Population sampling is the process of selecting representative individuals from a population for statistical analysis. It is important to ensure accuracy and avoid errors. Techniques include random, cluster, and systematic sampling. A diverse sample from a larger population is preferred over convenience sampling.
Population sampling refers to the process by 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 individuals can be used to generate data which in turn can 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 each individual in the population. However, because statistical sampling does not carefully examine every individual, it is prone to error. Therefore, many researchers spend a significant portion of their time sampling the population to ensure that it is done in a way that it stands up to scrutiny by other researchers and scientists.
The first step in population sampling is to identify the population that researchers want to know more about. If, for example, someone wants to find out how many African Americans own cats, the researchers know that the population under study is the African American community. Population sampling is used to select representative individuals from this large community so that an estimate of cat ownership can be extrapolated among other members of this community.
One of the most common population sampling techniques is random sampling, where a researcher essentially picks nouns out of a hat. A scientist may also use cluster sampling, a technique in which a larger population is broken up into smaller clusters; many of these clusters are randomly selected for study. Another common technique is systematic sampling, in which a researcher chooses one nth individual from the population he or she is studying to gather information.
There are an assortment of other permutations of these sampling techniques that are used to collect data. In general, the larger the sample size, the better the resulting results. What most statisticians try to avoid is convenience sampling, where a sample of easily accessible individuals is used, rather than a diverse sample of a larger population. An example of convenience sampling would be placing a stack of surveys at a single medical clinic, which might reveal information about the population using that medical clinic, but not necessarily a set of results that could be interpreted more broadly.
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