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General notions of sampling applied to health sciences

Abstract:

The purpose of the paper is to describe sampling general notions applied to health sciences, analyzing definitions, formulas, procedures and data. In probabilistic studies, the sample determine the target population through a sampling plan. In all statistical process, the primary, secondary and tertiary sampling units are important. Likewise, the sampling type may involve techniques as simple random sampling, systematic sampling, stratified random sampling or cluster sampling. In order to develop a good sampling plan, the following criteria has to be clearly set: population size, confidence level, relative error, design effect, adjusted design effect, parameters, estimated variance and non-response rate. The achievement of these goals may reduce costs and time, besides to allow the inference of results and findings from the sample to population. The calculation of estimators and the inference made on parameters, require an expert in statistics or mathematics, with knowledge and abilities in recollecting, processing, analyzing and interpreting the information gathered. This features allow the researcher to gain a greater reliability and validity of the investigation results.