Normalizing variables with too‐frequent values using a Kolmogorov–Smirnov test: A practical approach

Normalizing variables with too‐frequent values using a Kolmogorov–Smirnov test: A practical approach

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Article ID: iaor201110327
Volume: 61
Issue: 4
Start Page Number: 1240
End Page Number: 1244
Publication Date: Nov 2011
Journal: Computers & Industrial Engineering
Authors: ,
Keywords: gaussian processes
Abstract:

Many quantitative applications in business operations, environmental engineering, and production assume sufficient normality of data, which is often, demonstrated using tests of normality, such as the Kolmogorov deemed Smirnov test. A practical problem arises when a high proportion of a too‐frequent value exists in data, in which case transformation to normality that passes tests for normality may be impossible. Analysts and researchers are therefore often concerned with the question: should we bother transforming the variable to normality? Or should we revert to other approaches not requiring a normal distribution? In this study, we find the critical number of the frequency of a single value for which there is no feasible transformation to normality within a given a of the Kolmogorov–Smirnov test. The resultant decision table can guide the effort of analysts and researchers.

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