Statistical sampling is the process by which a statistical sample is selected that is representative of the population and with which the estimated values of the parameters to be analyzed will be worked.

As the population or universe of study is usually very large, researchers*they limit* its analysis to a sample that is representative of the population. To do this, they select a sampling method and conduct an investigation through the sample that has been selected. Through the statistical inference On the sample, the researchers obtain information from the population they are studying.

The researcher, in addition to selecting the sample, must verify the following aspects:

- The right size for the sample and the level of confidence you want to establish
- The sampling frames that will be used in the investigation
- The estimators to be used
- The technique of selecting the elements of the investigation

## Main sampling techniques

Well, there are different sampling techniques that the researcher can use to create a statistics. The most common are those discussed below.

- Simple Random Sampling (MAS). It is a technique in which all elements of the population have exactly the same probability of being selected.
- Stratified Random Sampling (MAE). It is used when there are several separate groups (strata) and we want to know if the distribution between them is correct.
- Systematic Sampling (MS). The elements of the population are classified first and then choose one of them randomly, as well as the successive ones according to a systematic interval.
- Cluster Sampling (MPC). The elements of the sample are a conglomeration or a collection of units of study of analysis.

All of these we have mentioned are probabilistic techniques, although there are non-probabilistic ones, such as convenience or snowball sampling.