Precision Phrased

Following on from my last post, which contained some pretty precise phrases (albeit not ones of my own), I’ve been reading with interest some good recent posts on the need for precision in scientific writing. On this network Matt Shipman (@ShipLives) wrote about the importance of defining technical terms, using examples like ‘extinction’ which is often used more loosely than its specific and precise meaning allows. Elsewhere, Lewis Spurgin (@LewisSpurgin) channels Orwell in an entertaining (and, I may add, spot-on) tirade against poor writing in science. Simon Leather (@EntoProf) has a more specific complaint regarding lazy referencing and ignoring precedence when citing the work of others. Although these three posts take aim at different targets, they are united by their abhorrence of sloppiness. Writing properly matters, whether that be choosing the right word, avoiding lazy phrasing, or applying rigorous standards of scholarship. On two occasions recently I’ve had pause to reconsider the precision of my own writing. First, I received an email about one of my papers (the one I discussed here). It was a nice email, but it contained this:

There was one sentence that I wanted to ask you about: ‘More generally, the environmental conditions (as measured by the typical spatial and temporal scales of environmental variation) that are likely to select for or against the evolution of specialisation are not restricted to either marine or terrestrial systems…’ I just want to make sure that I understand this correctly – are you suggesting that both marine and terrestrial systems have relatively variable environments and also relatively stable environments?

‘No, no, no!’ was my initial thought, ‘you’re not meant to actually read what I write, and certainly not to take it seriously!’ This partly was just an expression of my underlying anxiety about being rumbled and hounded out of the academy; but it also instigated a minor panic about my writing methods. Writing is something that comes quite easily to me - I’m not making any claims as to quality, but when I get going the words flow without major obstruction. A consequence of this is that sometimes I will write something that feels right, stylistically, without necessarily considering every nuance of meaning. My defence against this is to proof-read and edit mercilessly and repeatedly, and usually this works. On the occasion in question, for example, a second reading was reassuring, and I was able to respond that yes, that was exactly what I had meant; furthermore, I could happily stand by it as I was confident that it was a valid statement (even if not expressed with perfect precision).

The second instance is rather different. I was part of a group which drafted a call for a large funding programme in marine ecology. The resulting document suffered from all the pitfalls of writing-by-committee, and contains various ambiguities (is this a ‘work package’ or an ‘objective’?), lazy citations, important-sounding but essentially empty phrases, and incidences of imprecision that didn’t seem to matter at the time as we were aiming for ‘big picture’ stuff.

But now I’m part of a consortium that is actually trying to get at some of this funding, and every hollow phrase, every ambiguity or lazy shortcut, is coming back to haunt me. The document we wrote is now a sacred text. Sticking to the text is seen as crucial to securing the (multi-million pound) prize. Hushed and urgent conversations include phrases like, “We must follow exactly what the text says here”; “When they give examples of the kinds of things we might address, this means we must address exactly those things, and nothing else” [perish the thought that they were simply the first examples to occur to us]; “What do you think they meant by this?” [just sort of sounded right…]

The moral? Be careful what you write. Someday, somebody may read it carefully and quiz you on it. Worse, they may not, and take what you wrote at face value. You’d better hope then that your phrasing is sufficiently precise to bear the weight of responsibility.

Pretentious, moi? Literary quotes in science

The most important thing to consider as a PhD student writing up is, of course – I’m sure we’d all agree – what quotes you plan to use in order to show of to your examiners just how cultured and well-read you are. A decade and more after submitting my thesis, I’m still proud of my selections, feeling they tick both boxes. (I will leave it to you to decide whether they also tick a third, ‘pretentious git’.) Having finally, reluctantly come around to the fact that the total number of people ever to have read my masterwork is unlikely to increase any time soon, I thought I’d share them with you here. First thing to note: I took this quote selection process very seriously (as is right and proper) and started noting down potential candidates fairly early in my PhD. I was determined to avoid anything commonplace, and in particular steered well clear of quotation dictionaries. Also – I only now realise – it never really occurred to me to quote a scientist, still less a scientific paper. I guess I thought that side of me would be well represented throughout the rest of my work, and I wanted these choice quotes to reflect instead my more arty, sophisticated, fancy-cocktail-and-complicated-music sensibilities.

I also need to give some context. I spent my PhD studying the phenomenon of rarity. Rarity is common: most species are extremely restricted both in terms of numbers of individuals and spatial distribution. What are the causes and consequences of this? In particular, I was interested in whether rare species are in any sense special – for instance, do their biological characteristics differ consistently from those of common species? So throughout my studies I was on red alert for any interesting use of the word ‘rare’, and especially anything that carried connotations of oddity arising as a function of being rare.

The perfect quote finally arrived in the cinema, as I was watching Terry Gilliam’s masterful interpretation of the great Hunter S. Thompson’s Fear and Loathing in Las Vegas. I had no notebook, no pen; however, I knew I had the novel at home so simply had to re-read it (always a pleasure) to find the quote, no? No. Turns out it’s not in the book; so I bought the VHS (OK, OK: I'm old) when it came out and watched it, finger poised over the pause button (and rewinding several times to make sure I’d interpreted Johnny Depp’s drawl perfectly) until I grabbed the quote:

There he goes, one of God’s own prototypes – a high-powered mutant of some kind never even considered for mass production. Too weird to live, too rare to die. Raoul Duke, Doctor of Journalism, of his Attorney

The rather odd attribution was because I was unsure if it was a Thompson original, or directly from Gilliam’s sceenplay, so I stuck with the character names. Only later did I find the original source, in The Great Shark Hunt, a collection of Thompson’s writing, where he uses it to describe his (HST, Doctor of Journalism, alter-ego: Raoul Duke) real-(if larger-than)-life attorney, Oscar Zeta Acosta.

So that was all nice and relevant to the topic of my thesis, but how should I demonstrate the true depth of my intellectual facilities? Being a bit of a francophile, I thought I should have something in French; and who better to quote than Enlightenment poster-boy Voltaire? But I didn’t want anything run-of-the-mill – nothing from Candide, say. Fortunately, I’d read a collection of Voltaire’s work, and came across this from Memnon to start my introduction:

Memnon conçut un jour le projet insensé d’être parfaitement sage. Il n’y a guère d’hommes à qui cette folie n’ait quelquefois passé par la tête. Voltaire, Memnon (ou la sagesse humaine), 1747

My French is far rustier these days, but a (very) loose translation is something like, “One day Memnon came up with the ludicrous plan of becoming perfectly wise. There are few men to whom this mad idea has not occurred, from time to time.” Seemed somehow apt.

Finally, I needed something to start the general discussion. My thesis was rather a rambling affair (the first comment of my external examiner was, “Tell me, why did you decide to write two theses…?”), and I found a gem in Francis Wheen’s terrific biography of Karl Marx. I was not trying to make a political point – although it’s hard to disagree with the sentiment of ‘from each according to his ability, to each according to his needs’ – but through Wheen’s book I had become quite fond of Marx the fallible man, especially the contradictions between his socialist ideas and his own rather upwardly-mobile social pretensions. He was quite the procrastinator too, and as a writer nearing the end of this major project, my PhD thesis – and freshly out of funding and relying on benefits and the generosity of friends – I certainly empathised with the sentiment expressed here:

The material I am working on is so damnably involved… but for all that, for all that, the thing is rapidly approaching completion. There comes a time when one has forcibly to break off. Marx, letter to Joseph Weydemeyer, 1851

I have never really stopped struggling with this. (Neither did Marx: it took a further 16 years after he wrote the above for the first volume of Das Kapital actually to appear…) Knowing when to finish something, to submit and move on, is not my greatest strength. Perhaps this is the place.

Machismo and excellence in cooking and statistics

The inevitable return to TV this week of Masterchef, after a close season shorter even than the English Premier League, has (for strange reasons that I hope nonetheless will become clear) triggered this response of sorts to Brian McGill’s post on Statistical machismo over at Dynamic Ecology last year. Brian lamented the use by ecologists of the latest ‘must use’ statistical method, which is typically complicated both to perform and (perhaps especially) to interpret, without necessarily having much of an effect on the conclusions drawn. He felt this macho posturing – as he puts it, “my paper is better because I used tougher statistics”; in Masterchef terms, “analysis doesn’t get any tougher than this” – ends up overcomplicating papers and wasting everyone’s time. I enjoyed the post at the time, and felt it raised some interesting points; and though I disagreed with the thrust of it, this was not to the extent that I felt compelled to comment, still less to respond. Now that I’ve come up with a convoluted, almost certainly over-played culinary analogy, though, I’m going to have a bash at expressing my thoughts on the matter properly.

If you watch Masterchef (especially the early rounds) you’ll probably see a great deal of culinary machismo. Even if you don’t, you probably know what I mean: food prepared by someone who is a decent chef, but a pretty awful cook. Smears of jus and droplets of fluid gel on big white plates, but the chicken’s raw; burnt chips in a flowerpot; spun sugar on a duff dessert. Contrast this with what a good non-cheffy cook might produce: a really excellent, well seasoned, ugly stew; a pudding that tastes sublime but looks like a car crash. When I lived in Thornton le Clay near York, our pub specialised in the latter: fantastic, simple, pub food, cooked to perfection with no pretension (it's unfair on them to suggest it was ugly, but the emphasis was on flavour not prettiness). Next village we lived, the pub was very gastro, and the food – though twice the price, and served on wooden boards as likely as not – was nowhere near as good.

This, I think (bear with me!), is similar to the issue that Brian raises. In particular, the use of advanced techniques – statistical or culinary – without having mastered the basics, indeed without even considering the basics, reeks of posturing. In these cases, I agree, we should beware.

Consider for example something like Generalised Linear Mixed Effects Models (GLMMs) as a statistical equivalent of nitro-poached aperitifs or popping candy cheesecake. I am very wary of GLMMs. Ben Boelker’s TREE paper on them basically says as much: do not go here unless you really know what you’re doing. As a minimum, you ought to have mastered the basic component techniques of GLMs and LMMs (and naturally, you need to know your LMs for either of them). Yet I see students who describe themselves as ‘not very confident’ at statistics merrily fitting GLMMs with no clear idea of what model they’ve fitted, or why. Not machismo in this case, but rather blindly following a statistical recipe which demands a great deal more skill than their current aptitude allows.

So yes, in these kinds of cases – and similarly in some of the others Brian mentions – doing a simple analysis well is probably preferable to making a dogs dinner out of a complicated one.

And yet…

Let’s stretch this analogy further. If you really want perfect chips, you’ll triple cook them. Liquid nitrogen really does make excellent ice cream. The way to ensure your meat is exactly à point every time is to cook it sous vide in a water bath. Simply put: some methods of cooking are better than others, and if you can master a Blumenthal-esque skillset, the resulting food will be objectively, qualitatively superior to the lovely, hearty stuff I used to eat in my local, or that I aim to cook at home.

In the same way, some methods of statistical analysis are simply better than others. Brian’s post mentions phylogenetic correction, for example, complaining that it hardly ever affects the result of an analysis, yet entails a great deal of work and additional assumptions. Well perhaps (and his point about errors in phylogenies is a good one), and of course you can fluke the ‘correct’ result with simple statistics, just as you can fluke excellent food with a less scientific approach than that employed by the molecular gastronomists. But if you want consistent excellence – if you want to do something right – you use the best available methods.

Specifically regarding the inclusion of phylogenies in comparative analyses, it’s largely immaterial in my view whether or not this has a large effect on your results; rather it’s simply sensible to consider evolutionary processes when you're modelling a pattern which is the result of evolution. This point is nicely made in a new paper in Methods in Ecology and Evolution by Hernández et al., in which they make a plea for moving beyond phylogenies as ‘statistical fix’ (i.e., ‘phylogenetic correction’) and embracing instead a fully evolutionary view of macroecology in which we test mechanistic hypotheses rather than just describing patterns. (One could of course make a similar case for including spatial processes.)

The cooking/statistics analogy breaks down in one important aspect, however: there are very good reasons why you might not even attempt to master those fancy cooking skills. I read the Fat Duck cookbook much as I might read an account of the building of a great cathedral: full of admiration for the skill, craftmanship and effort involved, but with no intention of even attempting to replicate the endeavour. Blumethal’s Pot roast loin of pork, braised belly, gratin of truffled macaroni, for instance, includes 74 incredients, including two separate stocks (a further 24 ingredients and several hours of prep time), and requires nine separate procedures to produce a single course. You (or at least, I) would never do that to feed two at home; it is only feasible at a restaurant scale. Even those recipes that look technically manageable need expensive equipment, putting them well out the reach of the home cook, who might be better advised to concentrate on mastering more simple skills.

Developing beyond being a good ‘home statistician’ – mastering the essentials of analysis – on the other hand, requires none of this expense. Unlike haute cuisine, mastering statistics – especially in the age of R – is free. We have no excuse not to master the best available methods. So you maybe should roll up your sleeves and chase that Michelin (Fisher? Pearson? Gaussian?) Star after all. Not because you feel you have to in order to show off – I’m with Brian there – but because doing things right is important.