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Background and Aim: One of the problems of the researchers at the start of the research is the estimation of sample size and selection of an appropriate sample of the population. Small sample size can damage research credibility and lead to loss of money and waste of time. In contrast, large sample size can lead to increased workload, cost, and lack of proper feedback. The purpose of this study was to evaluate the sample size in various medical studies.
Materials and Methods: In this study, after determining the types of studies, various sources (books, journals, articles, etc.) were searched and different sampling methods based on the type of variable, were determined and classified on the basis of the corresponding formulas. Among the different methods, we extracted the best formulas and methods on the basis of the type of study and other important and confounding factors.
Result: Various studies have shown that several factors affect sample size including effect size, power of the test, significance level, the maximum error of measurement, ratio, and variation of the trait. In this study, it was shown that in regression models, the effect of the correlation coefficient between variables should be controlled and the sample size should be estimated by considering and balancing the generated dispersion amount. Also, the size of the effect of the most important indicator and the test power is strongly influenced by the sample size.
Conclusion: In most similar studies, it has been shown that for estimation of the effective and valid sample size, the error rate should be low and the test power should be high, and also the effect values, level of significance and dispersion, and the correlation between the variables should be controlled.