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Sampling theory is the study of how different populations relate to samples drawn from them. For sampling theory to apply, the samples have to be randomized to ensure they are representative of the population and that no bias is present. Sampling theory involves itself with the estimation of population properties from those of a sample and how accurate the estimate is. For instance, a problem in sampling theory could be the use of statistical methods such as binomial and normal distributions to determine how the prevalence of birth complications among obese patients from the randomized study relates to the actual rate of prevalence of birth complications among all obese patients. This estimation is essential as it determines how well the results of a study can be applied to the population. This application of results obtained from a sample to the entire population is known as generalization. It is a vital requirement of studies that aim to be part of best practice. Only generalizable studies can be applied to patients of nursing, as these have the highest threshold of safety (McNiff & Petrick, 2018).