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The Role of Sample Size in Testing a Hypothesis in Complex Cross-Sectional Studies: A Monte Carlo Simulation Study

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  • P A E Serumaga-Zake
  • R Arnab

Abstract

In this study, we explore the relationship among sample size, variability of a variable, power of the statistical test and effect size in complex cross-sectional studies.The argument that power analysis can be used to optimize the resource usage through determining the smallest sample size needed for a study to assess the detectable effect size has been found questionable. It is worse when dealing with independent variables, which have more than two groups to be compared because the null hypothesis and alternative hypothesis keep on changing with the sample size. Any population mean difference, so long as it is absolutely not equal to zero, can be found statistically significant depending on the sample size, holding other factors constant. The study suggests that, in complex cross-sectional studies, it is very difficult, if not impossible, to determine the appropriate sample size for a hypothesis test in the planning stage of a study.

Suggested Citation

  • P A E Serumaga-Zake & R Arnab, 2008. "The Role of Sample Size in Testing a Hypothesis in Complex Cross-Sectional Studies: A Monte Carlo Simulation Study," Studies in Economics and Econometrics, Taylor & Francis Journals, vol. 32(2), pages 63-68, August.
    • Handle: RePEc:taf:rseexx:v:32:y:2008:i:2:p:63-68
    DOI: 10.1080/10800379.2008.12106450
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