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To fit a symmetrical distribution to data obeying a negatively skewed distribution (i.e. skewed to the left, with [[mean]] < [[mode (statistics)|mode]], and with a right hand tail this is shorter than the left hand tail) one could use the squared values of the data to accomplish the fit.
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
The generalization enhances the flexibility of probability distributions and increases their applicability in distribution fitting.
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