Sampling techniques refer to methods used to select members of a population to be studied in order to draw inferences about the population as a whole. Broadly speaking, there are two main types of sampling techniques: probability sampling and non-probability sampling.
Probability sampling techniques draw samples from a given population using certain principles of probability. This is the most reliable method since it eliminates the effects of sampling bias and produces results that are more representative of the population. Examples of probability sampling methods include random sampling, systematic sampling, stratified sampling, and cluster sampling.
Non-probability sampling techniques do not use probability principles and often do not result in a representative sample. Examples of non-probability sampling methods include convenience sampling, snow-ball sampling, and judgment sampling.
Sampling techniques are commonly used in the field of computer science. Computer scientists often use these techniques to collect data for research studies. They are also employed in the construction of software and hardware systems. For instance, they are used to determine which algorithms and data structures are most suitable for specific applications.
The choice of sampling technique will depend on the characteristics of the population, the objectives of the study, and the available resources. It is important to select an appropriate technique in order to obtain reliable and meaningful results.
