**9.2 Simple random sampling**

A simple random sample means that each case in the population has the same probability of being included in the sample. This approach is the most straightforward of all probability sampling methods, because it includes only a single random sample and requires no specialized knowledge of the population. Since randomization is used, any research conducted on this sample should have a high internal and external validity.

Simple random sampling can be difficult to implement in practice. There are some prerequisites for using this method:


Simple random sampling works best if you have a lot of time and resources to carry out your study, or if you are studying a limited population that can be easily sampled (**Figure 2**).

## **9.3 Stratified sampling**

Stratified sampling is where the population is divided into sub-groups and a random sample is collected from each sub-group.

*Simple random sampling (source: https://www.datasciencemadesimple.com/simple-random-sampling-in-sas/).*

*Probability and Sampling in Dentistry DOI: http://dx.doi.org/10.5772/intechopen.97705*

(Stratified sampling formula)

Stratified random sampling ¼ Total Sample size*=*Entire population � Population of subgroups*:*

Mostly used when the population is heterogeneous and includes a variety of different classes, some of which are related to the subject of the analysis. The advantage of stratified sampling that it ensures a high degree of representativeness of all strata or strata in the population. Disadvantage is that it increases the work for planning and analysis for keeping the uncertainty within an acceptable level (**Figure 3**) [30].

#### **9.4 Cluster sampling**

Cluster sampling is where the entire population is divided into clusters or groups. Subsequently, a random sample of these clusters is taken, all of which are used in the final sample [31, 32].

It is simple and convenient, but the downside is that the members of the groups can be different from each other, decreasing the efficiency of the techniques (**Figure 4**).

#### **9.5 Systematic sampling**

Systematic sampling is the selection of a sample on an orderly basis. To build a sample, look at the target population and choose every fifth, tenth, or twentieth name, based on the size of the sample.

Systematic sampling can be used by statisticians if they want to save time or are disappointed with the results obtained from a simple random sampling process. If a fixed starting point has been established, the statisticians choose a constant interval to encourage the selection of the participant.

It ensures a high degree of representativeness, and no need to use a table of random numbers. Disadvantage is that it is Less random than simple random sampling (**Figure 5**) [33].

**Figure 4.** *Cluster sampling (source: https://research-methodology.net/sampling-in-primary-data-collection/cluster-sampling/).*

**Figure 5.**

*Systematic sampling (source: https://www.wallstreetmojo.com/systematic-sampling/).*
