Power Considerations in Designed Experiments

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...

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Bibliographic Details
Published in:Journal of statistical theory and practice Vol. 14; no. 1
Main Authors: Lin, Luyao, Bingham, Derek, Lekivetz, Ryan
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.03.2020
Subjects:
Algorithms
Analysis and Advanced Methodologies in the Design of Experiments
Mathematics and Statistics
Original Article
Probability Theory and Stochastic Processes
Statistical Theory and Methods
Statistics
Statistical minimum power specification
ANOVA
Power distribution
ISSN:1559-8608, 1559-8616
Online Access:Get full text
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Summary: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.
ISSN:1559-8608
1559-8616
DOI:10.1007/s42519-019-0071-6