged 18 to 65 registered with a general practitioner in a given region. The target population is the ideal group; the accessible population is the part that can actually be reached. The sample is the subset actually studied, and its results are used to estimate population parameters such as a mean or a proportion.
- Sampling frame: the list or source from which the sample is drawn.
- Sampling unit: the element selected, which may be an individual, a household, a practice or a school.
- Sampling error: the difference between a sample estimate and the true population value that arises simply because only part of the population is studied.