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Power Considerations in Designed Experiments

  • Luyao Lin
  • Derek BinghamEmail author
  • Ryan Lekivetz
Original Article
  • 13 Downloads
Part of the following topical collections:
  1. Algorithms, Analysis and Advanced Methodologies in the Design of Experiments

Abstract

Designed experiments for ANOVA studies are ubiquitous across all areas of scientific endeavor. An important decision facing experimenter is that of the experiment run size. Often the run size is chosen to meet a desired level of statistical power. The conventional approach in doing so uses the lower bound on statistical power for a given experiment design. However, this minimum power specification is conservative and frequently calls for larger experiments than needed in many settings. At the very least, it does not give the experimenter the entire picture of power across competing arrangements of the factor effects. In this paper, we propose to view the unknown effects as random variables, thereby inducing a distribution on statistical power for an experimental design. The power distribution can then be used as a new way to assess experimental designs. It turns out that using the proposed expected power criterion often recommends smaller, less costly, experimental designs.

Keywords

Statistical minimum power specification Power distribution ANOVA 

Notes

Supplementary material

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Copyright information

© Grace Scientific Publishing 2019

Authors and Affiliations

  1. 1.Department of Statistics and Actuarial ScienceSimon Fraser UniversityBurnabyCanada
  2. 2.JMP DivisionSAS InstituteCaryUSA

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