t-Tests are widely used by researchers to compare the average values of a numeric outcome between two groups. If there are doubts about the suitability of the data for the requirements of a t-test, most notably the distribution being non-normal, the Wilcoxon-Mann-Whitney test may be used instead. However, although often applied, both tests may be invalid when discrete and/or extremely skew data are analyzed. In medicine, extremely skewed data having an excess of zeroes are often observed, representing a numeric outcome that does not occur for a large percentage of cases (so is often zero) but which also sometimes takes relatively large values. For data such as this, application of the t-test or Wilcoxon-Mann-Whitney test could lead researchers to draw incorrect conclusions. A valid alternative is regression modeling to quantify the characteristics of the data. The increased availability of software has simplified the application of these more complex statistical analyses and hence facilitates researchers to use them. In this article, we illustrate the methodology applied to a comparison of cyst counts taken from control and steroid-treated fetal mouse kidneys.