The Myth of the Sure Bet: Why You Shouldn’t Be Afraid of Imperfect Data

I have many friends who are making huge decisions right now.  They’re all brilliant and big-hearted people, so they will not make a bad decision.  Their decision-making practices have gotten them this far, and they have fantastic lives.  It got me thinking, though; I haven’t made any huge decisions in a while.  None I’ve struggled with, anyway.  My last big decision was marrying Mo, which wasn’t a decision.  I knew I wanted to marry him even before he asked me, so his proposal was more of an, I don’t know, progression of a process than it was an actual decision.  Starting my coaching business wasn’t really a decision either.  I learned a skill set that helped me immensely, and I wanted to help people in my position feel better, not just in their jobs but their lives.  That was another progression of a process.  Maybe the similarity with both is that I felt I couldn’t lose.  I thought chances were much better that Mo and I would make it than that we wouldn’t, but if we got married and the relationship didn’t last for some reason, I knew I’d be okay.  I’d be very, very sad, but I wouldn’t, like, die.  I love the coaching practice I’m building and know I’m helping people.  But if I can’t keep it going for some reason, I’ll be disappointed and lose some money, but again, I won’t die.  But what if the decisions we face aren’t sure bets?

I think that’s what many of my friends have been dealing with this year.  Changing jobs.  Moving across the country.  Getting divorced.  Coming out.  Starting a family.  Having a basically experimental medical treatment.  Caring for aging parents (or grandparents).  I don’t think I’m going out on a limb when I say people in medicine tend to be very analytical and profoundly respect data.  I remember making an Excel spreadsheet to determine how to make my residency rank list.  Every program in column A and then all of the things I thought mattered filling subsequent columns: number of trainees, salary, benefits, friends who may end up in the same city, access to flights back to my hometown, rank of the program’s city on the “Pick My Spot” website (a vast survey that claimed to match you with your perfect city based on your personality, but was probably just a huge, rigged real estate ad now that I think about it).  That spreadsheet made me feel like I was really taking my rank seriously.  Analytics.  Data.  Objectivity.  But once I got down to the top four-ish choices, everything in those programs’ columns was kind of a wash.  There wasn’t a sure bet.  There wasn’t a 100% correct answer.  I’d done weeks of work, all of the information-gathering that I possibly could, and didn’t come up with an answer.  The whole exercise was so disheartening that I didn’t even bother with a spreadsheet when I put in my rank list for fellowship or when I was choosing my first faculty position.

The commitment to systems and analytics we have to practice medicine can hamstring us when functioning in the real world.  We overthink, over-plan, and over-Excel and then get into analysis paralysis: we would rather make no choice than make the “wrong” one.  So we stay stuck, only knowing that we’re not happy where we are but unable to change our circumstances.  There’s a massive opportunity for self-compassion there.  Nothing wrong has happened when we’re stuck in an unhappy place.  Our brain is just keeping us safe.  Even when we’re dissatisfied with the status quo, the status quo is known.  And what is known is comfortable, while what is unknown is dangerous.  So our brains keep us stuck, spinning in indecision, to keep us safe.  Nothing wrong has happened.  It’s not sophisticated.  It’s just evolution.  

We must know the right answer when caring for patients (or, at least, the best answer given data that is too often imperfect), or somebody may die.  But when it comes to decisions outside of medicine, like changing jobs, moving cities, starting families, and the like, chances are that nobody will die due to the decision.  At our jobs, sometimes there is no turning back from a choice that is made, and decisions may legitimately be life or death.  But the rest of life isn’t like that.  And back to the “data that is too often imperfect” for a second.  We can take the skill set that medicine has taught us, making the best possible decision with imperfect data, and apply it to make those life-outside-of-medicine decisions.  Think there’s no sure thing with imperfect data?  You are the sure thing.  You know that whatever comes of a decision made with imperfect data, you can figure it out.

Move to a new city and find your dream neighborhood after you bought a house in what you mistakenly thought was your dream neighborhood?  Excellent.  Start strategizing what purchasing a home in the legitimate dream neighborhood will take while deliberately looking for the absolute best things about your current neighborhood.  Worry starting a family will rob you of your current lifestyle?  It will.  But by failing to choose among being a DINK, DEWK, SIWK, or DINKWAD, you’re losing out on the opportunity to start living the fullest version of who you’re becoming.  And maybe I’m not giving my decision to marry Mo its due credit.  It’s the right decision to be married to Mo because I make it the right decision.  He’s my sure bet because I’m not betting on him.  I can’t bet on him.  I can’t control his thoughts, emotions, and actions, even as much as I’d like to sometimes.  The person I’m betting on is myself.  Every day, I bet on myself to be his best partner.  And figure it out when curve balls are thrown (which they inevitably will be).  And I think he probably bets on himself to be my best partner daily and figure out curve balls too.  All of the researching, all of the planning, all of the spreadsheets, and the commonality is you.  You know what’s right for you.  You also know that when something happens because you didn’t know what you didn’t know, you can figure it out.  Talk about a sure bet.  I’d take those odds, the ones where I’m betting on you, any day of the week.