Dear Editor, Prior to initiating interventional clinical studies in humans, researchers usually conduct in-silico (computer simulation) in vitro and in vivo studies in order to evaluate the initial efficacy, dose determination and toxicity, pharmacokinetics, and pharmacodynamics of the interventions. Accurate determination of Sample size in animal studies is essential both in terms of the validity of scientific inference and animal rights. Insufficient Sample size reduces the probability of detecting the relationship between variables. Furthermore, excessive Sample size imposes unnecessary costs and kills more animals (1). According to the paragraph 12 of the Helsinki Declaration the rights of laboratory animals in research need to be respected (2).

This review basically provided a conceptual framework for Sample size calculation in epidemiologic studies with various designs and outcomes. The formula requirement of Sample size was drawn based on statistical principles for both descriptive and comparative studies. The required Sample size was estimated and presented graphically with different effect sizes and power of statistical test at 95% confidence level. This would help the clinicians to decide and ascertain a suitable Sample size in research protocol in order to detect an effect of interest.

Introduction: One faced to matched data when the main object for a study is investigation of the effect of a given factor on an outcome, such that the Samples are matched on the some factors which are known as confounders. The response can be according to quantitative or qualitative scales. Matched case - control, before - after and crossover studies are used frequently in medicine. The significant tests are required to sufficient Samples. In this paper, formulas for determination of Sample size in matched studies are introduced by considering the scale of response and extended for matched case-control studies with multiple controls and illustrated using practical examples.Material & Methods: Using statistical techniques, formulas for determination of Sample size are introduced according to the scale of response.Results: The practical examples interpreted in order to clarify the formulas and their applications.Conclusion: One can increase the power and the accuracy of the statistical tests for matched studies, using the introduced formulas.

Background and Aim: One of the problems of the researchers at the start of the research is estimation of Sample size and selection of appropriate Sample of the population. A small Sample size can damage research credibility and lead to loss of money and waste of time. In contrast, a 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, 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.