  The normal distribution and its perturbation has left an immense mark on the
statistical literature. Hence, several generalized forms were developed to
model different skewness, kurtosis, and body shapes. However, it is not easy to
distinguish between changes in the relative body and tail shapes when using
these generalizations. What we propose is a neat integration approach
generalization which enables the visualization and control of the body and the
tail shape separately. This provides a flexible modeling opportunity with an
emphasis on parameter inference and interpretation. Two related models, the
two-piece body-tail generalized normal and the two-piece tail adjusted normal
are swiftly introduced to demonstrate this inferential potential. The
methodology is then demonstrated on heavy and light-tailed data.
