1. With an educational background in mathematics at St Johns College, Cambridge and the University of Leeds, when and how did you first become aware of statistics as a discipline?
I was studying at Cambridge during the Second World War and after two years, one was sent either into the Forces or into some kind of military research establishment. There were very few statisticians then, although it was realised there was a need for statisticians. It was assumed that anybody who was doing reasonably well at mathematics could pick up statistics in a week or so! So, aged 20, I went to the Royal Aircraft Establishment in Farnborough, which is enormous and still there to this day if in a different form, and I worked in the Department of Structural and Mechanical Engineering, doing statistical work. So statistics was forced upon me, so to speak, as was the case for many mathematicians at the time because, aside from UCL, there had been very little teaching of statistics in British universities before the Second World War. Afterwards, it all started to expand.
14. At the recent Future of Statistical Sciences workshop, there was much talk about Big Data and a concern that many ‘hot areas’ such as big data/data analytics, which have close connections with statistics and the statistical sciences, are being monopolised by computer scientists and/or engineers. What do statisticians need to do to ensure their work and their profession gets noticed?
Do better quality work, which I don’t mean as a criticism as to what is done at the moment but rather, do high quality work that is important in some sense, either intellectually or practically in particular fields. Part of the problem is that relatively speaking, there are not that many statisticians who are trained to the level needed.
15. What do you think the most important recent developments in the field have been? What do you think will be the most exciting and productive areas of research in statistics during the next few years?
The most immediately important is as you said – Big Data, which will bring forward new ideas but it does not mean that old ideas from the more traditional part of the subject is useless. It is the most obvious and biggest challenge.
Ideally, we should be looking at very important practical problems in a different number of fields and see some sort of common element and build the ideas that are necessary in order to tackle any issues that arise. You should not tackle just one issue successfully but tackle a collection of issues – the Big Data aspect is one common theme undoubtedly. It goes beyond statistics – to what extent Big Data can replace small, carefully planned investigations which are much more sharply focussed on a very specific issue.
My intrinsic feeling is that more fundamental progress is more likely to be made by very focused, relatively small scale, intensive investigations than collecting millions of bits of information on millions of people, for example. It’s possible to collect such large data now, but it depends on the quality, which may be very high or not, and if it is not, what do you do about it?
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