Statistical applications and repercussions of Poisson's work are reviewed in historical perspective with special reference to (i) the distinction he made between two kinds of probability; (ii) the law of large numbers; (iii) the Poisson distribution; (iv) the difference between two proportions; (v) legalistic statistics; (vi) Poisson's summation formula; (vii) the Cauchy distribution; and (viii) the Poisson bracket.

Biometric studies of the forms of organisms usually consider size and shape variations in the geometric configuration of landmarks, points that correspond biologically from form to form. The size variables may be usefully considered the linear vector space spanned by the set of all distances between pairs of landmarks. The shape of a single triangle $\Delta ABC$ of landmarks may be reduced to a single pair of shape coordinates locating the vertex $C$ in the coordinate system with landmark $A$ sent to (0,0) and landmark $B$ to (1,0). A useful space of shape variables is the span of all such shape coordinate pairs for various triples of landmarks. On a convenient null model of identical circular normal perturbations at each landmark independently, one size variable $S$, which may be taken as the mean square of all the interlandmark distances, has covariance zero with every shape variable. Then associations between shape and size may be tested by the $F$ ratio for multiple regression of $S$ on any basis for shape space. For a single triangle of landmarks, the existence of any mean difference or mean change in shape may be tested by Hotelling's $T^2$ applied to any pair of shape coordinates for that triangle. When such a difference is statistically significant, it may be interpreted as the ratio of a pair of size variables measured along directions at an angle averaging $90^\circ$ in the samples of forms. One size variable will bear the greatest mean rate or ratio of change between the forms, the other the least. Analysis of configurations of more than three landmarks reduces to consideration of size variables involving at most three landmarks. These techniques are demonstrated in a study of the growth of the head in 62 normal Ann Arbor youth. Each comparison of interest is summarized in its own orthogonal coordinate system, the biorthogonal grid pair.

A computing environment is a collection of hardware and software tools that are integrated so as to work well with one another and designed to make a selected class of computations easy and natural to carry out. Experience with interactive statistical programs such as GLIM, Snap, and Minitab suggests that experienced data analysts develop distinctive computing styles when approaching data that integrate some of the tools these programs provide into more general strategies for data analysis. Computing environments specifically designed for data analysis make these strategies easier and more natural to describe and to implement, and make them accessible to data analysts with considerably less experience.

The two persons most responsible for the founding of the IMS in 1935 were Henry Rietz and Harry Carver, and some background about each of them is given. Others who had some important roles to play were Cecil Craig, Allen Craig, Sam Wilks, Al O'Toole, Walter Shewhart, Paul Rider, and Harold Hotelling. Fred Stephan, then the executive secretary of the American Statistical Association, wanted IMS to be one of ASA's sections and worked very hard to smooth the way for that type of relationship. While Rietz also leaned in that direction, Carver did not trust ASA at that time and wanted nothing to do with that arrangement. In 1938, three years after IMS was established, Carver officially turned his Annals of Mathematical Statistics over to IMS; and IMS's initial editorial board consisted of Sam Wilks, Jerzy Neyman, and Allen Craig.