There are two branches in statistics, descriptive and inferential statistics. Of these two main branches, statistical sampling concerns itself primarily with inferential statistics. This involves determining values of population parameters on the basis of a statistical sample. There are a variety of different types of samples in statistics. Each of these samples is named based upon how its members are obtained from the population. Below is a list with brief description of some of the most common statistical samples.

### List of Sample Types

- Random sample – Here every member of the population is equally likely to be a member of the sample.
- Simple random sample – Not only is this a random sample, but every group of
*n*is equally likely to be the sample. - Voluntary response sample – Here subjects from the population determine whether they will be members of the sample or not.
- Convenience sample - This type of sample is characterized by the selection of easy to obtain members from the population.
- Systematic sample - A systematic sample is chosen on the basis of an ordered system.
- Cluster sample – A cluster sample involves using a simple random sample of evident groups that the population contains.
- Stratified sample - A stratified sample results when a population is split into at least two non-overlapping subpopulations.

It is important to know the distinctions between the different types of samples. For example, a simple random sample and a systematic random sample can be quite different from one another. Some of these samples are more useful than others in statistics. A convenience sample and voluntary response sample can be easy to perform, but these types of samples are not randomized to reduce or eliminate bias.

It is also good to have a working knowledge of all of these kinds of samples. Some situations call for something other than a simple random sample. We must be prepared to recognize these situations.