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More generally one can raise the data to a power ''p'' in order to fit symmetrical distributions to data obeying a distribution of any skewness, whereby ''p'' < 1 when the skewness is positive and ''p'' > 1 when the skewness is negative. The optimal value of ''p'' is to be found by a [[numerical method]]. The numerical method may consist of assuming a range of ''p'' values, then applying the distribution fitting procedure repeatedly for all the assumed ''p'' values, and finally selecting the value of ''p'' for which the sum of squares of deviations of calculated probabilities from measured frequencies ([[Chi-squared test|chi squared]]) is minimum, as is done in [[CumFreq]].
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>
== Inversion of skewness ==
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[[File:SanLor.jpg|thumb|left|Composite (discontinuous) distribution with confidence belt<ref>[https://www.waterlog.info/composite.htm Intro to composite probability distributions]</ref> ]]
The option exists to use two different probability distributions, one for the lower data range, and one for the higher like for example the [[Laplace distribution]]. The ranges are separated by a break-point. The use of such composite (discontinuous) probability distributions can be opportune when the data of the phenomenon studied were obtained under two sets different conditions.<ref
== Uncertainty of prediction ==
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