The standard model for sampling has a *target* *population* about which inferences are to be made. The population consists of *sampling units, *the natural units that can be chosen to be part of a sample. The units might be people, households, businesses or geographical areas. In practice, perfect sampling is rarely achievable.

Frequently it is not possible to perfectly target the desired population – some may simply be inaccessible, or too expensive to make it practical to sample them. In such a case, it is common to talk of the *sample population*. Strictly speaking, inferences can then be made only about the sample population.

A sample is simply a chosen subset of the population, from which inferences will be drawn about the population. How that subset is chosen relates to the principles of sample design and selection.

These relationships are illustrated schematically in the accompanying figure to the right.

The researcher must typically use other information to consider whether inferences about the sample population are relevant to the target population. This is closely linked to the more general issue of representativity.

### Representativity of a sample

Since the purpose of a sample is to make inferences about the population, the sample must in some sense be *representative* of the population. That simply means that the sample must not be too special – the properties of the sample must at least approximate those of the population. It does not have to exactly match the properties of the population – weighting of the sample can assist with this – but it is important to consider just what your sample is representative of, and also what it is not.