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160 Exciting timesIn general I find that the changes lamented by Finney, and applauded byVarian, invigorate and energize our field. I’m not troubled by them, exceptfor the fact that they can make it more challenging to get funding for positionsin statistics, and more generally for research and teaching in the field.Indeed, it is not just in Australia, but across the globe, that the funding poolthat is notionally allocated to statistics is being challenged by many differentmulti-disciplinary pressures, to such an extent that financial support for coreresearch and teaching in statistics has declined in many cases, at least relativeto the increasing size of our community.Funding is flowing increasingly to collaborative research-centre type activities,where mathematical scientists (including statisticians) are often notinvolved directly at all. If involved, they are often present as consultants,rather than as true collaborators sharing in the funding. This is the maindanger that I see, for statisticians, in the diversification of statistics.15.2.2 Global and local revolutionsThe diversification has gone hand in hand with a revolution, or rather severalrevolutions, that have resulted in the past from the rapid development of inexpensivecomputing power since the 1970s, and from technologies that have ledto an explosion in machine recorded data. Indeed, we might reasonably thinkthat massive technological change has altered our discipline mainly throughthe ways our data are generated and the methods we can now use to analysethem.However, while those changes are very important, they are perhaps minorwhen set against the new questions that new sorts of data, and new computationaltools, enable scientists to ask, and the still newer data types, anddata analyses, that they must address in order to respond to those questions.Statisticians, and statistical methods, are at the heart of exploring these newpuzzles and clarifying the new directions in which they point.Some aspects of the revolutions are “global,” in the sense that, althoughthe motivation might come from a particular area of application, the resultingchanges influence many fields. Others are more local; their impact is notso widespread, and sometimes does not stray terribly far from the area ofapplication that first motivated the new developments.During the last 30 years or so we have seen examples of both global and localrevolutions in statistical methodology. For example, Efron’s (1979a) bootstrapwas definitely global. Although it arguably had its origins in methodologyfor sample survey data, for example in work of Hubback (1946), Mahalanobis(1946), Kish (1957), Guerney (1963), and McCarthy (1966, 1969), ithas arguably touched all fields of statistics. [Hall (2003) gave a brief account ofthe prehistory of the bootstrap.] Statistical “revolutions” that are more local,in terms of influence, include work during the 1980s on image analysis, andmuch of today’s statistical research on very high-dimensional data analysis.