HIGHLIGHTS: Sir David Spiegelhalter

0:00Hi, everybody. Tune in to this short version of the podcast, which we do every Friday. For the long version, tune in on Wednesdays. hi everybody i'm nikolai tangen the ceo of the norwegian sovereign wealth fund and today i'm in really good company with sir david spiegelhalter he is well i would say he's the world best statistician and for sure the best communicator of risk that i've ever seen written lots of fantastic books and is particularly known for making statistics which is uh difficult uh really accessible for most people. So big thanks for joining us. No, great, great pleasure to be here.

0:39Having worked with risks, what's the main thing that you have learned about human psychology? Well, first of all, that I'm not a psychologist. So, you know, I am a statistician, but I have done my best to learn from psychologists that I have worked with. And I suppose just from observing how people react to risk and uncertainty. I tend to think of the broader idea of uncertainty rather than just risk. Everything to do with not knowing about what might happen in the future or even not knowing what's going on at the moment or what's happened in the past. All these things we are uncertain about. Some of them, usually things do have an upside and a downside, and so they could be considered risks depending on how they occur.

1:22But I think what I've learned is that people have to live with uncertainty. When you ask people, they say, oh, I don't like uncertainty. But some people like it, right? Well, some people like it, and some people are a bit more bold than others. But the point is that when you actually then go a bit further and say, well, do you want to know what you're going to get for Christmas? Do you want to know how a match is going to end, if you've recorded it? Do you want to know, do you just jump to the end of your series on TV to see the last episode and see what happens? Also, the thing I ask is, do you want to know when you're going to die, if I could tell you?

1:57No, they all say no. Some would like to know when they're going to die. Some are so, in a way, uncertainty-averse that they really like to have everything planned. But most people have realized that you've got to live with uncertainty and think of a life without uncertainty, without some risk. Now, you are a leading public communicator of statistics. Why is it important that people have a grasp of this field? Oh. What kind of wrong decisions are people – what are the big wrong decisions people are making? So we only have to look at some of the – without mentioning any names. Well, do a few. Do a few.

2:38Okay. We only have to look at what's happening in America at the moment to see what happens when high-level public discourse is not based on evidence. It's not based on numbers. It's just based on people saying what they feel like saying and regardless of the evidence behind it. Give some examples. What are the most horrifying ones in your mind? Oh, well, I think what RFK is saying about vaccines at the moment because he's got a built-in buzz. I mean, vaccines are not perfect. They're not perfectly safe and they're not perfectly effective. So I'd be the first one to say the term vaccines are safe and effective is actually misleading.

3:15However, they are of enormous value. And he's got his particular, I think, biases there. And, of course, I'm not going to talk about Trump and his way he was setting tariffs and things like that. So, you know, it seemed to be, you know, how he originally did that seemed to be based on what a, you know, 20-year-old intern might do on a spreadsheet. And so it just upsets me when I see the enormous – oh, apart from, of course, sacking the head of the Bureau of Labor Statistics when he doesn't like the numbers. So all these things are deeply upsetting to a nerdy statistician who – I don't want to tell anyone what to do.

3:55I don't want to tell anyone what the right policy is. All I want to do is say, please respect the evidence that we've got. just respect it and it doesn't tell you what to do or whatever but just try to respect it and and it's not just because politicians it's social media it's conspiracy theories everywhere the um lack of concern or the almost i think deliberate lack of understanding of what good evidence is and how it should be used is deeply upsetting to a to a nerd what was the most important thing we learned from COVID? Oh, the importance of data. The importance of data and its clear communication.

4:33Which data in particular? Oh, everything. Good. We wrote a whole book, you know, with every chapter on a different data source. There was so much. I mean, I was working, you know, around the clock, really, analyzing data and communicating about it because I didn't have an official role, which was great, which meant I could get out there with the media and try to explain things. And again, I never said what should be done. It was only trying to explain the numbers. And you had everything. You had the vaccines, you had the rollout, you had the testing, You had, of course, the disease, the infections.

5:00What a fantastic time for a statistician. I mean, it must be a paradise for you. Well, I don't know. Paradise isn't quite the right word. But it was very exciting, very challenging, and incredibly rewarding. And important. I mean, statisticians have hardly been that important before. Exactly. And I keep on – I even now get people coming up and saying, well, thank you so much for the work you're doing during COVID. And the media had to learn. And, you know, it wasn't just me, other statisticians who were out there talking about the numbers. And yet we always, at the beginning at least, ask, well, you know, who's to blame and what's going to happen or what should be done?

5:36And we'd have to say, I'm not going to say, no, no, that's not my job. You'll have to ask somebody else. All we're doing is explaining the numbers. And after a while, the media learned that this is what the audience actually wanted. They wanted an unbiased, unagendered discussion of the numbers. in your book you talk about the five five-count rules you know you tell people what you know you tell people what you don't know yeah tell us about these five things oh yeah well this is from this is for communicating evidence in a crisis this is all derived from john krebs when he was head of the food standards agency in the uk when he faced crisis after crisis he had foot and mouth he had mad cow disease he had everything one after the other total disasters and he developed sort of playbook that he afterwards he wrote and said, this is what I did when I was talking.

6:23Five points. And you've got to have, everyone should have these written tattooed on them, I think. First, what you know. So, you know, be really clear about what we know. And then you say what you don't know. You say where the areas of uncertainty are. You admit them straight away. Second, not first, but what is second. And then you say, you say what we're doing about it. You know, we are learning more. We're doing experiments. We're finding out, we're collecting data. We are learning. We are learning. Then you tell people what they can do in the meantime, that you may want to be cautious, you may not want to eat beef, you may not want to do everything.

6:52So you give people advice, self-efficacy in the meantime. But the final one is the most important. You say, we will come back to you and our advice will change as we learn more. So you emphasize the provisionality of what you're saying. Now, this is both deeply trustworthy because it's true. It's also, as far as I can see, absolutely impossible for politicians to do. In which cases do you look at probabilities where other people would look at yes or no? Oh, I got prostate cancer. And so I'm really interested in forecasting effect of people with cancer. We've been involved in algorithms for breast cancer and prostate cancer, building software to demonstrate those to people.

7:40And they're all in terms of probabilities, you know, roughish, but not bad in terms of 10-year survival and how those will change depending on the different treatments you've got. And I think it's absolutely essential when somebody says, oh, my doctor told me I had six months to live. I think, what? I never believe they said that anymore. Maybe there's some doctors who'd be so stupid as to say that. But I can't believe that. You have to show we don't know how long anyone's going to live. We can put some broad bracket on it because it's a survival curve. But first of all, I want to say I'm really sorry to hear that you have prostate cancer.

8:17But given that you are a mathematician, what are your stats? Oh, mine. Oh, yeah. That's the problem. I've got locally advanced prostate cancer. So I've got some minor metastases, oligometastatic it's called. I've got a few of them. And so I know, but the drugs now, the new drugs, the new hormone drugs on abiraterone, and they just are so effective. Now, I've got my PSA is essentially non-measurable. But the problem is we're trying to get these data from survival in clinical trials. I can't, it's really depressing. When I go back on the trials for abiraterone, they're terrible. You get sort of median survival of 18 months or something.

9:00And I think, what? No, I'm going to live longer than that, I'm sure. Because, you know, in the trials, they were trying this on very sick people. And things have improved so much. And actually, I'm at an earlier stage than the people who got the trials. So there's no good data, really, on what are my survival prospects. It's very difficult. It's a shame. And I wish there were, you know, better, you know, database. I really wish I could just tap in and find out, well, out of 100 people who are most similar to me, what happened to them. But for a start, we don't know. We don't know. It's only been given to people like me for a few years.

9:43So we've got no long-term follow-up. We don't know. I mean, some people got it earlier. My oncologist said, oh, I've had someone on this for 16 years. So that's the problem with something that's fairly rapidly changing. When you're asking for a long-term prediction, you can't say. so uh well all i can say all i can say is uh fingers crossed and yeah yeah i mean it does seem a bit it's a bit pathetic for a statistician just to say well i you know i hope i'm lucky but i hope i'm lucky