Standard Guide for Identification and Transformation of Frequency Distributions (Withdrawn 2009)
In measuring, testing, and experimenting, statistical tests are made to determine whether the observed effect of the introduction of a factor is real or simply due to chance. The appropriate statistical test to use depends on the kind of distribution used to model the data. Distribution identification is useful in selecting the most powerful statistical test.
Note 2—There are statistical tests which can be used for data for which a parametric distribution cannot be selected. But these non-parametric tests do not discriminate as well as the distribution-dependent tests.
For certain types of data, a transformation can be made which will make it possible to use the hypothesis that the normal distribution is a suitable model for the transformed data. When this hypothesis can be made, the analysis of the data is made much easier.
1.1 This guide gives the rudiments of identification of some of the most common and useful frequency distributions. It does not give rigorous identification. To achieve exactitude, the procedures similar to those given by Shapiro should be used.
1.2 This guide provides a key to identify frequency distributions.
1.3 This guide gives ways to select the proper transformation to use to transform a particular set of data to one which can be modeled by the normal distribution, if such a transformation can be found at all.
1.4 This guide includes the following topics:
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