Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1135607 | Computers & Industrial Engineering | 2011 | 5 Pages |
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 α of the Kolmogorov–Smirnov test. The resultant decision table can guide the effort of analysts and researchers.
► We address a common problem in business and engineering production analytics. ► We find when a transformation to normality can satisfy a Kolmogorov–Smirnov test. ► The resultant decision table can guide the effort of analysts and researchers.