Hypothesis testing is an integral part in the process of experimental design that is used to identify significant effects in a study. A significant effect is one that is statistically determined to influence the response variable of interest and is based on the results of a hypothesis test. Any hypothesis test is prone to two types of error. When an effect is not significant in reality but the null hypothesis is rejected, then it is called a type I error and specified as α. Conversely, when an effect is significant in reality but we fail to reject the null hypothesis, then a type II error is committed and specified as β. Statistical power of a factor is defined as the probability of not committing a type II error (1- β). This research focuses on increasing the statistical power of a factor by augmenting the experimental design with appropriate runs. In this work, a methodology is proposed to integrate power calculations into the existing design of experiment framework. The research also includes a case study to demonstrate the application of the proposed method to real life problems.
Library of Congress Subject Headings
Experimental design; Statistical power analysis
Industrial and Systems Engineering (MS)
Department, Program, or Center
Industrial and Systems Engineering (KGCOE)
Nair, Anil, "Augmentation of Experimental Design Using Statistical Power" (2016). Thesis. Rochester Institute of Technology. Accessed from
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