Around 89,960. That’s the number of meals we can expect to eat if we live to the age of 82. Take an average of men and women’s life expectancies (79.9 + 84.3 ÷ 2), x 3 meals a day, x 365 days a year, + 20 leap years (another 60 meals).
For I have known them all already, known them all
Have known the evenings, mornings, afternoons
I have measured out my life with coffee spoons.
Like TS Eliot’s Alfred J Prufrock, you can select pretty much any occurrence and unitise it. What does this prove exactly? Is your life worth 89,960 meals, does it contain 89,960 meals, or should you expect 89,960 meals? The answer is context-dependent. Are you starving or dieting? Are you a nutritionist or a marketing manager? Do you want to know how much food people put into their mouths or whether it tasted any good?
The bald integer 30,000 by itself tells us nothing. It’s the story behind the figures that matters and while numbers assist in the assignment of meaning, they don’t mean much by themselves.
They are – and this is both their beauty and the limit of their use – an abstraction from life, not life. It doesn’t matter if 99% of people who saw a film loved it if you, the other 1%, did not. Their enjoyment won’t change your lack of it. We can share our experiences but we can’t aggregate them.
The more singular an experience, the harder it is to abstract at all. There’s the rub. In a world awash with figures, many of them hanging tangentially off reality like a stage villain from the edge of a cliff, it’s identifying what they refer to that’s the trick.
Playing the numbers
I recently had a lesson in the existential gulf between “quant” and “qual”. Last May, I published a Currency House Platform Paper, The Retreat of Our National Drama. As part of my evidentiary strategy I put forward repertoire figures relating to nine Australian theatre companies over a 25-year period.
Even before the Paper was published the figures came in for heavy criticism. From the outside, the issue appeared to be a relatively minor one: the coding of a number of Australian plays in the “non-premiere” instead of the “premiere” category (around 3-5% of the source data). But testy phone calls were followed by testy emails, and eventually by a solicitor’s letter demanding an erratum and a public apology.
AusStage, the database responsible for the original figures, was keen to fix any mistakes. So a revision exercise was undertaken, checking the status of all possibly suspect shows (around 10% of the source data). My role involved examining the original repertoire lists – that is, going back over old play programs and deciding what was and was not an “Australian drama premiere”.
When the revision exercise was complete the results were posted on the publisher’s website, together with an Explanatory Statement from me.
I had not made egregious errors in my quantitative analysis, as some claimed, but I had made enough mistakes to give me pause for thought – and to reflect on the impoverished rule of quantitative data over our lives, the Tyranny of Numbers that insists we treat any string of figures, however removed, as “objective” and all qualitative judgements, however informed, as “anecdotal”.
Cliometrics v Melpometrics
The term “cliometrics” is used by researchers when applying statistical and econometric techniques to historical material. Risking an ugly neologism, let me coin the word “melpometrics”, after Melpomene the ancient muse of tragedy, to characterise their application to culture.
Bringing quantitative and qualitative data together is a challenge for any discipline, but particularly those looking at fields with singular outcomes. “A difference is a difference only if it makes a difference” quips Darrell Huff in his classic book How To Lie with Statistics.
I had included multiple caveats in the Paper about the dodginess of figures in relation to the theatre repertoire. But confronted by my abstract figures the companies identified in it felt anger and frustration. They do not produce numbers, after all. They produce shows.
If cliometrics is a controversial area of scholarship, melpometrics is even more so. Flitting between play programs and aggregate tables I saw close up why this is the case. Of the 200 dramas I considered, some I had attended, some I had read, and some I knew only by their media coverage. Others I had to ring around and ask about.
My knowledge of these productions was varied and variable, resting on a complex ground that took in different kinds of experiences and turned them into informed opinion. It was a messy, awkward and personal process. The results are defensible but do not constitute absolute proof. There was too much that was intangible and heterogeneous about these shows, too much life in them, to be expressed in a standardised way.
Yet this is what numbers in respect of culture often seek to do: take a complex aesthetic experience and derive from it solid-seeming “evidence” divorced from the subjective response whereby it is given meaning.
This does not have to prompt skepticism about numbers per se. There are many aspects of the world where we need quantitative analysis to guide vital decision-making. Attacking the integrity of disliked data has become an all-pervasive deflection strategy these days, akin to an earlier age’s questioning of one’s patriotism or belief in God.
But this only points up the issue at hand, which is getting off the treadmill of generating ever-more figures and instead using them to better effect. Here culture is a microcosm of the broader political domain, with its addiction to de-contextualised algorithmic thinking.
The problem is not how to lie with statistics, as Huff imagined, it is how to tell the truth with them.