INTRODUCTION TO SAMPLE AND SAMPLING IN BIOMEDICINE

In scientific research, the population is the largest statisti-cal group and consists of all the studied subjects placed in a particular time and place. Subjects 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 (random selection) and nonprobabilistic (not representative samples). Probabilistic sample types include simple random samples, stratified samples, systematic samples, and cluster samples, while nonprobabilistic samples include convenience samples and quota samples. The sample size is the only factor that the researcher can control while planning the study. However, for a sample to be representative, it is necessary to define inclusion and exclusion criteria to ensure that the respondents included in the sample have characteristics that affect the association being researched. 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.