In this way, all eligible individuals have a chance of being chosen for the sample, and you will be more able to generalise the results from your study. In probability (random) sampling, you start with a complete sampling frame of all eligible individuals from which you select your sample. There are several different sampling techniques available, and they can be subdivided into two groups: probability sampling and non-probability sampling. For example, if the electoral roll for a town was used to identify participants, some people, such as the homeless, would not be registered and therefore excluded from the study by default.
This may involve specifically targeting hard to reach groups. If a sample is to be used, by whatever method it is chosen, it is important that the individuals selected are representative of the whole population. (Calculation of sample size is addressed in section 1B (statistics) of the DFPH syllabus.) Reducing the number of individuals in a study reduces the cost and workload, and may make it easier to obtain high quality information, but this has to be balanced against having a large enough sample size with enough power to detect a true association. Sampling is a method that allows researchers to infer information about a population based on results from a subset of the population, without having to investigate every individual. It would normally be impractical to study a whole population, for example when doing a questionnaire survey. We are currently in the process of updating this chapter and we appreciate your patience whilst this is being completed.
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