Introduction to sample and sampling in biomedicine

Abstract

Population is the largest statistical group and consists of all the studied subjects, placed in a particular time and place. Sample represents a portion of the population; express variability and the two samples can never be equal but only similar. It is not possible to use the entire population due to limited time for research, financial issues, and, most importantly, the real availability of subjects. Hence, scientists developed a research methodology based on a sample, part of the population, generated through the process of sampling. Sampling produces a sample, but a sampling error is also described in statistics by standard error. The most important sample feature is representativeness, achieved by adequate calculation of sample size and random selection of subjects from the population that differentiates two sample types: probabilistic, with random selection, and nonprobabilistic (not representative samples). Probabilistic sample types are simple random samples, stratified samples, systematic samples, and cluster samples. The sample size is the only factor that the researcher can control while planning the study. It directly affects the statistical power of the study, and therefore, sample size has to be calculated using the statistical methods described in this paper.