Many commonly used statistical methods assume that data are normally distributed . This assumption is often violated in practice . There has been an increasing interest over the last decade in the construction of flexible parametric families of distributions that exhibit skewness and kurtosis differing from those of the normal distribution . Skewed and heavy tailed data occur frequently in real life and pose challenges to our usual way of thinking . Examples of such data include household incomes , loss data such as crop loss claims and hospital discharge bills , and files transferred through the Internet to name a few . Candidate distributions for simulating and fitting such data are not abundant . One can’t simply take the normal or the t distributions or as such as substitutes .