Quantitative

Quantitative

For me ‘ah ha’ moments fall into one of two types. There are the ‘oh why didn’t I think of that before’ kind of ah ha’s that tickle the brain when they happen but often fade into the nether regions of forgetfulness soon after.

Then there are the real ‘ah ha’s’, the kind that are arresting, stick around, and may even shift my perception of the world.

Recently I experienced one of the latter, a real doozy.

In a meeting with colleagues who, between them, had over a 100 years of environmental experience I realised that none of them understood numbers. They did not think quantitatively.

It worth taking a moment to absorb this observation. Eight experienced professionals who most would describe as technical experts, all with a tertiary education and many years of practice with problem solving in land management, native vegetation and agriculture, were not thinking numerically.

Few of them would admit to this of course. They’ll pour over spreadsheets, examine graphs and even contemplate statistics alongside the best of their ilk, but deep down they are not thinking numbers.

Instead they shift words and documents around. They think in the language of processes and procedures not likelihood, rates, and difference.

As many a post on this blog attests, my brain handles proportions and probabilities.

However, I am not especially mathematical, and often lament a lack of fluency in that language. But limited math literacy does not stop me thinking numbers. I’ll see a proportion and instantly ask “proportion of what, a thousand or a million?” In my head 20% of 10 is not the same as 20% of 1,000,000 when it’s, let’s say, greenhouse gas emissions. There is materially in the latter number even though the proportions are the same. It seems impossible not to do this numerical reality checking when faced with the variability in space and time of the matters environmental people are interested in.

But there it was, plain as a binomial distribution. My colleagues were not quants. When they relaxed into an innate thinking state, they did not see the world quantitatively.

Now before the trolls get too upset, this ‘ah ha’ is not about belittling or downgrading all the feeling thoughts, the creative thinker or even the normative types. All problems are best tackled with a variety of thought processes and the best answers do not always come from understanding a likelihood. What got me was that the quantitative type was not in the very building you would expect to find it.

For a scientist, researcher and one time lecturer in biostatistics this is a hard one to fathom. The question still bouncing around like a subatomic particle is why? There is no obvious reason, other than the peculiar quirks of chance, that none of these people were quantitative.

Only they were not and soon the consequences started to come up. Any talk of likelihood, rates, and difference would not be fully understood without explanations and time to digest what the numbers mean.

It would not be possible to just present a graphic and assume that everyone would understand any obvious pattern, let alone the nuance.

In short, my colleagues were not going to have an easy handle on inference.

This is a huge deal. If the people who are closest to the facts as they play out in the real world do not get the numbers, the same people who support decisions around sustainability and the trade-offs with natural resource use… Well, there is a good chance we are in muppetville all over again.

Ah ha.