Systematic sampling is probability sampling method where one chooses elements from a target population by selecting a random starting point and selecting other members after a fixed ‘sampling interval.’ Thereafter you calculate the sampling interval by dividing the entire population size by the desired sample size. Systematic sampling is an extended implementation of probability sampling in which each member of the group is selected at regular periods to form a sample. For example, in school, while selecting the headboy , teacher asks us to call out numbers such as 1-5 and the students with a random number decided by the teacher, for instance, three would be called out to be the headboy. It is a non-stressful selection process for both the teacher and the students. There’s an equal opportunity for every member of a population to be selected using this sampling technique.

Random sampling is collecting data from the total population. Under random sampling, each member of the subset carries an equal opportunity of being chosen as a part of the sampling process. For example, the total students in school is 1 000 and to conduct a survey, a sample group of 250 students is selected to do the survey. In this case, the population is the total number of students in the school and the sample group of 250 students is the sample. Each member has an equal opportunity of being chosen because all them which were chosen to be part of the survey were selected randomly. But, there is always a possibility that the group or the sample does not represent the population as a whole, in that case, any random variation is termed as a sampling error.

Cluster sampling is a sampling method in which the entire population of the study is divided into externally homogeneous, but internally heterogeneous, groups called clusters. Essentially, each cluster is a mini-representation of the entire population. This method of sampling requires few resources and it is feasible. However it has a lot of bias and high sampling error.