Sampling technique is the process or the method of drawing a definite number of the individuals, cases or the observations from a particular universe, selecting part of a total group for investigation. Examples of some common sampling techniques include Simple Random Sampling (SRS), Stratified Sampling, Cluster Sampling, Systematic Sampling, Multistage Sampling. Sampling techniques can also be defined as the method or the technique consisting of selection for the study of the so called part or the portion or the sample, with a view to draw conclusions or the solutions about the universe or the population.
In research, it is a sequence or other collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent
The role of sampling techniques in research
Sampling is necessary because we usually cannot gather data from the entire population due to large or inaccessible population or lack of resources. Even in relatively small populations, the data may be needed urgently and including everyone in the population in data collection may take too long.
In any study involving a large population, it is not possible for the researcher to evaluate the whole population by checking/inspecting each and every member of it hence he starts by taking a sample large enough to represent all the characteristics of the whole population under study. He applies the tests to the sample and based on the results of the tests, gives his recommendation about the whole population.
The following are some of the roles or sampling techniques in research:
- Sampling helps the researcher to meet up with the challenge of time
- To bring the population to a manageable number
- To reduce cost
- To help in minimizing error from the despondence due to large number in the population.
