You will be able to describe a decision as a bet on an uncertain future, as Annie Duke does in Thinking in Bets.
Siti, an HR business partner at a regional logistics company, is asked by her director whether the new hybrid policy will cut resignations. She says, "I think it should help." Her director says, "Good," and moves on. Six months later, resignations are about the same. Was Siti wrong? It is hard to say. "Should help" could mean almost certain or barely more likely than not, and neither of them knows which one she meant.
This module is about replacing statements like "should help" with something clearer, and using that clarity to compare choices. It starts with a way of seeing decisions that the professional poker player turned decision writer Annie Duke sets out in her book Thinking in Bets.
Duke's central argument is that almost every decision is a bet. When you choose one option over another, you are betting that it will turn out better than the alternatives. You make that bet with incomplete information, because you cannot know the future, and with luck playing some part in how it turns out.
Poker made this obvious to her. A poker player never knows the other players' cards. They decide with what they can see, an estimate of the odds and some sense of the other players, and then the cards fall. A good decision can lose a hand, and a bad one can win it.
Duke's point is that life works the same way. Taking a job is a bet that it will suit you better than staying. Hiring a candidate is a bet on how they will perform. Siti's hybrid policy is a bet that it will keep more people than the old one. None of these come with certainty. Seeing them as bets does two useful things: it reminds you that you are working with probabilities, and it separates the quality of the decision from the luck of the outcome, which Module 8 builds on.
If decisions are bets, it helps to say how sure you are. Words like "probably", "likely" and "should" feel clear but mean very different things to different people. The American intelligence analyst Sherman Kent noticed this decades ago, when colleagues who had agreed on the wording of a report turned out to mean quite different odds by the same phrase.
A percentage fixes that. "I think there's about a 60 percent chance resignations fall noticeably over the next six months" tells the director something "it should help" did not. She now knows Siti thinks it is better than even, but far from certain. If the director needs more confidence than that before spending money on the policy, she can say so, and they can talk about what would raise it.
A number also makes your thinking clearer to you. Putting 60 percent on something forces you to ask why not 80, and why not 40. The answers are your reasons, and you may find some of them are weaker than you thought.
Your numbers do not have to be precise. Nobody can tell the difference between 62 and 65 percent on a question like this. Rough bands are fine: around 10, 30, 50, 70 or 90 percent. The point is to be clear about the size of your confidence, not to pretend to accuracy you do not have.
Duke describes a simple question that changes conversations: "Wanna bet?" When someone states something confidently and is asked whether they would bet on it, they often hesitate, and then soften the claim. Being asked to put something at stake makes people check how sure they really are.
You do not need to make actual bets with colleagues. You can ask yourself the question silently. "I'm sure the client will sign this month." Would I bet a month's salary on it? A week's? Lunch? The amount you would be willing to risk is a quick read of your real confidence, and it is often lower than the confidence in your voice.
In a team, a gentle version works well: "If you had to put a number on it, how likely is that?" It invites a probability without suggesting the person is wrong.
Very few things at work, or in life, have a probability of exactly zero or exactly a hundred percent. Projects that are "definitely on track" slip. Clients who "would never leave" leave. Candidates who are "a sure thing" turn down the offer.
Saying 90 percent instead of "definitely" is not a lack of conviction. It is accurate. It leaves room for the one time in ten when things go the other way, and it means that when that happens, you have not been proved wrong. You said it could. This connects to lesson 1.3, Qualifiers and rebuttals make a claim stronger: a probability is the most precise qualifier there is.
There is a trap in the other direction too. Saying 50 percent about everything is a way of never being wrong, and it tells nobody anything. Commit to the number you actually believe, even when it is uncomfortable.
Siti tries again at her next one-to-one: "I'd put it at about 60 percent that resignations fall by a noticeable amount within six months, mostly because exit interviews mention commuting a lot. I'd go higher if the pilot team's numbers hold up." Her director now knows exactly what she thinks and why.
Making predictions with numbers takes practice, and you only get better at it if you check them later. The activity below starts your first set, and lesson 7.4 will bring you back to them.
Write five predictions about the next three months at work, each with a probability, and set a reminder to check them.
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