#73 - Being less wrong

A primer on thinking in probabilities

Hello, readers! Welcome to issue #73 of Curiosity > Certainty 👏

We can see being wrong as a chance to expose what we don’t know and to get better OR as we often do, see it as a test score that will close doors for us if we aren’t good enough. Practicing the former helps to learn and, dare I say, stay happier. That’s why this week’s edition is all about being less wrong.

Why don’t I call it being more right, you may ask. Being less wrong is an exercise in humility—there’s a lot more we don’t know about than we do know.

Being less wrong

Just one question can tell you a lot about a person.

I’m not talking about an interview question. In fact, the question I’m going to ask is probably going to make you raise an eyebrow or two. And you’re not even obliged to answer it. But it has the power to reveal a lot about you.

How much does the elephant in the picture weigh? You can pick your unit–kgs or lbs.

We’re embarrassed about being wrong. So we tend to say ‘I don’t know’ when we don’t want to make a wrong guess. 

Or we offer a precise estimate as if we have the exact information.

When we don’t have all the information and we’re asked to guess, we switch between saying ‘I have no idea’ AND offering a bull’s-eye answer. 

We alternate between underselling and overselling what we know. Both can be poor ways to get better at predicting outcomes.

Here’s the truth: most of the time, we do have an idea. It is not nothing, but it is also not everything. 

A good practice against uncertainty is to try and build a target range, instead of jumping to an overly exact number.

Now, if you’ve to guess the weight of this elephant, saying ‘I don’t know’ means you’re ignoring what you already know. You already know your weight and the fact that an elephant–any elephant–weighs more than you do. You already have a lower bound. 

You can then home in on an upper bound that factors the size of the elephant shown. You probably know there aren’t land mammals bigger than an elephant, and you imagine that any elephant is lighter than a blue whale. You can base these as references to guesstimate the upper bound. Once you’ve put some thought into defining the range of values for the elephant’s weight, you’ve already improved your chances of being less wrong.

Being less wrong may seem like an unexceptional goal. But in life being less wrong matters. It can be the difference between losing some and all of your savings, between one and all of your limbs, between blowing one and all chances.

As a child, I used to drive my folks crazy by quizzing them about animal weights and who would come out trumps in an inter-species face-off (hippo versus rhino!). 

I was mostly rebuffed. If my child shows any such tendency, I’ll make it a point to teach her the art of building an educated guess.

Our imagination tends to be captured by an all-or-nothing syndrome. We forget there’s an entire range of hits from the edge to the bull’s eye. 

Forget about the bull’s eye at first. Work on getting closer to it, step by step. Be less wrong.

You can go far with blunt instruments

We tend to think absolutely, in all-or-nothing terms. 

But thinking in probabilities moves us from the embarrassment of not knowing the right answer to preparing for a range of futures. A more accurate estimation of the future happens when we improve the quality and quantity of what we know.

This is important because we know so little and if what we know is mistaken but we don’t realize it, then we really don’t have a clue about our ignorance. It’s worse than not knowing. 

So, first, we must repair the cracks in the ‘what we already know’ pile. 

Next, we must increase the size of the pile. 

How do we do that? 

By making bets about the future. By estimating the likelihood of something happening in the future and then checking our guess against reality. Estimating is nothing but building a target and taking aim.

A good way to start is to use common terms to express likelihood. 

Words like rarely, usually, unlikely, maybe are imprecise. In that sense, they are blunt instruments. But we use them every day and we’ve a certain idea of what they mean. Even if not everyone can settle on a common mathematical value for usually and rarely, they are still more accurate than always and never. Which is what we otherwise use.

A blunt instrument is better than nothing. Even if you have only them, you have more than what most do. Probabilistic thinking announces your uncertainty and gets people to correct you if they think you’re off the mark. That is what improves the quality of your decisions.

Handling precision instruments

On January 10, 1961, the New York Times outed on its front page that the CIA was training Cuban exiles to launch a guerilla war against the new Fidel Castro-led government in Cuba. The headline should have chucked this covert plan of the John F Kennedy-led US government to the dustbin. Only it didn’t.

Three months later, 1400 of the so-called guerillas landed to find 20,000 of the Cuban army waiting. In three days, all were dead or imprisoned.

In the days leading up to the infamous Bay of Pigs invasion in April 1961, when asked for an assessment, the Joint Chiefs of Staff told President Kennedy the US had a ‘fair chance’ of success. Kennedy read fair as substantial. Buoyed by the positive recommendation, he greenlit the mission. It was only after the fiasco that he came to know that his advisors had meant a 1 in 4 chance. 

A hammer is better than nothing. But a chisel is better than a hammer if what you’re building is of significance.

One of the problems with expressing probabilities using natural language terms (like fair chance!) is that not everyone agrees on what they mean. Andrew and Michael Mauboussin surveyed 1700 of the general public on their use of common terms to indicate likelihood. When asked to express each term (usually, likely, rarely, etc.) as a percentage chance between 0% and 100%, people revealed big spreads for the same common terms. On top of that, women tended to interpret possibilities more optimistically than men.

Clearly, President Kennedy and his advisors wouldn’t have appeared in the same respondent segment.

Tagging a percentage range instead of common terms makes explicit your thinking for anyone else to comment on. It avoids a ‘fair chance’ scenario. Some of us may not be comfortable using numbers–that’s how common terms were born. For such people, decision strategist Annie Duke suggests a reframing of the question: ‘For me to use this term to describe the likelihood of an outcome, how many times out of a hundred do I think that outcome would be the one that happens?’

In summary, once you’ve dropped your hesitation to guess, use common terms to express chance. Once you’ve grown comfortable with the idea of thinking in probabilities, talk in percentages. Make it clear where you stand. Give yourself a chance to be corrected.

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Thank you for your time! If you try any of the shared strategies, I would love to know how that works out for you. Also, let me know how I can make this newsletter more useful for you. Comments are open, so is my inbox (satyajit.07@gmail.com). Stay well!