Sampling is about the choice of cases to include in the evaluation, the aim of which is that the cases should be as representative as possible of the wider group of cases being sampled (the population). This will give generalisability, i.e. the ability to generalise the results to a wider population. Relevant factors are the size of the sample and the means by which it is chosen.

It is best to assess the required sample in terms of the sort of analysis to be undertaken with the data. Statistical analyses with lots of sub-categories will require larger samples so that reasonable numbers occur in each sub-category; for example, a two way analysis of race and sex will give six sub-categories if using categories of male/female and Asian/black/white. We know that some of these sub-groups are likely to be very small, e.g. black females. If they made up 2% of the population being studied the chances are that a sample of 50 would produce just one, and may not include any in this sub-category. Stratified sampling would ensure adequate numbers in minority groups, but be more complex to organise and may not be feasible in relation to naturally occurring groups. The more variability anticipated on key factors within a population, the larger the sample needed.