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R.D. Cook 105variance reduction designs difficult to employ, particularly since the underlyingnon-linear model called for an unbalanced treatment design.Optimal experimental design was for many years regarded as primarily amathematical subject. While applications were encountered from time to time,it was seen as largely a sidelight. Few would have acknowledged optimal designas having a secure place in statistical practice because the approach was toodependent on knowledge of the model and because computing was often animpediment to all but the most straightforward applications. During the 1970sand most of the 1980s, I was occasionally a party to vigorous debates on therelative merits of classical design versus optimal design, pejoratively referred toby some as “alphabetic design” in reference to the rather unimaginative designdesignations like D-, A- and G-optimality. Today classical and optimal designare no longer typically seen as distinct approaches and the debate has largelyabated. The beginning of this coalescence can be traced back to technologicaladvances in computing and to the rise of unbalanced experimental settingsthat were not amenable to classical design (Cook and Nachtsheim, 1980, 1989).9.4 Enjoying statistical practiceStatistics has its tiresome aspects, to be sure, but for me the practice ofstatistics has also been the source of considerable pleasure and satisfaction,and from time to time it was even thrilling.For several years I was deeply involved with the development of aerial surveymethods. This included survey methods for snow geese on their moltinggrounds near Arviat on the west shore of Hudson Bay, moose in northernMinnesota, deer in southern Manitoba and wild horses near Reno, Nevada.It became apparent early in my involvement with these studies that the developmentof good survey methods required that I be actively involved inthe surveys themselves. This often involved weeks in the field observing andparticipating in the surveys and making modifications on the fly.The moose and deer surveys were conducted in the winter when foliagewas largely absent and the animals stood out against a snowy background.Nevertheless, it soon became clear from my experience that aerial observerswould inevitably miss some animals, leading to underestimation of the populationsize. This visibility bias would be a constant source of uncertaintyunless a statistical method could be developed to adjust the counts. I developeddifferent adjustment methods for moose and deer. Moose occur in herds,and it seemed reasonable to postulate that the probability of seeing an animalis a function of the size of its herd, with solitary animals being missedthe most frequently. Adding a stable distribution for herd size then led to anadjustment method that resulted in estimates of population size that were inqualitative agreement with estimates from other sources (Cook and Martin,