ABSTRACT
This chapter presents graphical displays for checking distributional assumptions about data. At the heart of probabilistic statistical analysis is the assumption that a set of data arises as a sample from a distribution in some class of probability distributions. The reasons for making distributional assumptions about data are several. First, if the author can describe a set of data as a sample from a certain theoretical distribution, say a normal distribution, then can achieve a valuable compactness of description for the data. A second reason for distributional assumptions is that they can lead to useful statistical procedures. A third reason is that the assumptions allow us to characterize the sampling distribution of statistics computed during the analysis and thereby make inferences and probabilistic statements about unknown aspects of the underlying distribution. A fourth reason for distributional assumptions is that understanding the distribution of a set of data can sometimes shed light on the physical mechanisms involved in generating the data.
