One for the geeks...

The annual confluence of bank holiday, a rampant garden, and exam marking has seriously curtailed blogging time for now, but I feel compelled to share this fabulous emporium of all things statsy-mathsy-geeky with you. You want cushions of all your favourite statistical distributions?
stats cushions.jpg
Check.

You want statistics propaganda posters?
stats propaganda.jpg
Check.

And all manner of other charming products, from prime number quilts to cross-stitch patterns.

OK, so importing this stuff to the UK is a bit pricey. But I wouldn’t bet against seeing Mola mola Jr. sporting this sometime later this summer…
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Evidence in Science and Policy

Ben Godacre’s Bad Science column has recently been moved to the inside back page of the Saturday Guardian, which means I read it over breakfast (like most people with a passing interest in sport, and old enough still to read news on paper, I always read newspapers back to front, even when the sport is in a separate section…). Last week, he wrote about the dearth of evidence in politics. Specifically, on the resistance to actually finding out – collecting and analysing evidence, in other words – if policies do what they were intended to. I couldn’t agree more, and it’s an issue that has been frustrating me for a while. Those of us involved in environmental science (and I suspect it’s the same in other areas) are constantly bombarded with calls to feed in to the ‘evidence-based policy’ process. Now, basing policy on evidence seems to me a very good idea (although I suspect that ‘evidence-based policy’ is usually just a stalling mechanism – a way to avoid making difficult decisions by constantly calling for more evidence before acting), and one result of this push is that the evidence base for phenomena like climate change, biodiversity loss, etc., is fast becoming exceptional.

But I’ve often felt that whereas ‘scientific’ policy is held up to very high standards of evidence, the same is not the case for ‘social’ policy (nor economic policy either, which may be the subject of a future blog…)

Rather, when considering which policy to advocate, politicians seem as likely to be swayed by a snappily-titled book than by any substantive body of evidence. A title like Blink (‘the power of thinking without thinking’), Nudge (‘improving decisions about health, wealth and happiness’), and Sneeze (‘harnessing the creative power of hayfever’) is ideal (OK, so I may have made one of those up), wherein a (sometimes good) idea is stretched well beyond its limits, and a hodge-podge of facts are crammed into this shaky framework. The Big Society beloved of Mr Cameron falls into this category: a scheme which nobody has tested, but on which basis incredibly important decisions are now being made. (For my money, the ‘big’ is redundant anyway, all that’s being described is what we used to call society (when such a thing still existed…).)

So, yes, Ben Goldacre is absolutely right: let’s get evidence into the policy process, and put some numbers behind big decisions (such as the voting system). If, say, we make wholesale changes to the NHS, triple university tuition fees, or whatever, we must carefully record the outcome of this intervention so that in years to come, we have a fighting chance of deciding whether or not it succeeded.

Where I depart slightly Goldacre is in how we do this. He (like most medics) is a firm believer in the randomised controlled trial, a tremendously powerful way to assess the efficacy of a medical procedure. In some cases, it may be feasible to perform analogous trials in social policy, but this will rarely be the case – you can’t, for instance, change the whole governance structure of one hospital in a region without changing others; and if you then end up comparing regions, the randomising is lost, as regions will differ in a series of other metrics.

I should add that Goldacre’s column is predicated on two books about randomised trials in social policy, which I haven’t yet read. My scepticism is derived more from my experience in applied ecology, where there has been a move recently to adopt medical methods – specifically, systematic reviews – to assess the outcome of conservation interventions. The problem is, ecosystem manipulations are not clinical trials. Often, there is no standard intervention, and even if there is, it may be applied to very different systems (differing in species composition, and all kinds of physical characteristcs, not least spatial extent). And often too, there is no agreed-upon outcome – I could increase the species richness in my garden, for instance (at least for a while) by introducing Japanese knotweed, but few would argue that that would be a ‘good’ conservation outcome. In medicine, you treat a patient, and they get better or they don’t, making comparisons between trials much more straightforward.

The solutions that environmental scientists have come up with generally are highly sophisticated statistical methods, allowing us to draw powerful inference from nasty, heterogeneous data. Similar methods have of course been applied to social systems, but somehow they don’t seem to feed through to policy, at least not as often as they should; and even when they do, they risk being ignored if their message is politically undesirable .

To return to the original point, improving social and environmental policy requires that we know what has worked, and what has not, in the past and elsewhere. So solving this evidence problem (i.e. gathering it, and communicating it) should be top priority for both natural and social sciences.

Tense

I am in paper-writing mode, which means (among other trials and tribulations) I am wrestling with the issue of tense. Specifically, did I apply my methods in the past, or am I implementing them now? Did my results show something, or are they still showing them? Which tenses need to match (and when…)? Primarily, this is a matter of style, and there seems to be a consensus that the present tense is somehow indicative of more exciting, more relevant results. Start your paper ‘Here, we show for the first time…’ and you are right on the bleeding tip of the cutting edge. ‘Here, we showed for the first time…’, on the other hand, and you are already yesterday’s news.

Now, I’ll plead guilty to sprucing up my dry academic prose with liberal sprinklings of immediacy, probably more often than is strictly healthy. But this kind of perky presence can really grate if used to excess, and can lead one into tricky little linguistic culs de sac to boot. A special bugbear of mine, for instance, is the insistence of all TV and radio historians on relating long-past events exclusively in the present tense. “In 1066, William invades England. After a bloody battle, he is victorious…”, and suchlike. I assume they have all been told that it somehow brings history alive to talk in this way. It didn’t, it doesn’t, and it won’t. It just annoys me. (If you’ve never noticed this before, you will now, and I’m afraid I may have ruined your enjoyment of the otherwise wonderful Simon Schama, for which I apologise!)

To return to the matter in hand, the particular problem with writing, say, a description of your statistical methods in the present tense, is what happens when one thing leads to another? For example, suppose you fit(ted) a linear model to some data, but on inspection of the model output, decide to remove a non-signficant interaction in order to make interpretation easier. You could find yourself writing:

“We model y as a function of x1, x2, and their interaction. Because the interaction is not significant, we exclude it and re-run the model”, which doesn’t seem right to me – you have described a sequence of events, but only one point in time. But if you start with “We modelled y as a function of x1, x2, and their interaction…” you are then committed to the past tense throughout.

Similar choices have to be made in the results. Is y significantly related to x; or was it? I tend to prefer the present in this case, because to use the past tense implies that the results were somehow contingent on something specific I did on the single occasion I ran the models for the paper, rather than being a constant property of the data (analysed according to my protocol).

But consistency is the key. And with a large pool of co-authors, and sufficient iterations of a manuscript through multiple drafts, tenses do tend to drift. So you can be performing an experiment which produced certain results, and other logical slips.

None of this really matters, perhaps. But if you want reading your paper to be a pleasant experience (as well as a necessary one, naturally) for your peers, then maybe it is worth plotting the timeline of your sentences to make sure no wormholes have appeared.

(For more on scientific writing, by the way, Tine Janssens has collected a load of good links in her latest interesting post, so rather than replicate them here, I’d encourage you to read them there.)