Population sampling refers to the process through which a group of representative individuals is selected from a population for the purpose of statistical analysis. Performing population sampling correctly is extremely important, as errors can lead to invalid or misleading data. There are a number of techniques used in population sampling to ensure that the individuals can be used to generate data which can in turn be used to make generalizations about a larger population.
Statistical sampling is an important research tool for a number of disciplines, because it allows people to learn more about a population without studying every single individual in the population. However, because statistical sampling does not closely examine every individual, it is prone to errors. Therefore, many researchers devote a significant portion of their time to population sampling to ensure that it is done in a way which will stand up to scrutiny by other researchers and scientists.
The first step in population sampling is identifying the population which the researchers wishes to learn more about. If, for example, someone wants to find out how many African Americans have cats, the researchers knows that the population under scrutiny is the African American community. Population sampling is used to select representative individuals from this vast community so that an estimate about cat ownership among other members of this community can be extrapolated.
One of the most common population sampling techniques is random sampling, in which a researcher essentially draws names out of a hat. A scientist can also use cluster sampling, a technique in which a larger population is broken up into smaller clusters; several of these clusters are randomly selected for study. Another common technique is systematic sampling, in which a researcher picks every nth individual from the population that he or she is studying to gather information.
There are an assortment of other permutations of these sampling techniques which are used to collect data. Generally speaking, the larger the sample size, the better the resulting results will be. What most statisticians try to avoid is convenience sampling, in which a sample of readily accessible individuals is used, rather than a diverse sample of a wider population. An example of convenience sampling would be the placement of a stack of surveys at a single medical clinic, which might reveal information about the population which uses that medical clinic, but not necessarily a set a of results which could be more broadly interpreted.