Can't scientists be intellectuals too?

Is it conceited of me to fancy myself as a bit of an intellectual? I have after all, like most practising scientists, obtained a doctorate in philosophy (and my PhD certainly included a good amount of ‘Ph’). Now I am paid, essentially, to sit and think, and to write about what I have thought. And I read pretty widely, and pretty critically, fact and fiction. If nothing else, the range of posts on this blog show that I’m certainly prepared (am perhaps too keen, if anything) to pontificate on subjects well outside my nominal area of expertise. I don’t think I am in any way unusual in this. Plenty of professional scientists would also count themselves as thinkers, perhaps even philosophers, and certainly intellectuals. Yet scientific voices remain very scarce in public intellectual debate in the UK. Sure, we’ll wheel out an ‘expert’ to comment on something on which they are, indeed, expert. But the thinkers considered suitable contributors to more general discussions on the human condition, public policy, and the like, remain overwhelmingly drawn from the arts, the humanities, and the social sciences.

This is all to give a little context, then, to my rather chippy reaction to finding out that the Arts & Humanities Research Council, together with the BBC, have identified 10 new public intellectuals through their New Generation Thinkers scheme. Said reaction being: ‘So ’intelligent discussion’ programmes on TV and radio will welcome yet more Oxbridge humanities graduates with a double first in self aggrandisement and a keen interest in the sounds of their own voices, yet no concept of the distinction between fact and opinion? Whoopie-doo.’

As it happens, this is unfair. Although at least 6 of the 10 have come through Oxbridge at some point (generally as undergrads), only one of them currently works at Cambridge, and none at Oxford. And they all look to be very high-achieving, worthy winners – several have already published well-received books, and all their ‘specialist subjects’ sound interesting, from Britain in the 1790s to the Rwandan Genocide.

But the broader point, and the source of my frustration, holds: why aren’t the scientific research councils doing something similar? Listen to what the scheme offers its winners, and turn green with envy:

BBC Radio 3 and the Arts and Humanities Research Council (AHRC) today announce the 10 winners of the inaugural New Generation Thinkers Scheme – the culmination of a nationwide search for the brightest academic minds with the potential to turn their ideas into fascinating broadcasts… The New Generation Thinkers for 2011 will now work closely with dedicated mentors from the production team of Radio 3’s arts and ideas programme Night Waves (Mondays to Thursdays 10-10.45pm). And each night from Tuesday 28 June, and for nine subsequent editions of Night Waves, a New Generation Thinker will talk about an idea inspired by their research.

Sounds pretty good, eh? And when you consider that it will surely be a fast track to regular appearances on the myriad cultural comment programmes on TV and radio, all in all a pretty good gig.

The science research councils seem to still be operating on assumption that what scientists really want is instruction from communications professionals, that we are all clamouring for the opportunity to be offered a walk-on part in Horizon, rather than actually to shape public intellectual discourse.

Even the Royal Society, who fund me, have betrayed something of this attitude in the new secondment opportunity that they have introduced for research fellows, which appears to be a kind of ‘advanced work experience’ at the BBC – getting to play with editing software and the like. It does look fun, I admit (providing you already live in London, as it comes with no living expenses; interestingly, 5/10 of the New Generation Thinkers are also London-based), but it does rather stand in contrast to the AHRC scheme: work experience, as opposed to a leg-up into public intellectual life.

I’m not suggesting that we scientists are offered anything on a plate, nor that we have any special privilege to contribute to public discourse. But rather, that public discourse would be enriched by more contributions of working scientists, applying their hard-won analytical and critical skills to fields outside their area of expertise. This would be both a complement and a balance to the views expressed (often in the absence of data) by the various think-tanks, pressure groups, and assembled ‘thinkers’ that tend to dominate discussions of social policy options.

Little bit of politics...

Yesterday in the UK, large numbers of public sector workers went on strike to protest about the changes that the government wants to make to their pensions. I was catching up with this by watching Newsnight, and a peculiar rhetoricical tactic became apparent from the government representative. Apparently unrelated to this, I have recently started what I hope will be a fruitful collaboration with a pure mathematician. In a meeting the other day, I was struck once again by the different prism through which mathematicians see the world, apparent through the questions that my colleague asks about ecological phenomena.

In the spirit of seeing things mathematically, I thought I would have a go at applying some basic set theory to the Newsnight debate. Well, that’s probably a little too grand – basically, I’ve produced a pair of Venn diagrams.

This first diagram represents government rhetoric: it is unfair for the generous public sector pensions to be funded by taxpayers (implicitly: public sector workers ⊄ taxpayers). And more specifically, those who suffer most during the strikes – especially those by teachers – are that (tautological, as I am now acutely aware) demographic most beloved of politicians of all stripes: ‘hard-working parents’ (i.e., teachers ⊄ hard-working parents).

In reality, of course all public sector workers are also taxpayers (in fact, about 20% of those working in the UK are public sector employees; PDF of the Office for National Statistics source for this here). And do you know what? Plenty of public sector employees are also parents. Some of these are even – shock! – teachers. And I don’t doubt that at least a few of these work hard. (NB – the incomplete union of hard-working parents with taxpayers is intentional, but more to capture the cash economy than the wealthy tax dodgers!)

I don’t pretend to understand pensions, I can barely muster the interest to open the annual statements I get regarding mine, and certainly I’m not in a position to offer any kind of analysis over whether we really are heading for a crisis that can only be averted by making us all work longer for a lower pension at the end. But, setting up false dichotomies to make political points ought not to pass without challenge.

And with that – I’m off on my hols!

Vital Statistics

Statistical analysis divides bioligists. There are those of us who like nothing more than to dive in to a sparkly new method, and who feel a day has been wasted if it has not involved the interrogation of at least one little model. And then there are normal biologists, for whom stats are, at best, a necessary evil, an arduous process that has to be endured in order to convince others of what you already know to be true. The kind who got into biology to get away from maths, and who have been resentful of its intrusion into their working lives ever since. But, given that biological research without statistics is not really biological research, every tree-hugging, dolphin-bothering biologist must at some point roll up their sleeves and learn some stats. And the question that faces people like me, who have to teach them this stuff that they find difficult and pointless, is: what does a biologist need to know about statistics?

So I was interested to come (indirectly, via the excellent R-bloggers site) across a blog by Ewan Birney entitled Five statistical things I wished I had been taught 20 years ago. Making lists like these is always going to set you up for a fall, and if I disagree with some of Ewan’s points, that’s not to say I don’t admire him for posting them. And some of what he has to say I agree with entirely.

Of his five, for example, I would stick a big tick next to nos. 2 (learn R) and 4 (understand the difference between a P value and an effect size, with (especially relevant for a bioinformatician) reference to sample size), and I also agree with 3 that permutation / randomisation can be a very effective way of making sense of large and badly-behaved datasets.

Regarding number 5, on learning linear models and Principal Components Analysis, I kind of agree. In as much as, yes, properly understanding the basic linear model is probably the single most important piece of statistical knowledge that you can obtain. So much else flows from it: from General to Genearlised Linear Models, on to Generalised Additive (Mixed) Models, mixed effects models of many different flavours, Generalised Least Squares, and most other things you will ever need. On the way, cutting out a lot of confusing terminology too, by conceptually uniting things like regression, Anova and t-tests that most of us are taught as stand-alone tests. PCA, on the other hand, is a useful little tool for visualising multivariate data, and potentially then for feeding in to more interesting analyses, not much more.

But I can’t agree with number 1, on the importance of non-parametric statistics. In my experience, people tend to use these because they don’t really understand the assumptions of parametric methods like linear models: almost always, these make assumptions about the residuals, rather than the raw data – so one of your variables not being ‘normal’ is not necessarily cause for alarm – and almost always, there is a parametric technique which has been designed to cope with the specific odd residual distribution that you have, and which will (incidentally) be considerably more powerful than its non-parametric (typically rank-based) equivalent. If not, randomisation will generally be more informative than rank-based statistics. In addition, people tend not to have a good understanding of the assumptions made by non-parametric tests (often relating to the variance distribution, the very thing which may have driven you to non-parametrics in the first place!).

Vital statstics for biologists, then? Learn your linear models (some good books). And basic probability. But then judging risk and probability is a vital skill for everyone, if you ask me.

Or, you can follow the advice that I read in a ‘Statistics for Social Scientists’ book (and I don’t mean to bash the social sciences here, at all, I know they drive some excellent stats, that did just happen to be the book I read this advice in): If the data are not unanimous in support of your hypothesis – change your hypothesis…