Absolute versus Functional Truth
"Does how tall you are matter in dating?" and other stupid arguments at work
One of the most common causes of debate, in companies and in life, comes from a misunderstanding of the difference between Absolute and Functional truths. This holiday’s example: does how tall you are matter in dating?
It all started because my very tall (6’4) friend pointed out that he has had an easier time on dating apps than some of his shorter friends. The ever-entertaining Shiv Ramdas then weighed in and argued that “height is basically irrelevant like 95% of the time. I'm well under 6' but I have a personality and a sense of humour and dating apps were like a dream.”
In terms of Absolute Truth, Shiv is wrong: it is absolutely true that height affects success on dating apps at much greater probabilities than 5%. This is beyond argument and supported by massive quantities of evidence across different samples, apps, etc. And people expressing absolute truths will often refer to the robustness of the evidence, much as a statistician or scientist would.
Because that is what an absolute truth is: a fact that can be proven through overwhelming, cross-validating evidence.
But in terms of Functional Truth, Shiv is right: no matter what height you are, you can find someone to love and be loved by if you lean in to characteristics that aren’t your height. He even expresses it in the way you often see people talk about functional truths: through lived experience, either their own or someone else's.
And that makes sense, because functional truths are actually recommendations for how to approach the world. It can be absolutely true that height matters in dating and functionally true that you can’t change your height and so should focus on other things.
I see these sorts of debates all the time in companies, typically between business/design stakeholders and their engineering/data science counterparts, with product trying frantically to resolve them. And they can mostly be avoided with an understanding of the difference between functional and absolute truths, plus a few linguistic tricks.
Absolute truths as they relate to humans are generally probabilistic, not deterministic. For example, “Tall men have an easier time getting a date than short men” is an absolutely true, probabilistic statement but it sounds deterministic.
To make it easier to understand as probabilistic, let’s add some modifiers. “In general, most tall men have an easier time getting a date than many short men.” In general, many, and most do a ton of work here to make this easier to see as a probability.
Because that is where functional truth comes in. The relationship between height and dating, while real, is not perfect: X influences Y, but X doesn’t equal Y. So you can be a short man and still have a fantastic love life.
It is important to recognize absolute truths, because evidence matters and if you’re a dating app, you might want to do things to downplay height to make a more level playing field (and yes, that was totally intentional; play dad games, get dad jokes). Acknowledging biases can help us build a more equitable world.
At the same time, embracing functional truths can also help with equity. It is absolutely true that intelligence is partially influenced by inherited genes. But the functional truth is that most people are smart enough that with effort, they can do most things. So even if it is absolutely true that it is easier or harder for people, we should treat people as capable and design systems to enhance capability across the board.
If you’re stuck in one of these debates at work, ask a simple question: “NAME, are you saying that is absolutely true or functionally true?” This is incredibly important, because it recognizes the validity of both approaches.
As a scientist, it is very important to me that you recognize the evidence that supports an absolute truth, because otherwise anti-science folks will literally ruin the world (*cough cough* anti-vaxxers *cough cough*).
But as a business leader, I get it: sometimes we need to do things for other reasons and to compromise for operational necessities. And that’s valid too, because scientist-me wants progress, which is what I’m in an applied field.
And if you ask that question and people look at you like you’re crazy, you can give them this article to read. Or explain it yourself. But like so many things in applied behavioral science, teaching people to use this language can avoid many of the unproductive debates that slow down our progress.
