3.1 Probability sampling

In probability sampling methods, every sampling unit within the population has the same (or known) probability of being selected. They allow for representative sampling (such that results can be generalised to the target population) and limit selection bias (see the Fundamentals of data for AMR module).

Probability sampling methods include the following:

• Simple random sampling: Where a sampling frame is available and is used to randomly generate a list of units to be sampled. This can be done manually (such as ‘pulling numbers out of a hat’) or using a random number generator to compile a list of sampling units.
• Systematic random sampling: Where every nth unit is selected as each unit presents (or appears). For example, this could involve selecting every tenth fish that swims through a race. In public health, this could involve selecting every fifth patient presenting at a primary healthcare facility included in the sampling frame. Both the sampling interval (what value n takes) and starting point (whether systematic sampling starts from the first, second, third or nth unit) should be selected randomly.

Video 1 summarises simple random sampling and systematic random sampling.

There are important extensions to simple random sampling or systematic random sampling, to allow for probability sampling of primary, secondary and tertiary sampling units (as described in Section 1.2), as required. This includes the following:

• Multistage sampling: A random sample of primary units is selected, followed by a random sample of secondary units (and then tertiary, and so forth). For example, randomly selecting regions and then randomly selecting farms and then randomly selecting animals from each farm.
• Stratified random sampling: The source population is divided into mutually exclusive strata based on factors that may affect the outcome, such as geographic region. A known number of units are then randomly selected from each stratum.
• Cluster sampling: This is similar to multistage sampling, except that all sampling units are sampled in the final stage. That is, the farms are randomly selected, and then all animals on those farms are sampled.

These different sampling methods can be combined. For example, stratified sampling can be included within a multistage sampling design.

Video 2 summarises two of the more advanced approaches to probability sampling. Watch the video and then answer the question below.