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Speaker 1: Pushkin, what's your biggest fear. I'd say the biggest fear

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Speaker 1: is something a mistake that I would make that would

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Speaker 1: damage my credibility to where people would not listen to

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Speaker 1: me when there's a tornado down. James Span meteorologist, maybe

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Speaker 1: Alabama's best known person aside from some football coaches. He's

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Speaker 1: all over TV talking about the weather, especially when the

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Speaker 1: weather might kill you. So this is a tornado emergency,

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Speaker 1: but the city's at Tuscaloosa and North Fort and the

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Speaker 1: campus of the University of Alabama. James is one of

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Speaker 1: those people who's never really had a job because he

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Speaker 1: found his calling. He once stayed on the air as

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Speaker 1: he watched a tornado make straight for his own home,

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Speaker 1: pleading with people to see the risk. If you're just

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Speaker 1: joining us, This is James Span with Taylor Serrallo mainly

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Speaker 1: chicken on my wife's she's okay and she's in the

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Speaker 1: tornado shelter. Okay, go ahead, Taylor. I'm sorry. I was

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Speaker 1: put on this planet to mitigate loss of life when

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Speaker 1: their tornadoes flying around here, and I have to be

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Speaker 1: very careful in what I say and what I do,

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Speaker 1: not just on the air, but on social media. And

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Speaker 1: in real life. To build trust with his audience, James

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Speaker 1: goes to incredible links. He's published a children's book called

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Speaker 1: Benny and Chipper Prepared Not Scared. He spends time in

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Speaker 1: dollar stores talking to people because the people who shop

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Speaker 1: in dollar stores are also the people who live in

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Speaker 1: trailer homes, the sort of homes that tornadoes obliterate. He

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Speaker 1: memorizes the names of Alabamians who've died in storms, people

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Speaker 1: he might have saved. There's lots of them. On a

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Speaker 1: single day back in April twenty eleven, a line of

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Speaker 1: tornadoes in Alabama killed two hundred and fifty three people.

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Speaker 1: I know their stories, I know their family members. I've

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Speaker 1: talked to many of them, and it's very motivating for me.

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Speaker 1: And that's my main job in life. It's to make

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Speaker 1: the warning process better with severe weather. He's doing all

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Speaker 1: he can to warn people, yet people still don't understand

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Speaker 1: what he's saying. I'm Michael Lewis. Welcome back to Against

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Speaker 1: the Rules, where we explore unfairness in American life by

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Speaker 1: looking at what's happened to various characters in American life.

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Speaker 1: This season is all about experts. Today, we're going to

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Speaker 1: explore the strange thing that's happened to experts. Not all experts,

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Speaker 1: a certain percentage of them, the experts who think and

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Speaker 1: speak in probabilities, who use data to forecast the likelihood

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Speaker 1: of this or that coming to pass. The experts who

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Speaker 1: can never be perfectly certain, and who risk our wrath

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Speaker 1: because we love thinking in absolutes. James Span has been

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Speaker 1: making and explaining weather forecast for the better part of

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Speaker 1: half a century. In that time, it's kind of incredible

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Speaker 1: how much has changed. So here's in nineteen seventy eight

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Speaker 1: forecast partly sunny tomorrow with a chance of showers in

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Speaker 1: the high of eighty. That's it. So today, under the

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Speaker 1: same circumstances, I'd say we'll have a pretty good bit

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Speaker 1: of sunshine between nine and eleven o'clock. After eleven o'clock

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Speaker 1: rain is likely between eleven and one. The chance of

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Speaker 1: any one spot getting wet during that two hour window

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Speaker 1: it's about seventy five percent. It's going to rain about

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Speaker 1: a half inch in most places. There could be some thunder.

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Speaker 1: Most of that should be out of here by two thirty.

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Speaker 1: After three o'clock, you're good to go. The sun breaks

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Speaker 1: back out a temperature should peek around eighty at one o'clock,

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Speaker 1: then falling back into the seventies by four o'clock. That's

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Speaker 1: the difference in what we can do now compared to

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Speaker 1: nineteen seventy eight. It's the prince between daylight and darkness.

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Speaker 1: If you go back to the beginning of your career,

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Speaker 1: were you encouraged to speak to the audience that way, like,

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Speaker 1: we don't know that much about this, this could be wrong.

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Speaker 1: Oh no, no, no, they didn't want you to say that.

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Speaker 1: I mean, coodn't you know. Back in the seventies, this

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Speaker 1: was when TV news was coming of age and I

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Speaker 1: witness news, you know, and they wanted to be this

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Speaker 1: godlike figure, you know on television. I was scared to

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Speaker 1: communicate uncertainty because that wasn't encouraged. We were the news,

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Speaker 1: the evening news, the Ron Burgundy newscast. Weather forecasts are

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Speaker 1: inherently uncertain, the where, the when, the how much. With

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Speaker 1: the current data we have, the best you can do

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Speaker 1: is judge the odds. But the odds have gotten much

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Speaker 1: more accurate over time. Back when James span was a

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Speaker 1: young meteorologist, he knew very little but tried to sound

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Speaker 1: like he knew a lot. Now that he knows a lot,

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Speaker 1: he works hard to explain what he doesn't know. You're

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Speaker 1: giving the audience more information and more new, honest information.

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Speaker 1: So it's more demanding on the audience, right it is.

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Speaker 1: And you know I hear this all the time. I

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Speaker 1: just want to know if it's going to rain tomorrow,

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Speaker 1: and they want a yes orno. They want that deterministic forecast,

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Speaker 1: deterministic as imperfectly predictable, which is something the weather still isn't.

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Speaker 1: When James Span started out, the ten day forecast was

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Speaker 1: no better than just guessing. Now it's a lot better.

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Speaker 1: But maybe the most obvious improvement, the one people really

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Speaker 1: should notice, has been in forecasters understanding of the kind

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Speaker 1: of weather that kills people. In nineteen seventy eight, we

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Speaker 1: were using nineteen fifty seven era radar and the old

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Speaker 1: black and white printouts of radar. It looked like somebody

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Speaker 1: barsed on a piece of paper, and so warnings. In

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Speaker 1: nineteen seventy eight, let's say we had a tornado down.

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Speaker 1: We didn't really know where it was, we had an idea,

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Speaker 1: so warnings were issued by an entire county. Tornadoes, even

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Speaker 1: the big tornadoes are small and counties are huge. So

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Speaker 1: here you are warning an entire county to get into

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Speaker 1: your safe place and do something where most people didn't

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Speaker 1: need to do anything. We're today we know literally within

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Speaker 1: maybe a few city blocks of where the tornado is located. Well,

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Speaker 1: so if I'm a consumer of tornado warnings, I get

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Speaker 1: a much more precise warning, and I do I get

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Speaker 1: a more advanced warning. Am I likely to get it?

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Speaker 1: Get more more time to prepare for this thing? Yes?

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Speaker 1: They have. Average lead time here is about twelve to

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Speaker 1: fifteen minutes, and the average lead time back in the

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Speaker 1: seventies was zero to three minutes. So we've come a

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Speaker 1: long way, and we don't use counties anymore. We use small, small,

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Speaker 1: small segments of counties. Geometric shapes, polygons. Anybody that knows

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Speaker 1: James span I've said this over and over. Respect the polygon,

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Speaker 1: and if you're in it, you do something. Respect the polygon.

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Speaker 1: And if you're in the polygon, you respect the polygon.

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Speaker 1: Respect polygon. Every storm today will mean business. Respect A

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Speaker 1: James Spans superfan did a remix of his famous frame

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Speaker 1: a polygon. I love this, of course, but it also

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Speaker 1: raises a question, why respect the Polygon instead of just

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Speaker 1: respect what I say. It's weird. If the James Span

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Speaker 1: back in nineteen seventy eight had been as accurate as

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Speaker 1: James Span is now, he'd have endured hail storms of gratitude,

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Speaker 1: hurricanes of appreciation, tornadoes of awe. But that's not the

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Speaker 1: wather he now lives with. Hello friends, this is James Span.

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Speaker 1: It's time to read some mean tweets. And thanks to

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Speaker 1: all of you for sending in the mean tweets. I

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Speaker 1: really appreciate from from my heart. You cost the people

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Speaker 1: in the state millions of dollars by your boop poor

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Speaker 1: boot forecasts. I woke up today expecting snow. I blame you, James.

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Speaker 1: I got my dogs all excited for nothing. James, either

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Speaker 1: you're the worst meteorologist I've ever layer my eyes on,

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Speaker 1: or you have the worst luck at predicting the weather.

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Speaker 1: I think it's time to step down. Brother. The only

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Speaker 1: difference between James Span and every other meteorologist is that

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Speaker 1: James reads his mean tweets on the air. Just to

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Speaker 1: show you where we stand, my producer called up some

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Speaker 1: weather tweeters. Here's the kinds of things that people have

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Speaker 1: to say weather forecasting is the only job you can

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Speaker 1: have where you can be wrong fifty percent of the

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Speaker 1: time and still make thousands of dollars. If we were

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Speaker 1: wrong fifty percent in our jobs, we probably would be fired.

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Speaker 1: I know nothing about meteorologists, but I know that you

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Speaker 1: know they always wrong. I'm one of those people that

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Speaker 1: actually vainly looks at the weather forecast because nine times

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Speaker 1: attend it's different from the forecast. As technology improves, they

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Speaker 1: don't improve. The continue to be it's smine boggling. You're

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Speaker 1: going to get the hate, not necessarily because of your

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Speaker 1: missed weather forecast, but just because of who you are.

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Speaker 1: You're a you're a weather person, and you know you're

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Speaker 1: a stooge. You're a you don't deserve to be on

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Speaker 1: the planet. You shouldn't be breathing air. People have that

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Speaker 1: attitude towards weather people. Oh listen. So I cut off

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Speaker 1: a basketball game on Christmas Day in twenty fifteen, and

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Speaker 1: we had a tornado coming up on the southwest part

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Speaker 1: of the city here. It could have killed a lot

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Speaker 1: of people, So we had to cover up about twenty

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Speaker 1: twenty five minutes of that game, and nobody lost their life.

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Speaker 1: The warning system worked beautifully but this is Christmas Day.

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Speaker 1: Joy to the world, peace on earth, goodwill toward men.

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Speaker 1: The first email I got, you know what it said.

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Speaker 1: It said you should have been aborted by a coat hanger.

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Speaker 1: So this is the stuff I deal with. I mean,

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Speaker 1: I'm amazed he still goes on the air. His forecast

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Speaker 1: just keep getting better and better, but the job of

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Speaker 1: being a meteorology just keeps getting worse and worse. But

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Speaker 1: I till these young people, you know, you better have

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Speaker 1: a thick skin when you get out of here and

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Speaker 1: you get your first job, because they're going to come

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Speaker 1: after you when you found that first forecast up. Back

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Speaker 1: in an earlier season of this show, I talked about

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Speaker 1: the problem of referees and a strange phenomenon. A lot

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Speaker 1: of refs are getting better at their jobs. They have

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Speaker 1: new tools, they're better train they get better feedbacks, they

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Speaker 1: are less likely to repeat mistakes. I mean, there's just

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Speaker 1: no way that the refs in pro sports are less

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Speaker 1: accurate than they were forty years ago when there was

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Speaker 1: no replay, less training and all the refs got hired

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Speaker 1: from the same old boys club, But they didn't used

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Speaker 1: to need police escorts from the arenas. Now they do.

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Speaker 1: In December of twenty twenty one, tornadoes ripped through Kentucky.

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Speaker 1: Weather experts gave people lots of warning, So this is

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Speaker 1: just an explosive severe weather set up, and that's the

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Speaker 1: outlook that we have heading our way, especially after midnight

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Speaker 1: through about eight o'clock in the morning. We definitely need

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Speaker 1: to stay aware of the weather game. Meteorologists like this

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Speaker 1: guy on WHASTV and Louisville were better than they'd ever been.

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Speaker 1: Make sure you have a way to wake up if

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Speaker 1: a warning is issued like this one that we have.

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Speaker 1: That night in Kentucky, at least seventy seven people were killed,

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Speaker 1: more people than have ever died from a weather event

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Speaker 1: in the state's history. All people had to do to

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Speaker 1: survive was listened to the experts, and still a lot

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Speaker 1: of them didn't. I think that having data is a

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Speaker 1: really recent phenomenon, Rebecca Golden, math professor at George Mason University.

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Speaker 1: We didn't have data about how things were, we didn't

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Speaker 1: record what happened previously. Then it's only really recently that

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Speaker 1: we think maybe our lived experiences could be in part

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Speaker 1: based on something probabilistic, Like a lot of people who

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Speaker 1: are good at math Rebecca noticed the confusion and wrongheadedness

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Speaker 1: of people who weren't. She also noticed that even when

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Speaker 1: statistics and these new big piles of data were properly explained,

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Speaker 1: people didn't really grasp their meaning. People have a hard

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Speaker 1: time being convinced by data. It's just that they don't

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Speaker 1: think that their experiences is in line with that data,

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Speaker 1: and so they dismiss it, or they have other experiences

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Speaker 1: that tell them that there are reasons to be skeptical

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Speaker 1: of the source of that data or the source of

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Speaker 1: the statements that are relying on the data. The problem

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Speaker 1: isn't just in the quality of the information we have

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Speaker 1: access to. It's in the way we make sense of

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Speaker 1: the world. There's a very large segment of the population

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Speaker 1: who are really struggle with basic mathematics. So people are

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Speaker 1: making mistakes because they don't think in probabilities. I think

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Speaker 1: that's right. Rebecca actually helped to start an organization called

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Speaker 1: stats to expose the statistical mistakes made by journalists. She

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Speaker 1: the public would see the world more clearly. Eventually, she

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Speaker 1: decided she was wasting her time because there was this

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Speaker 1: bigger problem how people comprehend statistics, even when they're accurate.

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Speaker 1: Why is it that people don't think in probabilities, like

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Speaker 1: the world's probabilistic. Why are our minds so deterministic? It's

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Speaker 1: kind of a philosophical question. I think we're hardwired to believe.

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Speaker 1: I think it helps us make decisions without being stressed

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Speaker 1: about those decisions. It helps us act with certainty and

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Speaker 1: make decisions so we don't hesitate too much and think

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Speaker 1: too hard. In the savannah, we don't say that's probably

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Speaker 1: a lion, right, we just run. But to just run

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Speaker 1: is less and less a viable way to move through

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Speaker 1: the world world because this relatively new thing called data

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Speaker 1: has given us a far shrewder alternative. Everywhere you turn,

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Speaker 1: you find someone analyzing data to generate the same sort

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Speaker 1: of probabilistic understanding of the world that weather people do.

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Speaker 1: I kind of come from this world of like, you know,

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Speaker 1: kind of quantz and like baseball geeks and like poker players.

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Speaker 1: That's Nate Silver. He got swept up in the nineteen

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Speaker 1: nineties by the statistical revolution in baseball. And I think

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Speaker 1: I'm kind of like one of the relatively people who's

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Speaker 1: kind of escaped, so to speak, from that world in

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Speaker 1: the like mainstream society. Back in two thousand and seven,

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Speaker 1: Nate quit forecasting the future of young baseball players. He

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Speaker 1: began to forecast elections instead. It's sort of what you're

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Speaker 1: doing is actually accepting the possibility that maybe you can

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Speaker 1: predict something that's right. But yeah, it's like kind of

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Speaker 1: like saying, hey, look, we built an audience for this

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Speaker 1: in in baseball, and so politics is still in the

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Speaker 1: Stone Age, and so there must be kind of an

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Speaker 1: audience for some politics too. When you turn your attention

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Speaker 1: to politics, at what point are you aware that the

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Speaker 1: expertise in political forecasting is sort of limited, that there's

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Speaker 1: kind of an opportunity. I mean I had an intuition

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Speaker 1: from that from the very beginning in politics, I mean,

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Speaker 1: the campaigns have to be fairly smart and data driven

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Speaker 1: about they were targeting. But like, but the media was

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Speaker 1: all about kind of narratives. It was really quite bad

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Speaker 1: in two thousand and eight. Right. It's really like a

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Speaker 1: bunch of like, you know, old white men getting together

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Speaker 1: and kind of deciding based on, you know, what their

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Speaker 1: friends think, kind of what the narratives should be in

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Speaker 1: the presidential primaries of two thousand and eight, Nate Silver

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Speaker 1: gave an upstart senator named Barack Obama a much better

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Speaker 1: chance than most everyone else did. In the general election.

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Speaker 1: He nailed not just the outcome, but the result in

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Speaker 1: every state, plus the precise number of votes Obama received

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Speaker 1: in the Electoral College. People paid more attention to what

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Speaker 1: Nate had done than how he had done it. He'd

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Speaker 1: simply use polling data rather than his gut or some

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Speaker 1: anecdote about some Iowa farmer. The polling data might not

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Speaker 1: be perfect, but it was better than every other source

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Speaker 1: of information, and they never made outright predictions. He issued

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Speaker 1: political forecasts like weather forecasts, with probabilities attached to them.

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Speaker 1: Going into election day of two thousand and eight, he'd

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Speaker 1: given Obama a ninety point nine percent chance of winning.

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Speaker 1: I mean, the irather thing about it is like like

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Speaker 1: there was always a chance that we would be wrong,

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Speaker 1: you know what I mean, and probably never heard from

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Speaker 1: politics again, potentially. Instead, Nate became basically overnight the country's

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Speaker 1: leading political forecaster because his expert piece was superior to

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Speaker 1: the storytelling it replaced. Nate Silver is his name, fortune

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Speaker 1: telling is his game. He's a celebrity statistician. Please welcome

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Speaker 1: Nate Silver. That's right, Nate Silver's the good will hunting

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Speaker 1: of political prognosticasia. There's a difference between weather forecasting or

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Speaker 1: sports statistics and politics, a difference more of degree than kind,

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Speaker 1: but still a difference. The people who celebrated Nate Silver

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Speaker 1: really really didn't understand how to judge him. His better

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Speaker 1: insights into pulling data had allowed him to see that

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Speaker 1: Obama was basically always doing better than political pundits thought

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Speaker 1: he was doing, but there was still no law that

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Speaker 1: said Obama had to win. Polling data is a bit

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Speaker 1: like the data that card counters get in blackjack. It's

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Speaker 1: a lot better than having no data at all. It

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Speaker 1: helps you to predict what comes next, but even card

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Speaker 1: counters lose lots of hands. And here we go, ladies

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Speaker 1: and gentlemen, welcome to Decision Night in America. Here at

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Speaker 1: NBC's Democracy Point, which brings us to two thou sixteen.

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Speaker 1: Nate Silver now had an enormous following. Once again, the

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Speaker 1: pundits gave Hillary Clinton better odds than the polls. On

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Speaker 1: election day, Nate gave Donald Trump a roughly thirty percent

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Speaker 1: chance of winning at the time that was a radical call.

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Speaker 1: A few traditional pundits thought Trump had that much of

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Speaker 1: a shot. Yeah, I guess question, guys, are we post

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Speaker 1: Nate Silver, are we pulled out? Well? They've been wrong,

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Speaker 1: not only just wrong, they're just they're superfluous. And at

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Speaker 1: the point where they just that's when you kind of

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Speaker 1: begin to realize that, like, the way you define success

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Speaker 1: and the way other people look at your forecast as

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Speaker 1: being successful are very different. And also because it wasn't

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Speaker 1: just that, like we got criticized after twenty sixteen for

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Speaker 1: having quote unquote been wrong, it was also in the

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Speaker 1: roup twentyteen people were actually mad at us for not

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Speaker 1: being confident enough in Clinton's chances. Right, Nate never claimed

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Speaker 1: to have some mystical ability to call a presidential election,

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Speaker 1: and assigning probabilities is not the same as taking sides.

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Speaker 1: Yelling at him for saying that Donald Trump had a

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Speaker 1: thirty percent chance of winning was like being mad at

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Speaker 1: the weather man for saying there was a thirty percent

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Speaker 1: chance of rain and then getting mad all over again

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Speaker 1: after it rains. I'm gonna get myself in a little

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Speaker 1: bit of trouble for saying this, right, But like people

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Speaker 1: like me really care about being right quote unquote for

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Speaker 1: the intrinsic value of like making a good forecast as

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Speaker 1: opposed to like influencing the narrative, if you will, Okay,

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Speaker 1: so I would love because it would educate me. How

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Speaker 1: do you evaluate a probabilistic a forecast? What's the right

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Speaker 1: way for people to judge Nate's Silver Expert. The right

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Speaker 1: way is if you take a whole bunch of forecasts

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Speaker 1: that we've made and look at how they've done collectively. Right, So,

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Speaker 1: let's say you made one hundred forecasts where the favorite

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Speaker 1: had a seventy percent chance of winning. Look at that

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Speaker 1: group of forecasts, and was it true that the favorite

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Speaker 1: actually won about seventy percent at a time? Right? The

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Speaker 1: slip side of that is that like it does mean that, like,

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Speaker 1: you can tell very little from anyone prediction. I mean,

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Speaker 1: unless you're like, unless you're very very close to one

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Speaker 1: hundred zero percent, right, then one prediction alone won't tell

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Speaker 1: you that much. Experts have gotten better, but they've also

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Speaker 1: gotten harder to judge, so hard that you need an

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Speaker 1: expert to judge them. And that's a problem, right, I Mean,

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Speaker 1: who's going to go to the trouble of evaluating hundreds

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Speaker 1: of Nate Silver's forecasts. And while it's true that he's

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Speaker 1: made thousands of election forecasts, he hasn't made thousands of

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Speaker 1: forecasts for presidential elections. Most people don't even think about

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Speaker 1: elections or forecasts or anything else the way Nate Silver does.

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Speaker 1: Most people don't even speak his language. I actually think

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Speaker 1: that the word uncertainty is used in English in a

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Speaker 1: very different way than uncertainty is used in statistics. Rebecca

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Speaker 1: Golden again, So when we talk about uncertainty and statistics,

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Speaker 1: we might say something about a confidence interval, or we

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Speaker 1: might use a pee value. I'm not really sure you

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Speaker 1: want this on your podcast, Like, maybe that's a little

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Speaker 1: bit too technical. It might be better to trying to

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Speaker 1: think of how it might be better to talk about

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Speaker 1: uncertainty for your Well, this is the root of the matter. So,

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Speaker 1: because it's not just my podcast listeners who are cut

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Speaker 1: above the average human beings, it's like, how the American

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Speaker 1: public understands uncertainty? How do you convey it? I think

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Speaker 1: the best way to talk about it is to actually

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Speaker 1: put it with specific numbers, Like instead of talking percentages,

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Speaker 1: let's talk about numbers instead of ten percent, say one

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Speaker 1: in ten, that kind of thing. But there's a much

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Speaker 1: bigger problem behind all this, an emotional problem. It comes

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Speaker 1: from us wanting certainty in situations where certainty just doesn't exist.

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Speaker 1: If the weatherman says is an eighty percent chance of

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Speaker 1: rain and it doesn't rain, the people on the receiving

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Speaker 1: end of the forecast don't say, oh, that was one

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Speaker 1: of the twenty percent of that was one of the

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Speaker 1: times when it wasn't going to right. They say the

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Speaker 1: expert doesn't know what he's talking about. So the inability

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Speaker 1: to think in terms of probabilities also becomes an inability

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Speaker 1: to evaluate the experts. There's a huge amount of inability

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Speaker 1: to evaluate who is an expert, and yeah, it costs lives,

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Speaker 1: it really does. A new kind of expert appears on

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Speaker 1: the scene, an expert who works with these new big

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Speaker 1: piles of data, an expert who thinks improbabilities, an expert

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Speaker 1: who admits to being uncertain. These new experts are clearly

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Speaker 1: better than the experts they replaced, and yet people treat

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Speaker 1: them as if they're worse and neglect their advice, even

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Speaker 1: when their lives depend on it. We who depend on

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Speaker 1: the experts still want them to have a definitive answer.

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Speaker 1: Either it will reign or it won't. Trump either will

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Speaker 1: win or he won't. But that's not the nature of

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Speaker 1: the world we live in, and we're having some trouble

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Speaker 1: accepting that fact. The more you look for it, the

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Speaker 1: more you see this problem. We've been talking about the

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Speaker 1: problem of the experts getting better yet being treated as

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Speaker 1: if they've gotten worse, a problem that leads to a

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Speaker 1: lot of mystifying behavior, like what's gone on in the

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Speaker 1: past two years inside the American healthcare system. I definitely

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Speaker 1: saw a lot of this coming. Alison Fearing is a

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Speaker 1: nurse at Rush Copley Medical Center in Aurora, Illinois. I

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Speaker 1: can't tell you how many times I've had somebody coming

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Speaker 1: and say I have this because I read X, Y

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Speaker 1: and Z on WebMD, so I know that that's what's

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Speaker 1: going on, and it's like, well, there's a lot more

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Speaker 1: that goes into it that we need to work up further,

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Speaker 1: because there are also other things that this could be

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Speaker 1: and we won't know this until we diagnose it with

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Speaker 1: lab work or a cat scan or whatever. Has this

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Speaker 1: been going on your whole career? I would say it's

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Speaker 1: definitely gotten worse over the past five years or so.

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Speaker 1: I think prior to that there was a bit of it,

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Speaker 1: but definitely not to the extent that there is now.

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Speaker 1: Modern medicine is one of the great miracles of our age.

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Speaker 1: If you went to a doctor in the nineteenth century,

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Speaker 1: he was more likely to kill you than cure you.

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Speaker 1: Now he's vastly more likely to cure you, and the

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Speaker 1: odds of that get better with each passing day. Do

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Speaker 1: you think there's anything that, if it happened to me

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Speaker 1: that required me to go to the emergency room, that

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Speaker 1: I'd be better off forty years ago than now. Honestly, no,

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Speaker 1: I really can't think of a single thing, just because

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Speaker 1: we have so much technology. At first glance, she's not

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Speaker 1: really like James Span or Nate Silver. Doctors and nurses

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Speaker 1: don't usually speak in probabilities, but her expertise is essentially probabilistic.

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Speaker 1: Behind her is a world of medical science that's calculating

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Speaker 1: the odds all the time, the odds that you have

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Speaker 1: this disease as opposed to that one, the odds that

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Speaker 1: this treatment will work versus that one. Every year, Gallup

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Speaker 1: publishes polls that show nursing as America's most trusted profession.

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Speaker 1: But every year the number of people who say they

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Speaker 1: trust nurses and doctors, that number keeps falling. Alison sees

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Speaker 1: it in the number of patients who argue with a

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Speaker 1: diagnosis or treatment. I for one, wouldn't like not ever

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Speaker 1: like go to my mechanic with my car and be like, oh,

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Speaker 1: I know it's you know whatever, because I know nothing

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Speaker 1: about cars, Like I know nothing, and so it's just

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Speaker 1: really wild for me to see that in healthcare, because

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Speaker 1: a human body is so significantly more complicated than a car.

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Speaker 1: If I went on Facebook and I said, if you

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Speaker 1: go jump off the Bay Bridge, you'll fly and it'll

428
00:26:35,876 --> 00:26:37,956
Speaker 1: be the greatest experience of your life, there are a

429
00:26:37,996 --> 00:26:39,836
Speaker 1: whole lot of people cann go jump off the Bay Bridge.

430
00:26:41,516 --> 00:26:44,716
Speaker 1: What is it about this kind of information that causes

431
00:26:44,756 --> 00:26:47,876
Speaker 1: people to respond to it? The answer popped into my

432
00:26:47,916 --> 00:26:49,996
Speaker 1: head as soon as I'd ask the question, which I

433
00:26:50,076 --> 00:26:53,996
Speaker 1: realize raises a question about the question. There are exactly

434
00:26:54,196 --> 00:26:57,156
Speaker 1: zero examples of people jumping off the Bay Bridge and

435
00:26:57,276 --> 00:27:02,276
Speaker 1: flying to safety because there's no uncertainty involved. However, there

436
00:27:02,276 --> 00:27:04,996
Speaker 1: are plenty of examples of doctors and nurses being wrong

437
00:27:05,876 --> 00:27:09,916
Speaker 1: because medical expertise is a series of probabilistic judgments. The

438
00:27:10,036 --> 00:27:13,076
Speaker 1: experts are using huge piles of data to judge the odds,

439
00:27:13,116 --> 00:27:15,876
Speaker 1: the odds that the vaccine will make you ill or

440
00:27:15,996 --> 00:27:19,596
Speaker 1: keep you safe, which brings us to our most recent

441
00:27:19,676 --> 00:27:25,676
Speaker 1: national crisis. I have been hit while trying to perform

442
00:27:25,716 --> 00:27:28,916
Speaker 1: a COVID swab on somebody who is very clearly dying

443
00:27:28,916 --> 00:27:31,636
Speaker 1: and like crashing fast, and we need to do everything fast,

444
00:27:31,676 --> 00:27:34,516
Speaker 1: and we say what we're doing. Hey, I'm Ali, I'm

445
00:27:34,516 --> 00:27:36,316
Speaker 1: your nurse. Today, I'm going to be swabbing your nose

446
00:27:36,356 --> 00:27:39,596
Speaker 1: real quick for a COVID swab and getting batted at.

447
00:27:39,636 --> 00:27:41,796
Speaker 1: You know, told that this is not COVID, that this

448
00:27:41,876 --> 00:27:45,116
Speaker 1: is just bronchitis, and just give me treatment for bronchitis.

449
00:27:45,196 --> 00:27:49,476
Speaker 1: This isn't what's going on. And it's like watching somebody

450
00:27:49,476 --> 00:27:53,236
Speaker 1: basically breakdown right in front of you and watching them

451
00:27:53,436 --> 00:27:59,556
Speaker 1: choose to basically not help themselves. You've heard some version

452
00:27:59,636 --> 00:28:03,476
Speaker 1: of these stories, and you likely have an opinion about them.

453
00:28:03,516 --> 00:28:07,596
Speaker 1: But what haunts Allison is one particular case. He was

454
00:28:07,636 --> 00:28:10,596
Speaker 1: a member of our local police apartment and you know

455
00:28:10,636 --> 00:28:14,036
Speaker 1: worth as a copy I mean, he was like the

456
00:28:14,076 --> 00:28:17,276
Speaker 1: epitome of health. He had no pre existing issues, nothing

457
00:28:17,276 --> 00:28:22,396
Speaker 1: else going on, and unfortunately he was unvaccinated and came

458
00:28:22,436 --> 00:28:25,916
Speaker 1: in very very ill. I took some time to call

459
00:28:25,996 --> 00:28:29,316
Speaker 1: his wife and explain to her what was going on,

460
00:28:29,996 --> 00:28:32,156
Speaker 1: what the game plan was, what we knew so far,

461
00:28:33,036 --> 00:28:34,956
Speaker 1: and the first thing that she had said to me

462
00:28:35,236 --> 00:28:37,556
Speaker 1: was just so you guys know, he does not want

463
00:28:37,556 --> 00:28:39,636
Speaker 1: to be intubated. He knows what happens when people go

464
00:28:39,676 --> 00:28:42,836
Speaker 1: on the ventilator, and he knows that hospitals are killing

465
00:28:42,836 --> 00:28:45,756
Speaker 1: people with this. So this is a police officer who

466
00:28:45,756 --> 00:28:49,156
Speaker 1: thinks hospitals are killing people. Yes, I just had to

467
00:28:49,316 --> 00:28:51,996
Speaker 1: take a step back and be like, if only you

468
00:28:51,996 --> 00:28:55,636
Speaker 1: could see the tears in your husband's eyes right now

469
00:28:55,716 --> 00:29:01,236
Speaker 1: and see how absolutely terrified he is right now, and

470
00:29:01,396 --> 00:29:06,036
Speaker 1: understand that this is not something that we're wanting to do.

471
00:29:06,876 --> 00:29:09,436
Speaker 1: Alison was faced with a man with a severe case

472
00:29:09,436 --> 00:29:12,796
Speaker 1: of COVID who had refused the vaccine. Now, the man's

473
00:29:12,836 --> 00:29:15,876
Speaker 1: wife wouldn't let the hospital improve his odds of living.

474
00:29:16,356 --> 00:29:18,796
Speaker 1: We had an extra thirty minutes or so before I

475
00:29:18,876 --> 00:29:21,796
Speaker 1: had to take him up to the ICU, and so

476
00:29:21,836 --> 00:29:24,436
Speaker 1: I thought, Okay, you know, I know how this is

477
00:29:24,476 --> 00:29:29,156
Speaker 1: going to go. I've seen how this has gone. Alison's

478
00:29:29,196 --> 00:29:32,116
Speaker 1: patient wasn't insane, not in the way a person would

479
00:29:32,156 --> 00:29:33,996
Speaker 1: be if they jumped off the Bay Bridge thinking they

480
00:29:33,996 --> 00:29:36,476
Speaker 1: were going to fly when there are zero chances of

481
00:29:36,516 --> 00:29:40,316
Speaker 1: that happening. The patient had refused a vaccine. There are

482
00:29:40,356 --> 00:29:43,156
Speaker 1: actual true stories of people getting sick from the vaccine,

483
00:29:43,836 --> 00:29:47,036
Speaker 1: and look at all those unvaccinated people who were totally fine.

484
00:29:47,996 --> 00:29:50,756
Speaker 1: The wife was refusing to allow the treatment most likely

485
00:29:50,796 --> 00:29:54,036
Speaker 1: to save his life. Well, there are actual true cases

486
00:29:54,036 --> 00:29:56,316
Speaker 1: of people being put on ventilators when they'd been better

487
00:29:56,356 --> 00:30:01,356
Speaker 1: off if they hadn't. In a probabilistic world, improbable things

488
00:30:01,396 --> 00:30:05,716
Speaker 1: do happen. We hear stories of the unlikely thing coming

489
00:30:05,756 --> 00:30:08,716
Speaker 1: true or not coming to pass, and they stick in

490
00:30:08,756 --> 00:30:14,756
Speaker 1: our minds. But so does Alison's story. I went into

491
00:30:14,796 --> 00:30:18,236
Speaker 1: his room and you know, gound up and everything, and asked, hey, like,

492
00:30:18,756 --> 00:30:20,196
Speaker 1: we have a few minutes, do you want to try

493
00:30:20,196 --> 00:30:23,276
Speaker 1: facetiming your family? And so we get his phone out,

494
00:30:23,516 --> 00:30:27,476
Speaker 1: we FaceTime his wife, and a couple minutes later, she says, hey,

495
00:30:27,516 --> 00:30:28,756
Speaker 1: do you want to say hi to the boys? The

496
00:30:28,756 --> 00:30:30,836
Speaker 1: boys want to say hi to you, And she brings

497
00:30:30,876 --> 00:30:33,276
Speaker 1: on his young children and I mean they were like

498
00:30:33,836 --> 00:30:35,956
Speaker 1: three and five years old. If I had to guess,

499
00:30:40,716 --> 00:30:43,156
Speaker 1: in my heart, I knew this might be the very

500
00:30:43,236 --> 00:30:45,676
Speaker 1: last time that they ever get to see their dad.

501
00:30:46,356 --> 00:30:49,756
Speaker 1: And they start saying, Daddy, Daddy, we love you, we

502
00:30:49,836 --> 00:30:52,476
Speaker 1: love you. And then one says, why aren't you saying

503
00:30:52,476 --> 00:30:55,396
Speaker 1: it back, dad? And I had to pan the camera

504
00:30:55,476 --> 00:30:58,276
Speaker 1: over to myself and say, oh, no, he's saying it back.

505
00:30:58,316 --> 00:31:00,796
Speaker 1: You just can't hear him. The machines are really loud

506
00:31:00,796 --> 00:31:03,476
Speaker 1: in here, but your daddy loves you, and he's saying

507
00:31:03,476 --> 00:31:06,156
Speaker 1: it back too, I promise. And we got off the

508
00:31:06,156 --> 00:31:08,476
Speaker 1: phone call and I got him up to icy you

509
00:31:08,556 --> 00:31:10,676
Speaker 1: and I had to take a good ten minutes to

510
00:31:10,716 --> 00:31:14,116
Speaker 1: go to the bathroom and just cry what happened to him.

511
00:31:14,436 --> 00:31:21,556
Speaker 1: He unfortunately passed away a week later. We're not wired

512
00:31:21,596 --> 00:31:24,756
Speaker 1: to see the odds. We're not wired to accept the

513
00:31:24,836 --> 00:31:28,636
Speaker 1: expertise that falls out of a giant pile of data,

514
00:31:28,716 --> 00:31:32,396
Speaker 1: but our minds still long for the simple answer rooted

515
00:31:32,396 --> 00:31:37,436
Speaker 1: in our personal experience or some story we've heard, even

516
00:31:37,476 --> 00:31:42,596
Speaker 1: when the simple answer kills us. We don't naturally respect

517
00:31:42,596 --> 00:31:52,076
Speaker 1: the polygon, but really we should, Really we should. Against

518
00:31:52,116 --> 00:31:54,956
Speaker 1: the Rules is written and hosted by me Michael Lewis

519
00:31:55,076 --> 00:31:59,356
Speaker 1: and produced by Catherine Girardo and Lydia Jeancott. Julia Barton

520
00:31:59,556 --> 00:32:03,756
Speaker 1: is our editor, with additional editing by Audrey Dilling. Beth

521
00:32:03,836 --> 00:32:07,676
Speaker 1: Johnson is our fact checker, and Mia Lobell executive produces.

522
00:32:08,196 --> 00:32:12,156
Speaker 1: Our music is by John Evans and Matthias Bossi of

523
00:32:12,236 --> 00:32:17,476
Speaker 1: Stellwagon Symphonette. We record our show at Berkeley Advanced Media Studios,

524
00:32:17,476 --> 00:32:22,556
Speaker 1: expertly helmed by tofur Ruth. Thanks also to Jacob Weisberg,

525
00:32:22,756 --> 00:32:28,436
Speaker 1: Heather Fain, John Snars, Carly Migliori, Christina Sullivan, Nicole Morano,

526
00:32:29,276 --> 00:32:34,956
Speaker 1: Royston Deserve, Daniella Lacan, Mary Beth Smith, and Jason Gambrell.

527
00:32:36,916 --> 00:32:39,996
Speaker 1: And an extra special thanks to Sam Sharpel's for letting

528
00:32:40,076 --> 00:32:44,836
Speaker 1: us use his amazing respect. The Polygon Remix Against the

529
00:32:44,916 --> 00:32:48,316
Speaker 1: Rules is a production of Pushkin Industries. Keep in touch,

530
00:32:48,716 --> 00:32:52,076
Speaker 1: sign up for Pushkin's newsletter at pushkin dot fm, or

531
00:32:52,156 --> 00:32:56,756
Speaker 1: follow at Pushkin Pods. To find more Pushkin podcasts, listen

532
00:32:56,796 --> 00:33:00,996
Speaker 1: on the iHeartRadio app, Apple Podcasts, or wherever you listen

533
00:33:01,036 --> 00:33:09,396
Speaker 1: to podcasts,