Sampling Theory and Generalizability

Sampling Theory and Generalizability
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Definition of the sampling theory
Sampling theory refers to a branch of statistics that entails the collection and analysis of data sets gathered from random samples of the population under study. Sampling theory is applied in statistics to ensure that the sample data collected from a population effectively reflects the characteristics of the entire population. The main concepts applied in the sampling theory include the sample, sampling criteria, sampling methods, population characteristics, and sampling plans (Spiegel & Stephens, 2017). The sampling method is applied in obtaining a representative portion of the population under study. As a result, the sampling plan should be designed to ensure optimal representativeness of the entire population while ensuring minimal systematic bias. The applicable sampling plan may apply either random sampling, probabilistic sampling, or non-random sampling methods. The speculative basis of the sampling theory is best applied in a normally distributed population. However, other probability distributions are also applied to form the speculative basis of the sampling theory.
Example
If we want to investigate and formulate hypothesis concerning the ratio of faulty bottles manufactured in a plan within a period of 4 weeks, we can do so by randomly inspecting 20 bottles at different intervals within the course of the day.
Generalizability to Nursing Research
Generalizability of the study results is determined by the ability of a researcher to differentiate the relevant facts from the unnecessary details.

This enables the researcher to present unbiased and relevant results. Therefore, it’s easier to generalize the findings of a human study to represent animal characteristics. This generalizability is applicable on the condition that there exists a mutual biological procedure or functional mechanisms of a disease are relevant (Spiegel & Stephens, 2017). This implies that the animal species is not important. Therefore, generalization is the applicability of study results as long as they have the internal validity.
References
Spiegel, M. R., & Stephens, L. J. (2017). Schaum’s outline of statistics. McGraw Hill Professional.