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Biostatisticians use a multiplicity of statistical terms and concepts that help them to organize numerical information in various formats, understand statistical techniques, and make informed decisions. These terms are important in helping professionals to not only design, analyze and interpret data of studies in public health and medicine, but also to make conclusions about the epidemiology of disease and health risks through the assessment and application of mathematical models to the factors that impact health (Rosner, 2010). This paper offers a technical and lay definition of a statistical term known as “sample” in order to develop an adequate understanding of the concept and its principles and applications.
The term “sample” is technically defined as “a subset of all the units of analysis which make up the population” (Watt & Berg, 2002, p. 121). This basically means that a sample is a smaller representation of the whole population as the units that comprise a sample are taken from the larger population. For example, a researcher who is interested in evaluating the incidence of dental caries in a community of 2000 residents may decide to use 200 residents only due to factors such as time constraints, financial limitations, and inability to contact all the residents to take part in the study. These 200 residents selected serve as the sample of the study because they represent the larger community. In the common man’s language, therefore, the term “sample” can be described as the units or individuals selected by the researcher for inclusion into the study based on their uniqueness in representing the characteristics of a particular population.
The sample selected must be able to represent the population, meaning that it should contain all the characteristics of the population to enable the researcher to draw valid conclusions or inferences about the population of interest to the study (Watt & Berg, 2002). If the sample is not representative in the example of dental caries described above, the researcher may end making wrong conclusions on the factors that cause the community to experience a high incidence of dental caries. It is therefore important for biostatisticians to evaluate the sample distribution, defined in the literature as “a statement of the frequency with which the units of analysis or cases that together make up a sample are actually observed in the various classes or categories that make up a variable” (Watt & Berg, 2002, p. 121). An assessment of the sample distribution will enable the researcher to make an informed decision on whether the selected sample can be used as a valid representation of the population.
Although there are many statistical techniques that can be used to select a sample from the population, the researcher must always ensure that the technique used has the capacity to provide a valid and representative sample (Rosner, 2010). This means that a sample can be limited by the techniques and strategies used to select cases or units for participation in a study. Researchers who use the simple random sampling technique to select participants for the dental caries study, for example, may end up having a more representative sample than those who use convenience sampling as the former technique ensures that all community members have an equal chance or probability for selection. Lastly, it is important to deal with issues of sampling error and sample confidence level to ensure that the inferences or conclusions drawn from the sample will portray the true picture on the ground.
References
Rosner, B. (2010). Fundamentals of biostatistics (7th ed.). Boston, MA: Cengage Learning.
Watt, J.H., & Berg, S.V.D. (2002). Research methods for communication science (2nd ed.). Boston, MA: Allyn & Bacon.
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