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 RESEARCH METHDOLOGY
Year : 2004  |  Volume : 70  |  Issue : 2  |  Page : 123--128

Sample size and power analysis in medical research


Clinical Epidemiology Unit, Department of Preventive and Social Medicine, Government Medical College, Nagpur, Maharashtra, India

Correspondence Address:
Sanjay P Zodpey
A/303, Amar Enclave, Prashant Nagar, Ajni, Nagpur - 440015
India
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Source of Support: None, Conflict of Interest: None


PMID: 17642587

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Among the questions that a researcher should ask when planning a study is "How large a sample do I need?" If the sample size is too small, even a well conducted study may fail to answer its research question, may fail to detect important effects or associations, or may estimate those effects or associations too imprecisely. Similarly, if the sample size is too large, the study will be more difficult and costly, and may even lead to a loss in accuracy. Hence, optimum sample size is an essential component of any research. When the estimated sample size can not be included in a study, post-hoc power analysis should be carried out. Approaches for estimating sample size and performing power analysis depend primarily on the study design and the main outcome measure of the study. There are distinct approaches for calculating sample size for different study designs and different outcome measures. Additionally, there are also different procedures for calculating sample size for two approaches of drawing statistical inference from the study results, i.e. confidence interval approach and test of significance approach. This article describes some commonly used terms, which need to be specified for a formal sample size calculation. Examples for four procedures (use of formulae, readymade tables, nomograms, and computer software), which are conventionally used for calculating sample size, are also given






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Online since 15th March '04
Published by Wolters Kluwer - Medknow