Speaker 1: Pushkin, what's your biggest fear. I'd say the biggest fear
Speaker 1: is something a mistake that I would make that would
Speaker 1: damage my credibility to where people would not listen to
Speaker 1: me when there's a tornado down. James Span meteorologist, maybe
Speaker 1: Alabama's best known person aside from some football coaches. He's
Speaker 1: all over TV talking about the weather, especially when the
Speaker 1: weather might kill you. So this is a tornado emergency,
Speaker 1: but the city's at Tuscaloosa and North Fort and the
Speaker 1: campus of the University of Alabama. James is one of
Speaker 1: those people who's never really had a job because he
Speaker 1: found his calling. He once stayed on the air as
Speaker 1: he watched a tornado make straight for his own home,
Speaker 1: pleading with people to see the risk. If you're just
Speaker 1: joining us, This is James Span with Taylor Serrallo mainly
Speaker 1: chicken on my wife's she's okay and she's in the
Speaker 1: tornado shelter. Okay, go ahead, Taylor. I'm sorry. I was
Speaker 1: put on this planet to mitigate loss of life when
Speaker 1: their tornadoes flying around here, and I have to be
Speaker 1: very careful in what I say and what I do,
Speaker 1: not just on the air, but on social media. And
Speaker 1: in real life. To build trust with his audience, James
Speaker 1: goes to incredible links. He's published a children's book called
Speaker 1: Benny and Chipper Prepared Not Scared. He spends time in
Speaker 1: dollar stores talking to people because the people who shop
Speaker 1: in dollar stores are also the people who live in
Speaker 1: trailer homes, the sort of homes that tornadoes obliterate. He
Speaker 1: memorizes the names of Alabamians who've died in storms, people
Speaker 1: he might have saved. There's lots of them. On a
Speaker 1: single day back in April twenty eleven, a line of
Speaker 1: tornadoes in Alabama killed two hundred and fifty three people.
Speaker 1: I know their stories, I know their family members. I've
Speaker 1: talked to many of them, and it's very motivating for me.
Speaker 1: And that's my main job in life. It's to make
Speaker 1: the warning process better with severe weather. He's doing all
Speaker 1: he can to warn people, yet people still don't understand
Speaker 1: what he's saying. I'm Michael Lewis. Welcome back to Against
Speaker 1: the Rules, where we explore unfairness in American life by
Speaker 1: looking at what's happened to various characters in American life.
Speaker 1: This season is all about experts. Today, we're going to
Speaker 1: explore the strange thing that's happened to experts. Not all experts,
Speaker 1: a certain percentage of them, the experts who think and
Speaker 1: speak in probabilities, who use data to forecast the likelihood
Speaker 1: of this or that coming to pass. The experts who
Speaker 1: can never be perfectly certain, and who risk our wrath
Speaker 1: because we love thinking in absolutes. James Span has been
Speaker 1: making and explaining weather forecast for the better part of
Speaker 1: half a century. In that time, it's kind of incredible
Speaker 1: how much has changed. So here's in nineteen seventy eight
Speaker 1: forecast partly sunny tomorrow with a chance of showers in
Speaker 1: the high of eighty. That's it. So today, under the
Speaker 1: same circumstances, I'd say we'll have a pretty good bit
Speaker 1: of sunshine between nine and eleven o'clock. After eleven o'clock
Speaker 1: rain is likely between eleven and one. The chance of
Speaker 1: any one spot getting wet during that two hour window
Speaker 1: it's about seventy five percent. It's going to rain about
Speaker 1: a half inch in most places. There could be some thunder.
Speaker 1: Most of that should be out of here by two thirty.
Speaker 1: After three o'clock, you're good to go. The sun breaks
Speaker 1: back out a temperature should peek around eighty at one o'clock,
Speaker 1: then falling back into the seventies by four o'clock. That's
Speaker 1: the difference in what we can do now compared to
Speaker 1: nineteen seventy eight. It's the prince between daylight and darkness.
Speaker 1: If you go back to the beginning of your career,
Speaker 1: were you encouraged to speak to the audience that way, like,
Speaker 1: we don't know that much about this, this could be wrong.
Speaker 1: Oh no, no, no, they didn't want you to say that.
Speaker 1: I mean, coodn't you know. Back in the seventies, this
Speaker 1: was when TV news was coming of age and I
Speaker 1: witness news, you know, and they wanted to be this
Speaker 1: godlike figure, you know on television. I was scared to
Speaker 1: communicate uncertainty because that wasn't encouraged. We were the news,
Speaker 1: the evening news, the Ron Burgundy newscast. Weather forecasts are
Speaker 1: inherently uncertain, the where, the when, the how much. With
Speaker 1: the current data we have, the best you can do
Speaker 1: is judge the odds. But the odds have gotten much
Speaker 1: more accurate over time. Back when James span was a
Speaker 1: young meteorologist, he knew very little but tried to sound
Speaker 1: like he knew a lot. Now that he knows a lot,
Speaker 1: he works hard to explain what he doesn't know. You're
Speaker 1: giving the audience more information and more new, honest information.
Speaker 1: So it's more demanding on the audience, right it is.
Speaker 1: And you know I hear this all the time. I
Speaker 1: just want to know if it's going to rain tomorrow,
Speaker 1: and they want a yes orno. They want that deterministic forecast,
Speaker 1: deterministic as imperfectly predictable, which is something the weather still isn't.
Speaker 1: When James Span started out, the ten day forecast was
Speaker 1: no better than just guessing. Now it's a lot better.
Speaker 1: But maybe the most obvious improvement, the one people really
Speaker 1: should notice, has been in forecasters understanding of the kind
Speaker 1: of weather that kills people. In nineteen seventy eight, we
Speaker 1: were using nineteen fifty seven era radar and the old
Speaker 1: black and white printouts of radar. It looked like somebody
Speaker 1: barsed on a piece of paper, and so warnings. In
Speaker 1: nineteen seventy eight, let's say we had a tornado down.
Speaker 1: We didn't really know where it was, we had an idea,
Speaker 1: so warnings were issued by an entire county. Tornadoes, even
Speaker 1: the big tornadoes are small and counties are huge. So
Speaker 1: here you are warning an entire county to get into
Speaker 1: your safe place and do something where most people didn't
Speaker 1: need to do anything. We're today we know literally within
Speaker 1: maybe a few city blocks of where the tornado is located. Well,
Speaker 1: so if I'm a consumer of tornado warnings, I get
Speaker 1: a much more precise warning, and I do I get
Speaker 1: a more advanced warning. Am I likely to get it?
Speaker 1: Get more more time to prepare for this thing? Yes?
Speaker 1: They have. Average lead time here is about twelve to
Speaker 1: fifteen minutes, and the average lead time back in the
Speaker 1: seventies was zero to three minutes. So we've come a
Speaker 1: long way, and we don't use counties anymore. We use small, small,
Speaker 1: small segments of counties. Geometric shapes, polygons. Anybody that knows
Speaker 1: James span I've said this over and over. Respect the polygon,
Speaker 1: and if you're in it, you do something. Respect the polygon.
Speaker 1: And if you're in the polygon, you respect the polygon.
Speaker 1: Respect polygon. Every storm today will mean business. Respect A
Speaker 1: James Spans superfan did a remix of his famous frame
Speaker 1: a polygon. I love this, of course, but it also
Speaker 1: raises a question, why respect the Polygon instead of just
Speaker 1: respect what I say. It's weird. If the James Span
Speaker 1: back in nineteen seventy eight had been as accurate as
Speaker 1: James Span is now, he'd have endured hail storms of gratitude,
Speaker 1: hurricanes of appreciation, tornadoes of awe. But that's not the
Speaker 1: wather he now lives with. Hello friends, this is James Span.
Speaker 1: It's time to read some mean tweets. And thanks to
Speaker 1: all of you for sending in the mean tweets. I
Speaker 1: really appreciate from from my heart. You cost the people
Speaker 1: in the state millions of dollars by your boop poor
Speaker 1: boot forecasts. I woke up today expecting snow. I blame you, James.
Speaker 1: I got my dogs all excited for nothing. James, either
Speaker 1: you're the worst meteorologist I've ever layer my eyes on,
Speaker 1: or you have the worst luck at predicting the weather.
Speaker 1: I think it's time to step down. Brother. The only
Speaker 1: difference between James Span and every other meteorologist is that
Speaker 1: James reads his mean tweets on the air. Just to
Speaker 1: show you where we stand, my producer called up some
Speaker 1: weather tweeters. Here's the kinds of things that people have
Speaker 1: to say weather forecasting is the only job you can
Speaker 1: have where you can be wrong fifty percent of the
Speaker 1: time and still make thousands of dollars. If we were
Speaker 1: wrong fifty percent in our jobs, we probably would be fired.
Speaker 1: I know nothing about meteorologists, but I know that you
Speaker 1: know they always wrong. I'm one of those people that
Speaker 1: actually vainly looks at the weather forecast because nine times
Speaker 1: attend it's different from the forecast. As technology improves, they
Speaker 1: don't improve. The continue to be it's smine boggling. You're
Speaker 1: going to get the hate, not necessarily because of your
Speaker 1: missed weather forecast, but just because of who you are.
Speaker 1: You're a you're a weather person, and you know you're
Speaker 1: a stooge. You're a you don't deserve to be on
Speaker 1: the planet. You shouldn't be breathing air. People have that
Speaker 1: attitude towards weather people. Oh listen. So I cut off
Speaker 1: a basketball game on Christmas Day in twenty fifteen, and
Speaker 1: we had a tornado coming up on the southwest part
Speaker 1: of the city here. It could have killed a lot
Speaker 1: of people, So we had to cover up about twenty
Speaker 1: twenty five minutes of that game, and nobody lost their life.
Speaker 1: The warning system worked beautifully but this is Christmas Day.
Speaker 1: Joy to the world, peace on earth, goodwill toward men.
Speaker 1: The first email I got, you know what it said.
Speaker 1: It said you should have been aborted by a coat hanger.
Speaker 1: So this is the stuff I deal with. I mean,
Speaker 1: I'm amazed he still goes on the air. His forecast
Speaker 1: just keep getting better and better, but the job of
Speaker 1: being a meteorology just keeps getting worse and worse. But
Speaker 1: I till these young people, you know, you better have
Speaker 1: a thick skin when you get out of here and
Speaker 1: you get your first job, because they're going to come
Speaker 1: after you when you found that first forecast up. Back
Speaker 1: in an earlier season of this show, I talked about
Speaker 1: the problem of referees and a strange phenomenon. A lot
Speaker 1: of refs are getting better at their jobs. They have
Speaker 1: new tools, they're better train they get better feedbacks, they
Speaker 1: are less likely to repeat mistakes. I mean, there's just
Speaker 1: no way that the refs in pro sports are less
Speaker 1: accurate than they were forty years ago when there was
Speaker 1: no replay, less training and all the refs got hired
Speaker 1: from the same old boys club, But they didn't used
Speaker 1: to need police escorts from the arenas. Now they do.
Speaker 1: In December of twenty twenty one, tornadoes ripped through Kentucky.
Speaker 1: Weather experts gave people lots of warning, So this is
Speaker 1: just an explosive severe weather set up, and that's the
Speaker 1: outlook that we have heading our way, especially after midnight
Speaker 1: through about eight o'clock in the morning. We definitely need
Speaker 1: to stay aware of the weather game. Meteorologists like this
Speaker 1: guy on WHASTV and Louisville were better than they'd ever been.
Speaker 1: Make sure you have a way to wake up if
Speaker 1: a warning is issued like this one that we have.
Speaker 1: That night in Kentucky, at least seventy seven people were killed,
Speaker 1: more people than have ever died from a weather event
Speaker 1: in the state's history. All people had to do to
Speaker 1: survive was listened to the experts, and still a lot
Speaker 1: of them didn't. I think that having data is a
Speaker 1: really recent phenomenon, Rebecca Golden, math professor at George Mason University.
Speaker 1: We didn't have data about how things were, we didn't
Speaker 1: record what happened previously. Then it's only really recently that
Speaker 1: we think maybe our lived experiences could be in part
Speaker 1: based on something probabilistic, Like a lot of people who
Speaker 1: are good at math Rebecca noticed the confusion and wrongheadedness
Speaker 1: of people who weren't. She also noticed that even when
Speaker 1: statistics and these new big piles of data were properly explained,
Speaker 1: people didn't really grasp their meaning. People have a hard
Speaker 1: time being convinced by data. It's just that they don't
Speaker 1: think that their experiences is in line with that data,
Speaker 1: and so they dismiss it, or they have other experiences
Speaker 1: that tell them that there are reasons to be skeptical
Speaker 1: of the source of that data or the source of
Speaker 1: the statements that are relying on the data. The problem
Speaker 1: isn't just in the quality of the information we have
Speaker 1: access to. It's in the way we make sense of
Speaker 1: the world. There's a very large segment of the population
Speaker 1: who are really struggle with basic mathematics. So people are
Speaker 1: making mistakes because they don't think in probabilities. I think
Speaker 1: that's right. Rebecca actually helped to start an organization called
Speaker 1: stats to expose the statistical mistakes made by journalists. She
Speaker 1: thought that if statistics were conveyed more accurately to the public,
Speaker 1: the public would see the world more clearly. Eventually, she
Speaker 1: decided she was wasting her time because there was this
Speaker 1: bigger problem how people comprehend statistics, even when they're accurate.
Speaker 1: Why is it that people don't think in probabilities, like
Speaker 1: the world's probabilistic. Why are our minds so deterministic? It's
Speaker 1: kind of a philosophical question. I think we're hardwired to believe.
Speaker 1: I think it helps us make decisions without being stressed
Speaker 1: about those decisions. It helps us act with certainty and
Speaker 1: make decisions so we don't hesitate too much and think
Speaker 1: too hard. In the savannah, we don't say that's probably
Speaker 1: a lion, right, we just run. But to just run
Speaker 1: is less and less a viable way to move through
Speaker 1: the world world because this relatively new thing called data
Speaker 1: has given us a far shrewder alternative. Everywhere you turn,
Speaker 1: you find someone analyzing data to generate the same sort
Speaker 1: of probabilistic understanding of the world that weather people do.
Speaker 1: I kind of come from this world of like, you know,
Speaker 1: kind of quantz and like baseball geeks and like poker players.
Speaker 1: That's Nate Silver. He got swept up in the nineteen
Speaker 1: nineties by the statistical revolution in baseball. And I think
Speaker 1: I'm kind of like one of the relatively people who's
Speaker 1: kind of escaped, so to speak, from that world in
Speaker 1: the like mainstream society. Back in two thousand and seven,
Speaker 1: Nate quit forecasting the future of young baseball players. He
Speaker 1: began to forecast elections instead. It's sort of what you're
Speaker 1: doing is actually accepting the possibility that maybe you can
Speaker 1: predict something that's right. But yeah, it's like kind of
Speaker 1: like saying, hey, look, we built an audience for this
Speaker 1: in in baseball, and so politics is still in the
Speaker 1: Stone Age, and so there must be kind of an
Speaker 1: audience for some politics too. When you turn your attention
Speaker 1: to politics, at what point are you aware that the
Speaker 1: expertise in political forecasting is sort of limited, that there's
Speaker 1: kind of an opportunity. I mean I had an intuition
Speaker 1: from that from the very beginning in politics, I mean,
Speaker 1: the campaigns have to be fairly smart and data driven
Speaker 1: about they were targeting. But like, but the media was
Speaker 1: all about kind of narratives. It was really quite bad
Speaker 1: in two thousand and eight. Right. It's really like a
Speaker 1: bunch of like, you know, old white men getting together
Speaker 1: and kind of deciding based on, you know, what their
Speaker 1: friends think, kind of what the narratives should be in
Speaker 1: the presidential primaries of two thousand and eight, Nate Silver
Speaker 1: gave an upstart senator named Barack Obama a much better
Speaker 1: chance than most everyone else did. In the general election.
Speaker 1: He nailed not just the outcome, but the result in
Speaker 1: every state, plus the precise number of votes Obama received
Speaker 1: in the Electoral College. People paid more attention to what
Speaker 1: Nate had done than how he had done it. He'd
Speaker 1: simply use polling data rather than his gut or some
Speaker 1: anecdote about some Iowa farmer. The polling data might not
Speaker 1: be perfect, but it was better than every other source
Speaker 1: of information, and they never made outright predictions. He issued
Speaker 1: political forecasts like weather forecasts, with probabilities attached to them.
Speaker 1: Going into election day of two thousand and eight, he'd
Speaker 1: given Obama a ninety point nine percent chance of winning.
Speaker 1: I mean, the irather thing about it is like like
Speaker 1: there was always a chance that we would be wrong,
Speaker 1: you know what I mean, and probably never heard from
Speaker 1: politics again, potentially. Instead, Nate became basically overnight the country's
Speaker 1: leading political forecaster because his expert piece was superior to
Speaker 1: the storytelling it replaced. Nate Silver is his name, fortune
Speaker 1: telling is his game. He's a celebrity statistician. Please welcome
Speaker 1: Nate Silver. That's right, Nate Silver's the good will hunting
Speaker 1: of political prognosticasia. There's a difference between weather forecasting or
Speaker 1: sports statistics and politics, a difference more of degree than kind,
Speaker 1: but still a difference. The people who celebrated Nate Silver
Speaker 1: really really didn't understand how to judge him. His better
Speaker 1: insights into pulling data had allowed him to see that
Speaker 1: Obama was basically always doing better than political pundits thought
Speaker 1: he was doing, but there was still no law that
Speaker 1: said Obama had to win. Polling data is a bit
Speaker 1: like the data that card counters get in blackjack. It's
Speaker 1: a lot better than having no data at all. It
Speaker 1: helps you to predict what comes next, but even card
Speaker 1: counters lose lots of hands. And here we go, ladies
Speaker 1: and gentlemen, welcome to Decision Night in America. Here at
Speaker 1: NBC's Democracy Point, which brings us to two thou sixteen.
Speaker 1: Nate Silver now had an enormous following. Once again, the
Speaker 1: pundits gave Hillary Clinton better odds than the polls. On
Speaker 1: election day, Nate gave Donald Trump a roughly thirty percent
Speaker 1: chance of winning at the time that was a radical call.
Speaker 1: A few traditional pundits thought Trump had that much of
Speaker 1: a shot. Yeah, I guess question, guys, are we post
Speaker 1: Nate Silver, are we pulled out? Well? They've been wrong,
Speaker 1: not only just wrong, they're just they're superfluous. And at
Speaker 1: the point where they just that's when you kind of
Speaker 1: begin to realize that, like, the way you define success
Speaker 1: and the way other people look at your forecast as
Speaker 1: being successful are very different. And also because it wasn't
Speaker 1: just that, like we got criticized after twenty sixteen for
Speaker 1: having quote unquote been wrong, it was also in the
Speaker 1: roup twentyteen people were actually mad at us for not
Speaker 1: being confident enough in Clinton's chances. Right, Nate never claimed
Speaker 1: to have some mystical ability to call a presidential election,
Speaker 1: and assigning probabilities is not the same as taking sides.
Speaker 1: Yelling at him for saying that Donald Trump had a
Speaker 1: thirty percent chance of winning was like being mad at
Speaker 1: the weather man for saying there was a thirty percent
Speaker 1: chance of rain and then getting mad all over again
Speaker 1: after it rains. I'm gonna get myself in a little
Speaker 1: bit of trouble for saying this, right, But like people
Speaker 1: like me really care about being right quote unquote for
Speaker 1: the intrinsic value of like making a good forecast as
Speaker 1: opposed to like influencing the narrative, if you will, Okay,
Speaker 1: so I would love because it would educate me. How
Speaker 1: do you evaluate a probabilistic a forecast? What's the right
Speaker 1: way for people to judge Nate's Silver Expert. The right
Speaker 1: way is if you take a whole bunch of forecasts
Speaker 1: that we've made and look at how they've done collectively. Right, So,
Speaker 1: let's say you made one hundred forecasts where the favorite
Speaker 1: had a seventy percent chance of winning. Look at that
Speaker 1: group of forecasts, and was it true that the favorite
Speaker 1: actually won about seventy percent at a time? Right? The
Speaker 1: slip side of that is that like it does mean that, like,
Speaker 1: you can tell very little from anyone prediction. I mean,
Speaker 1: unless you're like, unless you're very very close to one
Speaker 1: hundred zero percent, right, then one prediction alone won't tell
Speaker 1: you that much. Experts have gotten better, but they've also
Speaker 1: gotten harder to judge, so hard that you need an
Speaker 1: expert to judge them. And that's a problem, right, I Mean,
Speaker 1: who's going to go to the trouble of evaluating hundreds
Speaker 1: of Nate Silver's forecasts. And while it's true that he's
Speaker 1: made thousands of election forecasts, he hasn't made thousands of
Speaker 1: forecasts for presidential elections. Most people don't even think about
Speaker 1: elections or forecasts or anything else the way Nate Silver does.
Speaker 1: Most people don't even speak his language. I actually think
Speaker 1: that the word uncertainty is used in English in a
Speaker 1: very different way than uncertainty is used in statistics. Rebecca
Speaker 1: Golden again, So when we talk about uncertainty and statistics,
Speaker 1: we might say something about a confidence interval, or we
Speaker 1: might use a pee value. I'm not really sure you
Speaker 1: want this on your podcast, Like, maybe that's a little
Speaker 1: bit too technical. It might be better to trying to
Speaker 1: think of how it might be better to talk about
Speaker 1: uncertainty for your Well, this is the root of the matter. So,
Speaker 1: because it's not just my podcast listeners who are cut
Speaker 1: above the average human beings, it's like, how the American
Speaker 1: public understands uncertainty? How do you convey it? I think
Speaker 1: the best way to talk about it is to actually
Speaker 1: put it with specific numbers, Like instead of talking percentages,
Speaker 1: let's talk about numbers instead of ten percent, say one
Speaker 1: in ten, that kind of thing. But there's a much
Speaker 1: bigger problem behind all this, an emotional problem. It comes
Speaker 1: from us wanting certainty in situations where certainty just doesn't exist.
Speaker 1: If the weatherman says is an eighty percent chance of
Speaker 1: rain and it doesn't rain, the people on the receiving
Speaker 1: end of the forecast don't say, oh, that was one
Speaker 1: of the twenty percent of that was one of the
Speaker 1: times when it wasn't going to right. They say the
Speaker 1: expert doesn't know what he's talking about. So the inability
Speaker 1: to think in terms of probabilities also becomes an inability
Speaker 1: to evaluate the experts. There's a huge amount of inability
Speaker 1: to evaluate who is an expert, and yeah, it costs lives,
Speaker 1: it really does. A new kind of expert appears on
Speaker 1: the scene, an expert who works with these new big
Speaker 1: piles of data, an expert who thinks improbabilities, an expert
Speaker 1: who admits to being uncertain. These new experts are clearly
Speaker 1: better than the experts they replaced, and yet people treat
Speaker 1: them as if they're worse and neglect their advice, even
Speaker 1: when their lives depend on it. We who depend on
Speaker 1: the experts still want them to have a definitive answer.
Speaker 1: Either it will reign or it won't. Trump either will
Speaker 1: win or he won't. But that's not the nature of
Speaker 1: the world we live in, and we're having some trouble
Speaker 1: accepting that fact. The more you look for it, the
Speaker 1: more you see this problem. We've been talking about the
Speaker 1: problem of the experts getting better yet being treated as
Speaker 1: if they've gotten worse, a problem that leads to a
Speaker 1: lot of mystifying behavior, like what's gone on in the
Speaker 1: past two years inside the American healthcare system. I definitely
Speaker 1: saw a lot of this coming. Alison Fearing is a
Speaker 1: nurse at Rush Copley Medical Center in Aurora, Illinois. I
Speaker 1: can't tell you how many times I've had somebody coming
Speaker 1: and say I have this because I read X, Y
Speaker 1: and Z on WebMD, so I know that that's what's
Speaker 1: going on, and it's like, well, there's a lot more
Speaker 1: that goes into it that we need to work up further,
Speaker 1: because there are also other things that this could be
Speaker 1: and we won't know this until we diagnose it with
Speaker 1: lab work or a cat scan or whatever. Has this
Speaker 1: been going on your whole career? I would say it's
Speaker 1: definitely gotten worse over the past five years or so.
Speaker 1: I think prior to that there was a bit of it,
Speaker 1: but definitely not to the extent that there is now.
Speaker 1: Modern medicine is one of the great miracles of our age.
Speaker 1: If you went to a doctor in the nineteenth century,
Speaker 1: he was more likely to kill you than cure you.
Speaker 1: Now he's vastly more likely to cure you, and the
Speaker 1: odds of that get better with each passing day. Do
Speaker 1: you think there's anything that, if it happened to me
Speaker 1: that required me to go to the emergency room, that
Speaker 1: I'd be better off forty years ago than now. Honestly, no,
Speaker 1: I really can't think of a single thing, just because
Speaker 1: we have so much technology. At first glance, she's not
Speaker 1: really like James Span or Nate Silver. Doctors and nurses
Speaker 1: don't usually speak in probabilities, but her expertise is essentially probabilistic.
Speaker 1: Behind her is a world of medical science that's calculating
Speaker 1: the odds all the time, the odds that you have
Speaker 1: this disease as opposed to that one, the odds that
Speaker 1: this treatment will work versus that one. Every year, Gallup
Speaker 1: publishes polls that show nursing as America's most trusted profession.
Speaker 1: But every year the number of people who say they
Speaker 1: trust nurses and doctors, that number keeps falling. Alison sees
Speaker 1: it in the number of patients who argue with a
Speaker 1: diagnosis or treatment. I for one, wouldn't like not ever
Speaker 1: like go to my mechanic with my car and be like, oh,
Speaker 1: I know it's you know whatever, because I know nothing
Speaker 1: about cars, Like I know nothing, and so it's just
Speaker 1: really wild for me to see that in healthcare, because
Speaker 1: a human body is so significantly more complicated than a car.
Speaker 1: If I went on Facebook and I said, if you
Speaker 1: go jump off the Bay Bridge, you'll fly and it'll
Speaker 1: be the greatest experience of your life, there are a
Speaker 1: whole lot of people cann go jump off the Bay Bridge.
Speaker 1: What is it about this kind of information that causes
Speaker 1: people to respond to it? The answer popped into my
Speaker 1: head as soon as I'd ask the question, which I
Speaker 1: realize raises a question about the question. There are exactly
Speaker 1: zero examples of people jumping off the Bay Bridge and
Speaker 1: flying to safety because there's no uncertainty involved. However, there
Speaker 1: are plenty of examples of doctors and nurses being wrong
Speaker 1: because medical expertise is a series of probabilistic judgments. The
Speaker 1: experts are using huge piles of data to judge the odds,
Speaker 1: the odds that the vaccine will make you ill or
Speaker 1: keep you safe, which brings us to our most recent
Speaker 1: national crisis. I have been hit while trying to perform
Speaker 1: a COVID swab on somebody who is very clearly dying
Speaker 1: and like crashing fast, and we need to do everything fast,
Speaker 1: and we say what we're doing. Hey, I'm Ali, I'm
Speaker 1: your nurse. Today, I'm going to be swabbing your nose
Speaker 1: real quick for a COVID swab and getting batted at.
Speaker 1: You know, told that this is not COVID, that this
Speaker 1: is just bronchitis, and just give me treatment for bronchitis.
Speaker 1: This isn't what's going on. And it's like watching somebody
Speaker 1: basically breakdown right in front of you and watching them
Speaker 1: choose to basically not help themselves. You've heard some version
Speaker 1: of these stories, and you likely have an opinion about them.
Speaker 1: But what haunts Allison is one particular case. He was
Speaker 1: a member of our local police apartment and you know
Speaker 1: worth as a copy I mean, he was like the
Speaker 1: epitome of health. He had no pre existing issues, nothing
Speaker 1: else going on, and unfortunately he was unvaccinated and came
Speaker 1: in very very ill. I took some time to call
Speaker 1: his wife and explain to her what was going on,
Speaker 1: what the game plan was, what we knew so far,
Speaker 1: and the first thing that she had said to me
Speaker 1: was just so you guys know, he does not want
Speaker 1: to be intubated. He knows what happens when people go
Speaker 1: on the ventilator, and he knows that hospitals are killing
Speaker 1: people with this. So this is a police officer who
Speaker 1: thinks hospitals are killing people. Yes, I just had to
Speaker 1: take a step back and be like, if only you
Speaker 1: could see the tears in your husband's eyes right now
Speaker 1: and see how absolutely terrified he is right now, and
Speaker 1: understand that this is not something that we're wanting to do.
Speaker 1: Alison was faced with a man with a severe case
Speaker 1: of COVID who had refused the vaccine. Now, the man's
Speaker 1: wife wouldn't let the hospital improve his odds of living.
Speaker 1: We had an extra thirty minutes or so before I
Speaker 1: had to take him up to the ICU, and so
Speaker 1: I thought, Okay, you know, I know how this is
Speaker 1: going to go. I've seen how this has gone. Alison's
Speaker 1: patient wasn't insane, not in the way a person would
Speaker 1: be if they jumped off the Bay Bridge thinking they
Speaker 1: were going to fly when there are zero chances of
Speaker 1: that happening. The patient had refused a vaccine. There are
Speaker 1: actual true stories of people getting sick from the vaccine,
Speaker 1: and look at all those unvaccinated people who were totally fine.
Speaker 1: The wife was refusing to allow the treatment most likely
Speaker 1: to save his life. Well, there are actual true cases
Speaker 1: of people being put on ventilators when they'd been better
Speaker 1: off if they hadn't. In a probabilistic world, improbable things
Speaker 1: do happen. We hear stories of the unlikely thing coming
Speaker 1: true or not coming to pass, and they stick in
Speaker 1: our minds. But so does Alison's story. I went into
Speaker 1: his room and you know, gound up and everything, and asked, hey, like,
Speaker 1: we have a few minutes, do you want to try
Speaker 1: facetiming your family? And so we get his phone out,
Speaker 1: we FaceTime his wife, and a couple minutes later, she says, hey,
Speaker 1: do you want to say hi to the boys? The
Speaker 1: boys want to say hi to you, And she brings
Speaker 1: on his young children and I mean they were like
Speaker 1: three and five years old. If I had to guess,
Speaker 1: in my heart, I knew this might be the very
Speaker 1: last time that they ever get to see their dad.
Speaker 1: And they start saying, Daddy, Daddy, we love you, we
Speaker 1: love you. And then one says, why aren't you saying
Speaker 1: it back, dad? And I had to pan the camera
Speaker 1: over to myself and say, oh, no, he's saying it back.
Speaker 1: You just can't hear him. The machines are really loud
Speaker 1: in here, but your daddy loves you, and he's saying
Speaker 1: it back too, I promise. And we got off the
Speaker 1: phone call and I got him up to icy you
Speaker 1: and I had to take a good ten minutes to
Speaker 1: go to the bathroom and just cry what happened to him.
Speaker 1: He unfortunately passed away a week later. We're not wired
Speaker 1: to see the odds. We're not wired to accept the
Speaker 1: expertise that falls out of a giant pile of data,
Speaker 1: but our minds still long for the simple answer rooted
Speaker 1: in our personal experience or some story we've heard, even
Speaker 1: when the simple answer kills us. We don't naturally respect
Speaker 1: the polygon, but really we should, Really we should. Against
Speaker 1: the Rules is written and hosted by me Michael Lewis
Speaker 1: and produced by Catherine Girardo and Lydia Jeancott. Julia Barton
Speaker 1: is our editor, with additional editing by Audrey Dilling. Beth
Speaker 1: Johnson is our fact checker, and Mia Lobell executive produces.
Speaker 1: Our music is by John Evans and Matthias Bossi of
Speaker 1: Stellwagon Symphonette. We record our show at Berkeley Advanced Media Studios,
Speaker 1: expertly helmed by tofur Ruth. Thanks also to Jacob Weisberg,
Speaker 1: Heather Fain, John Snars, Carly Migliori, Christina Sullivan, Nicole Morano,
Speaker 1: Royston Deserve, Daniella Lacan, Mary Beth Smith, and Jason Gambrell.
Speaker 1: And an extra special thanks to Sam Sharpel's for letting
Speaker 1: us use his amazing respect. The Polygon Remix Against the
Speaker 1: Rules is a production of Pushkin Industries. Keep in touch,
Speaker 1: sign up for Pushkin's newsletter at pushkin dot fm, or
Speaker 1: follow at Pushkin Pods. To find more Pushkin podcasts, listen
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