Describe sampling theory and provide examples to illustrate your definition

Sampling Theory

Sampling is the process of selecting a group of individuals, events, habits, or other items to investigate. It is a technique for determining the most efficient way to obtain a sample that accurately reflects the population under investigation. The process of selecting samples is defined by a sampling plan. A population is a group of items that have a similar trait or traits which can be used for study, whereas a sample is a subset of the population that is chosen for research purposes.

Describe sampling theory and provide examples to illustrate your definition

The analysis of the association between the population and the subset taken from the population for the investigation is known as sampling theory. It is a type of data analysis in which samples are obtained from the population to determine the distributional data. It is applied to samples taken at random from the target population. It is also a research project that involves data collection, sampling, and analysis.

The implementation of sampling theory is associated with the appropriate choice of findings from the population which will make up the representative sample. It encompasses the use of probability theory, as well as prior knowledge of the population parameters, to interpret the data from a random sample and form an opinion from the analysis (Peregrine, 2021).

Sampling theory aids in the estimation of unknown population parameters using statistical measures derived from sample research. In other words, the basic goal of sampling theory is to acquire a parameter estimate using statistics. It also aids in forming broad generalizations about a population based on samples obtained from it and assessing the precision of such generalizations.

Take, for example, the sampling theory, which involves examining each disease that has been detected in a certain area affecting children. If the geographical area is large, only a small number of children from the entire population of children in that area will be studied. The researcher will take samples are taken at random from that population, considering factors like sex, age, living conditions, health status and age, and then analyze based on the research objectives. Ideally, movement in and out of that area should be considered before picking that specific area. The researcher will then use the results from the sample to generalize on the entire population of the said study area (total number of children).

A study’s generalizability, on the other hand, is a measure of how useful the findings are to a larger group of people or settings. If the findings of a study can be generalized to a wide range of persons or situations, it is said to have good generalizability. If the results can only be applied to a small group or in extremely specific situations, they have weak generalizability.

Concerns if the results of a single study can be transferred to initially unstudied issues and circumstances. Participants, locations, measurements, and experimental treatments are all examples of generalization in a research design. As Osbeck and Antczak (2021) note, result must provide the same results with multiple types of measurement and in different settings to be considered generalizable.

In the above example, the results from the sample of children are viewed as the result of the general children population. This shows the generalization of results. Generalizability requires determining whether a study’s results are applicable to a specific group. Content validity is also considered. The adoption of appropriate sampling methodologies, sample sizes, characteristics of the sample, study settings and processes will aid in the generalizability of such investigations.


Peregrine, P.N. (2021). Sampling Theory. In The Encyclopedia of Archaeological Sciences, S.L. López Varela (Ed.).

Osbeck, L. M., & Antczak, S. L. (2021). Generalizability and qualitative research: A new look at an ongoing controversy. Qualitative Psychology, 8(1), 62–68.