The normal curve has long been important in statistics. Most interval variables yield normal or quasi-normal distributions when data are collected from large samples, and the normal "Z" distribution is also used as a test statistic (e.g., to test differences between two means when sample size is large, since "t" approaches "Z" as degrees of freedom increase). Thus, almost all statistics books discuss the normal curve. Nevertheless, many researchers do not fully understand some concepts related to the normal curve, such as skewness and kurtosis statistics, because these two statistics often receive cursory instructional treatment, given the press for instructional time. This paper illustrates that shape statistics remove the influence of distribution variability (i.e., shape statistics always initially involve the conversion of raw scores to "Z" form, SD=1=V, so that impact of variability is held constant). Nine figures illustrate the shape statistics, and one table lists raw scores and "Z" scores. An eight-item list of references is included. (Author/SLD)