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The generalization enhances the flexibility of probability distributions and increases their applicability in distribution fitting. <ref name="gen">Software for Generalized and Composite Probability Distributions. International Journal of Mathematical and Computational Methods, 4, 1-9 [https://www.iaras.org/iaras/home/caijmcm/software-for-generalized-and-composite-probability-distributions] or [https://www.waterlog.info/pdf/MathJournal.pdf]</ref>
The versatility of generalization makes it possible, for example, to fit approximately normally distributed data sets to a large number of different probability distributions, <ref>Example of an approximately normally
distributed data set to which a large number of different probability distributions can be fitted, [https://www.waterlog.info/pdf/Multiple%20fit.pdf] </ref> while negatively skewed distributions can be fitted to
square normal and mirrored Gumbel distributions. <ref>Left (negatively) skewed frequency histograms can be
fitted to square normal or mirrored Gumbel probability functions.
[https://www.waterlog.info/pdf/LeftSkew.pdf]</ref>
== Inversion of skewness ==
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