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Sample Size Determination

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International Encyclopedia of Statistical Science
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A common problem arising in statistics is to determine the smallest sample size needed to achieve a specified inference goal. Examples of inference goals include finding a 95% confidence interval for a given statistic of width no larger than a specified amount, or performing a hypothesis test at the 5% significance level with power no smaller than a specified amount. These examples and others are discussed more fully below.

Sample Size to Achieve a Given Variance or Relative Variance

One may want to estimate a parameter θ by an estimator \(\hat{\theta }\) based on a sample of size n. Often the variance of \(\hat{\theta },\) \(\mbox{ var}(\hat{\theta }),\) will have the form \(\mbox{ var}(\hat{\theta }) = b/n\) for some known constant b. To achieve a variance of \(\hat{\theta }\) no larger than a specified amount A, one simply sets A = b ∕ n and solves for n: n = b ∕ A. The value of n must be an integer, so one takes n to be the smallest integer no smaller than b ∕ A. Note that nis...

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References and Further Reading

  • Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Erlbaum, Hillsdale

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  • Cohen MP (2005) Sample size considerations for multilevel surveys. Int Stat Rev 73:279–287

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  • Dattalo P (2008) Determining sample size. Oxford University Press, New York

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© 2011 Springer-Verlag Berlin Heidelberg

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Cohen, M.P. (2011). Sample Size Determination. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_500

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