Championing maths and statistics: a Q&A with Sir Adrian Smith

Written by Oz Flanagan on . Posted in Features

Adrian Smith has spent his career at both the highest levels of academia and advising government, giving him a valuable insight into how the lessons learned in academic statistics can be translated into meaningful education policy.

His academic career reads like a roll call of some of England’s finest mathematics universities. From his student days at Cambridge University and University College London, through to his academic posts at Nottingham University, Oxford University and Imperial College. He then went on to become Principal of Queen Mary University and since September of 2012 he has held his current post of Vice-Chancellor of the University of London.

In the past he has been appointed as an advisor to the Department of the Environment and the Ministry of Defence. Further to this he has been an advocate of supporting official statistics through his advisory positions at the Office for National Statistics and on the board of the UK Statistics Authority. He also had the honour of serving as RSS President from 1995 to 1997. Given his wealth of experience, we asked him for his thoughts on the best way to promote the importance of mathematics and statistics from education to official statistics.

You have recently been appointed to the Council of Mathematical Sciences, what do you see as the key challenges and opportunities for the mathematical sciences and statistics over the next few years?

There are challenges relating to the overall current and future health of all the mathematical sciences disciplines - a key issue being the 'people pipeline', ensuring the resources for and commitment to nurturing and developing talent, not only in the university context but right the way back through the education system. However, this quickly takes us into potentially contentious areas like curriculum content, modes of assessment and the supply of appropriately qualified teachers. In recent years, I think we’ve found it difficult as a mathematical sciences community to reach a clear consensus on some of these issues, but without such a consensus we may miss out on opportunities to influence policy.

These are also challenges in influencing and shaping public perception and awareness of the importance of the mathematical sciences. Some of this relates to the much discussed national cultural aberration, but some of it also relates to the all-pervasive nature of mathematics - it is everywhere, in everything, so, perversely, you can easily overlook or forget its fundamental presence. This means that we need to take every opportunity to wave the flag and beat the drum, so that everyone - children, parents, media opinion formers, research funders and ministers - gets the message. I think we need to get smarter at our PR and this may mean pragmatically that our messages will be predominantly geared around the more applied end of the spectrum. I don’t think we’ve been energetic enough in recent years in engaging with and communicating the role of the mathematical sciences in the societal and industrial challenges identified in, for example, the Research Councils’ cross-cutting themes or the TSB’s Catapults, much of which will also feature in the new EU Horizon 2020 programmes. In terms of emerging hot topics,  we clearly need to rise to the 'big data' challenge – whatever that means!

New government curriculum proposals appear to suggest that data handling skills should be given a higher profile in science and geography subjects rather being confined to just mathematics, how do you think this could be done to ensure that data skills are taught successfully?

I have always thought, since my 2004 'Making Mathematics Count' report, that this was the right way to go. There may need to be some specifically designed continuing professional development to support this, which may be a niche opportunity for the RSS in the future.

There is an ongoing debate over the differences between a ‘data scientist or analyst’ and a ‘statistician’. Do you think there are any differences between the two?

Given the scale on which data is now being generated, collected and processed in so many areas - health, environment, retail, finance - I do think there are skill sets required in a 'data scientist' that go considerably beyond just those that are provided by traditional statistical training, such as issues of data provenance, quality, modelling, numerical computation and interpretation. I think a minimal skill set now needs to include not just basic mathematical science competence but also a significant amount of computer science, with in depth understanding of issues around data storage, manipulation, searching, flow and transmission. There are, of course, some serious issues about how you produce such all-knowing beings given current disciplinary boundaries and educational organisation.

You are also deputy chair of the UK Statistics Authority, what do you see as the Authority’s most important role in official statistics since its creation?

The UKSA has two responsibilities: first, to act as a kind of quality assurance agency for all official statistics, acting as a watchdog to pick up on and respond to departures from appropriate standards of production or usage; secondly, and this is currently my own main Deputy Chair involvement, to provide governance oversight of the production function carried out by the Office for National Statistics. It is really for others to judge, but I believe the Authority has established a strong reputation for independence and quality professional judgements.

Finally, how important is public understanding of statistics, and how statistically literate do you think we, as a society, should be, in an ideal world?

I think it is fundamentally important both for the individual and for the functioning of a civilised parliamentary democracy. At the individual level, ability to engage with discourse around the assessment of risk is fundamental to decision-making relating to health and wealth. In the public domain, virtually all engagement with policy discussions ultimately relies on interpretation, actively or passively, of data. Data handling and interpretation absolutely needs to be part of the school curriculum.

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