Episode 361: Alex Edmans - Finding "The Truth" in Economics, Finance, and Life

24:40So one form that we just talked about is the idea that more might not always be better, or less might not always be worse, and so on. But another type of shades of grey, is even if something is good in general, not everything within that umbrella may well be good. So let's say if sustainability is generally good for financial performance, not everything under the sustainable bucket may be good. So let's say employee welfare is good for performance, which makes sense because it leads to motivated workers, but having a Catholic values screen might not be positively correlated with financial performance.

25:19But if we view the world in black and white terms, then anything which is called sustainable, we think is good. Similarly, anything which is called cholesterol might be bad, when actually there is good cholesterol and there's bad cholesterol. But another form of black and white thinking is we see the world in labels and we treat everything under that label the same. In the book, you kind of set everything up by going through confirmation bias, black and white thinking, and you get the ladder of misinference, which is so cool. Can you describe what the ladder of misinference is? So why did I come up with this to begin with?

25:52Before I wrote my book, I read other books about misinformation, and they were good. I'm not going to attack them because I view them as rival books. But they were so good, and they were so comprehensive that they came up with the 263 ways in which we can be misinformed. And then it's difficult for a reader to remember all 263 and to put them into practice. I wanted to boil down all the types of misinformation into just four categories so that it's easy to put into practice. And those four I display in a graphic called the ladder of misinference. So why a ladder? Well, when we start from some statements and we draw some conclusions from them, we are climbing a ladder.

26:37But why I call it the ladder of misinference, which is obviously a play on the ladder of inference, is that sometimes the steps that we make when we draw conclusions from raw data are incorrect. And so let me just talk through for now. So the first step is a statement is not fact. It may not be accurate. So what I mean by this is we quote statements all the time as if they were the gospel truth. Culture eats strategy for breakfast. Peter Drucker said this, so it needs to be true, because he was a management guru. But it may not be the gospel truth. And indeed, in that case, there's two issues at least.

27:15The first is that Peter Drucker never said that. And so this is a misattributed quote. And second, and more seriously, even if he had said that, it doesn't mean that it's true, Because where is the evidence that culture always beats strategy? Yes, he is a famous management guru, but that doesn't mean that everything that comes out of his mouth is correct. There's a lot of famous people I'm sure the listener can think about who says things which are not correct. So what we'd actually want to look at is, is there evidence behind that statement? Has there been a careful study of the link between culture and strategy performance?

27:50And even if there isn't hard evidence, this doesn't mean that what he says is definitely incorrect. But rather than Peter Drucker's state proves that culture eats strategy for breakfast, we will say, well, in his subjective opinion, culture is more important than strategy. We will still put some weight on that because he's a guru. But this doesn't mean that strategy is irrelevant, which is how some people have interpreted that black and white statement. it. Some of the examples you have of statements that are just not true, but have persisted for a long time are mind blowing. There's actually one, I don't think it was in your book that I was reading about recently about how lottery winners tend to go bankrupt or a large proportion of them.

28:31That's another one of these that there's no basis for that. Somebody said it once and it was attributed to a study that was never done, but now people believe it to be true. And you've got a bunch of examples like this in the book. Can you talk about where the famous 10 ,000 hour rule for mastery comes from? This was from a bestselling book by Malcolm Gladwell called Outliers. And the popular version of the rule is 10 ,000 hours is the key to success. You can be successful in anything that you want to, as long as you invest 10 ,000 hours into studying or practicing it. And immediately this plays on confirmation bias.

29:06We want this to be true. And we think it's true because we're taught from a young age that practice makes perfect. You can do anything that you set your mind to. And this is empowering. So this suggests that the world is your oyster. You don't need to be limited by your genetics or your past background. And I had to admit in the book that I myself had taught this to my students at Wharton. Why? I'm a finance professor. What has this got to do in finance? And well, at the end of my final lecture, because I start teaching right at the start of the MBA, then my final half an hour at the end of my final class is looking forward for the rest of the two years.

29:45And I give some advice. And one of them was the 10 ,000 hours rule, which is that to do an MBA, you can push yourself outside your comfort zone. You can redefine yourself. Maybe historically, you always worked on quantitative factors, but now you can become a leader, a master negotiator and public speaker, as long as you put your time in. And when I said that, I would get a lot of nods. People would say, oh, that was an inspirational, empowering speech. But then later on, I found that the evidence for this was really, really weak. So Gladwell claims that this rule applies in every context, yet the evidence he cites is limited to just violin playing.

30:25And what works in violin playing may well not work in public speaking or neurosurgery. But even worse, that study never measured 10 ,000 hours, and it never measured success, which is a bit of a problem if you think that 10 ,000 hours is the secret to success. Well, first, how did it not measure hours? So all it did is it asked people to report the hours that they had practiced since age five. Now, these violinists were currently 23 years old, So they had to remember from age five, 18 years ago, how much you practiced. You might well not remember how much you practiced last week. Here is the problem.

31:08If you were successful right now, you might well say, yeah, I probably did practice a lot to get here. I had to work hard. You don't want to say, I was just born with it and was lucky. And conversely, if you're not successful, you won't say I practiced a lot when I was young, because then you'd have to admit that this was all a waste of time. Stress leads to higher reported hours rather than hours leading to success. These are just self-reports. But another issue is it never measures success, not in any objective manner. It just asks the teachers at this violin academy who has the greatest potential to, in the future, play at a good orchestra.

31:47And here, it may well be that, let's say, the teachers knew how much the students practised. Probably if you know a student practices a lot, you will probably say, yeah, I think he or she will be successful. If somebody's goofing off and not showing up, you'll probably say that they will be unsuccessful. So for something which claims to study the link between practice and success, it doesn't measure actual practice, but memories of practice 18 years ago, it doesn't match an actual success, but a subjective evaluation. That's wild. And that statistic is so commonly cited. There's another one that was pretty striking.

32:22There's a book that a lot of people have read, Why We Sleep. Can you talk about the example from that book? Yeah, so this book, Why We Sleep, by Dr. Matthew Walker, because he stresses on the front cover his scientific credentials, argues that sleep is the secret to success. We're in a national or global sleep epidemic. People are not sleeping enough. This confirms what we want to be true. We want an excuse to sleep more because it's going to lead to greater physical health, greater mental alertness. And we would like to believe that our over-eager beaver colleagues who wake up at 5am, they will get their comeuppance.

32:56And what might their comeuppance be? Well, maybe if they're not fully alert, they might just have accidents. And indeed, there is a graph which shows the more you sleep, the fewer the accidents. If you sleep for nine hours, you have fewer than eight. That's fewer than seven, fewer than six. Yet if you go to the underlying paper behind this, he actually shows a quite different graph. that has another bar for five hours of sleep. And those people who sleep for five hours have fewer injuries than six. They also have fewer injuries than seven. But because Matthew Walker doesn't like that, he cuts this out of the graph.

33:32He just shows something which is really selective. You think that this is a book founded on lots of data and evidence. Again, it's by this Dr. Matthew Walker who goes out of the way to stress his credentials. And yet there's a lot of errors in this. and this is only one of many errors in the book. There's an entire blog post by Alexi Guzzi on why the book Why We Sleep is riddled with scientific errors. And so again, even if something is said to be based on data and science, you could just be quoting selectively, going so far as to cut out a bar of a graph because it doesn't support your viewpoint.

34:07That is just like a police officer hiding some evidence because it exonerates his suspect. Yeah, that one's crazy because you show the chart from Why We Sleep, and then you show the chart from the paper. In the Why We Sleep, it shows this monotonic relationship, which is completely destroyed by the actual chart from the paper, which is, like you said, it's like hiding evidence. How is something that egregious not flagged by an editor or a fact checker? It's because people just may not want it to be true. If you are an editor, you might think this is a guy with strong scientific credentials. So another thing is Matthew Walker misrepresents his scientific credentials.

34:42So he said he had a PhD from the prestigious Medical Research Council of the UK. There's no such council which ever gives any degree. So this gives you grants. It doesn't give you a PhD. He's got his PhD, I think, from the University of Newcastle. That's an OK university. It's not Oxford or Cambridge or London, so on. So that is also misrepresented. But people might think, OK, this is a guy who's got great credentials. He's quoted a paper. If you quote something, you don't have the time to go through and look up every evidence. You might trust him. But I think what is even more surprising or more disappointing is even after all of these issues have been raised on this very long article critiquing his book, nobody's really changed their opinion.

35:28Because they say, OK, well, despite all of that, oh, it's my gut feel which thinks that sleep is still important or my lived personal experience suggests the more I sleep, the more alert that I'm going to be. But again, this is sort of mixing up correlation and causation. Why are you able to sleep a lot? It's probably that lots of other things in your life are going well. So in the periods I'm sleeping more, then I'm probably going to be happier. But because lack of stress means I'm going to sleep more. In the periods in which I'm not sleeping, it's because I'm having to juggle loads of other things.

36:00Right now, I just had another child a month ago, and maybe that is going to cause a lot of stress and a lot of busyness, not necessarily the lack of sleep. Similarly, this might happen with political outcomes. So in the Brexit referendum where the UK voted to leave the European Union, there was an incorrect claim posted on the side of a bus that the European Union cost the UK£350 million per year. That was a sum which was completely exaggerated. When that was corrected, people said, oh, well, that never influenced my viewpoint anyway. I would have voted the same way. But you would say that because you don't want to admit that you were misled by something.

36:43So once you have a particular opinion, even after there is some disconfirming evidence, people are not changing their view. So this is the often quoted phrase that a lie has gone halfway around the world before the truth has laced up his or her shoes, is once something has gone viral, people stick to it, even if there's disconfirming evidence. Sleep's a tough one. I do like to sleep. How should people verify whether a statement is actually based on a fact? One thing is actually just to try to search for this, is try to search for disconfirming evidence. So if you were to look at why we sleep errors, or you might look at this in any type of popular book, 10 ,000 rule critique, then you might find this.

37:28And so while I've talked about the dangers of getting information from the internet because there might be less trusted sources. On the other hand, what it does mean is we do have access to a wide source of information. And even if somebody who might not be a massive household lame like Alexi Guzzi, even if he's a serious researcher and has done his homework, this is something that it will come out with. So try to find something which is disconfirming. Sometimes it may well be as simple as just going to the underlying article. Now, Now, I don't expect people to go through every single word of a scientific paper on sleep.

38:04But just to look at that picture, you can just see how that picture is very different from the picture that was ended up being quoted. Those are examples of how a statement may not actually be based on a fact. Once it's been verified, how is a fact not data? This is the second step of the ladder of misinference. So you might think, OK, what Alex has said is a statement, it's not fact. Let's check the facts. Now, if we know that something is a fact, are we home and dry? Well, the answer is no, because even if something is a 100 % true fact, it could be cherry-picked. It might be the exception that does not prove the rule.

38:39So one example is Simon Sinek's book Start With Why, which claims that having a why is the secret to success. And he gives a number of facts to back this up. For example, Apple started with why, and Apple is extremely successful. There's no denying that. And he also gives other facts, other anecdotes. For example, it applies in the non-profit sector. Wikipedia, extremely successful. It applies to individuals. So the Wright brothers, they were the first to have a test-powered flight. But all of these things can be cherry-picked. So they can well be hundreds of other companies, or non-profits, or individuals, that also started with Y, and they failed.

39:20But we never hear about them, because Simon Sinek will never tell you about this. So this is the difference between facts and data. So facts are individual stories, data is something which is large scale. And the issue here is not just authors like Simon Sinek, the issue could be business school professors. So I'm saying this as much to myself as I am to the reader, is that in business schools, we like to teach with case studies. But how do you choose a case study. You choose the most vivid and striking example of the particular point that you want to show. So it may well be a company which was sustainable and a pioneer in that and had massive performance, whereas there might be others which are sustainable companies who failed.

40:07So I do use stories and I do use case studies in my teaching and in my books. But when I do use them, I always try to make sure that it's backed up by large scale evidence. So for me, I start with the large scale evidence and I find a story which brings that evidence to light. And then sequentially, yes, I lead with the story first, and then go to the evidence to back it up immediately afterwards. But often what people will do will just find a story which supports that viewpoint, find other examples that support that viewpoint as well, but they won't do a systematic study of the large-scale evidence.

40:42What are the steps involved with separating fact from data? What you'd want is something similar to a randomized control trial in medicine. So what happens there? So you would take a number of patients and they're all suffering from some affliction. You give some of them a treatment, a drug or behavioral intervention, see how many get better and how many get worse and give others a placebo and see how many get better and how many get worse and you compare the two but importantly your data set needs to contain people who took the drug and still got worse that's companies that started with y and yet they failed but they will never be in any simon sinek book and similarly you need companies that did not so you need people who took the placebo, and yet they still got better.

41:36That's like companies that did not start with Y, and yet they still succeeded. But again, you don't see this in any Simon Sinek book. So he will only show you the companies that started with Y and succeeded. And even if those examples are 100 % fact, it's what he's not telling you, which is where all the action lies. All of these other companies that started with why and failed, and all of these companies that succeeded without why. And why is that so striking? It suggests that misinformation is really hard to police. So some people might think, oh, why is it not the government stepping in and prosecuting it?

42:13To one of your earlier questions, Ben, why don't editors come out and check this? But the issue is not what you publish and what you disclose being incorrect. Maybe what you're saying is correct, but it's what you're hiding, which is where all the action lies. And it's really difficult to prosecute Simon Sinek for not telling you about all of the counter-examples. He could claim he just never knew of those counter-examples, perhaps because he engaged in a bias search. The Simon Sinek example touches on this, but what role do narratives play in how people interpret or misinterpret facts? It's a substantial role.

42:50So this is the idea of the narrative fallacy, which is even if there is no relationship between two factors, if you can weave in a compelling story, that story is memorable, and then we perceive a link when there is no link. And again, this is deeply ingrained with us, because even before we had writing, it would be the oral tradition, people pass on information through stories. So the narrative that Simon Sinek weaves in is, well, if you have a why, people will buy into this. It inspires people. People buy Apple products not because of what they do, but because of why they do it. They buy it because Apple has this vision and this purpose.

43:32Now, if you were just to look at that statement with a clear head, that sounds crazy. That's a load of rubbish. Why do you buy Apple products? Because of their functionality, because they work, because they have the apps, because of the reviews, because other people will have iPhones. They work. I don't really know why Apple did this, nor did I care. And I don't think this is unique for me. I think most people will buy products because of their reviews. Other people have them, their connectivity, their functionality. But the idea that it's a why, it's a vision that sounds more compelling, that might say as a leader, I don't need to get involved in the boring stuff, making sure that all the wiring is correct and everything works, I can just create a vision.

44:19And that's something that many people think is much more compelling. Yeah, it makes sense. Stories are very powerful. So as we move up the ladder of misinference, we start with a statement, then we go up to a fact, and then we go up to data. Once we have data, why is data not evidence? If the solution is not to look at a select example, then why don't we get data, which is the full sum, the full picture, the companies that started with Y and the companies that did not. Well, the issue here is that you may have a strong correlation, but it's not causation. There could be multiple explanations for the finding.

44:54So let's say in general, what we find is that companies with a purpose statement, a Y, do tend to be successful. And we've done this, we found this link looking at hundreds or thousands of companies. But is it that having a Y leads to success? Or is it once you're successful, you have the time and headspace to come up with a purpose statement because you're not firefighting? Or is it a third factor causes both? A great CEO causes good performance. A great CEO comes up with a purpose statement, but there's no direct link between the two. And so why do I call this misstep data is not evidence? It may not be conclusive.

45:36Because what is the difference between data and evidence? We often to hear these terms interchangeably. But what is evidence? Now we hear evidence in the context of a criminal trial. Evidence is evidence that points to one particular suspect. If the evidence suggests that Tom or Dick or Harry could have killed Sarah, that is not evidence. It's consistent with multiple explanations. So what we would want to move from data to evidence is not just a correlation which has multiple interpretations, but something which may be causation, a way of ruling out, is it success that leads to a Y, or is it good management that leads to both?

46:18But again, if we have confirmation bias with a side of a nice narrative, then we latch onto the causal explanation, which is the Y leads to success. How big of a problem is data mining in research, generally speaking? It's a huge problem. And so data mining, this is a different issue from correlation versus causation, this is the problem that there might not even be a correlation to begin with. Why? Because you've fished for it, you've mined for it. And so what do you mean by this? Again, let me give an example. So let's say I want to link sustainability and financial performance. Well, there's so many ways I could measure sustainability.

46:59Maybe I could look at carbon footprint, I could look at water usage, I could look at biodiversity impact, I could look at how you treat your employees. I can look at gender diversity, racial diversity. The list is endless. And in terms of financial performance, I can look at sales growth. I could look at profitability. I can look at total shareholder return. I could do this over five years or 10 years or one year. Even if I came up with a robust correlation that sustainable companies perform better, you need to ask yourself, well, could there have been many, many other ways of measuring sustainability and performance that the researcher did, and they only report the one that gave them the findings that you want.

47:42Then how does a reader discern this? Because you have no idea about all the other tests that they ran, but it's to ask yourself, do you have the most natural measure of performance and sustainability? For example, McKinsey has a ton of reports which link sustainability or other things like diversity to EBITDA, which is earnings before interest, tax, depreciation, and amortisation. So that is one measure of performance. But those common measures of performance, if we just look at how the stock price starts. And so why is that much better? Because profitability only measures the short term, yet the stock price is forward-looking.

48:23some of the best performing companies nowadays are tech firms, which don't have high current profits, but because they're expected to have higher future profits, the stock price is already encapsulating that. Can you share the example of an investor that had come to you wanting to build a fund around some performance measure and you did the research and it wasn't there, but they went and did the fund anyway? Yes. So this is one of the world's leading investors who I give a student into in the book. But she had heard of my research linking employee satisfaction to long-term firm performance, and she wanted to launch a fund based on diversity.

48:59I was really excited. I thought, OK, I could work with this leading investor. I myself am an ethnic minority, so I would like to believe that diversity pays off. And there were 24 measures of gender diversity, which was her specific lens, which I tried to correlate with performance. And I did, and I found in 22 of those 24 cases, there was sadly a negative link. Now, it was clear what I should do if I wanted to work with this investor. I could just disclose the two positive ones, but that would not be correct in terms of honesty. So I disclosed all 24, and she wasn't interested in working with me.

49:39She politely thanked me, but then moved on. I thought, well, this is going to be the end of it. And then just six months later, I saw her launch the fund with a big fanfare that this is backed up by scientific research showing a strong link. And I was surprised because I had done the research 24 times and not found it. And what she had found was research by another company linking diversity not to shareholder returns, which is what any investor should care about, but measures like the return on sales or return on capital employed. it had a very specific measure of diversity. It compared boards with three or more women to boards with zero women.

50:19But why is it three or more versus zero? Why is it not three or more versus two or fewer? Two or more versus one or fewer? Or why isn't it something percentage-wise, at least one third women and two thirds men or something? It may well be that this three versus zero was the only thing which worked. And so that's why they reported this. And so this matters because this fund and many other funds with the same theme, they have all underperformed. And so if you're basing this on misleading data, it doesn't matter how much of an advocate you are for the cause, and it may well be seen to be a worthy cause, but do not back up this cause with some incorrect data.

50:59Just as, as a police officer, you might have a high conviction that this person is guilty. And it may well be that the court has just unfairly made certain rulings that certain evidence is inadmissible. But despite how strongly you feel that it's not just, it is not an excuse to then make up data or make up evidence to convict your suspect. Seems like a really tricky thing in financial economics. As a practitioner, people used to say evidence-based investing was a good thing to imply things like index funds. I found that phrase to increasingly meaningless because you can find evidence or a form of evidence, at least, to back up pretty much any investment strategy that you want to make a case for.

51:38We like to throw around these phrases like research finds that, study proves that, but you can find a study to show whatever you want to support. What matters is the quality of the research and also not just to be skewed by one study, but to look at scientific consensus, which is the findings of multiple funds. What can people do to guard against data mining? As a consumer of research is to ask, well, have you looked at the most plausible ways of measuring an input and an output? So let's say the output is performance. The most natural measure for any fund is total shareholder return. That's the measure of performance they always advertise.

52:17So if you look at return on sales, that is quite odd. And also the input, if you're looking at three or more versus zero, So that's strange, because what about the board with one or two women? Why are we looking at the absolute number of women on the board? Shouldn't it be a percentage? Because if a board is larger, you might expect more women than if the board was small. So just to ask yourself, are there more plausible ways of measuring this? And if there were and they're not reported, then that might raise a red flag. And what the best studies will do, will do a robustness check, which is to show, well, if we were to measure performance in this other different way, we still get the same results.

52:55If we measure diversity in this different way, we looked at percentages, non-absolutes, we still find this. So moving up the ladder, when is data evidence? So that is when you have something like a randomized control trial. So in medicine, I have randomly assigned some people to get the drug and some people to get a placebo. Now, in real life, outside of randomized trials, you might think, well, that's pretty rare. But sometimes you do get the equivalent, which is known as a natural experiment. So what do I mean a natural experiment? Well, a lab experiment is what I have just described placebo and a treatment.

53:35A natural experiment is when the separation applies naturally, perhaps because of a law change. There was a time where I hope I'm getting the stakes the right way around, where Pennsylvania passed a minimum wage, but New Jersey, which is just across the border, did not pass a minimum wage. And so it's like the New Jersey restaurants were a placebo, nothing happened. The Pennsylvania restaurants were the ones with the treatment, with the drug. And so you can compare the effects of the minimum wage on employment. And the study I cite in the book finds surprisingly, the minimum wage of anything increased employment compared to the common perception that minimum wages are always damaging to employment.

54:19Why? You compare two very similar restaurants with similar economic conditions, because they're just very close to each other. Just one is in Pennsylvania, the other is in New Jersey, and then one was affected and the other was not. What does this say about evidence in financial economics where natural experiments and RCTs, well, RCTs I don't think you can do, but natural experiments are pretty rare as well. It's actually quite difficult to get evidence which is conclusively causal. And does this mean that, well, we can't believe anything and anything goes? No, but it's just to say that we don't want to rely on things too heavily.

54:54So absolutely, the gold standard should not be causal, 100 % evidence. Otherwise, we could just be ignoring many other things which might be 80 % true or 70 % reliable. But instead, it's not to view things as black and white. It's not either completely nailed and completely invalid. Even if what you find is a correlation, that could still be useful. And I need some of my own papers only find correlations because I don't have a natural experiment. So one example is my work on cognitive diversity. Now, we do have natural experiments on demographic diversity, which is forcing, say, boards to have 40 % of women.

55:33There is no natural experiment forcing there to be a diversity of different viewpoints. That's much harder to pass a law about. So all I have is correlations, but those correlations at least can be suggestive. They might get a 70 % of the way there, even if it's not completely conclusive. When we have evidence, why is evidence not proof? So let's say we've nailed causality. We've got a perfect natural experiment. That might only be true in that particular setting. It may not be universal. So what is the difference between evidence and proof. A proof is universal, so when Archimedes proves the area of a circle, that was true not only in ancient Greece in the 3rd century BC when he did his proof, it's true in circles in 2025 in Canada today.

56:21But often when we look at evidence, it might be gathered only in one particular setting. Let me give you an example. So there was a very famous book by Angela Duckworth and a really famous TED talk about the power of grit, the power of passion and perseverance. And what she did to show that grit mattered was she took men and women who got to West Point, which is the United States Military Academy. If you get into West Point, you're not yet in the army. You have to survive a six-week course called the Beast Barracks, which is really tough. It's mentally and physically demanding. And she wanted to see, well, what predicts survival of Beast Barracks, you might think it's physical fitness, but she found the one thing even more important than fitness was grit, which she defines as passion and perseverance.

57:11So that is striking, so grit must be so powerful if it's even more important than fitness in a physical challenge. But the problem here is something known as restriction of range. So what is that? She had a sample of already very fit people. So in order to get into West Point, you already have to be extremely fit. And maybe they had already passed the threshold level of fitness. And that's why fitness didn't matter, because everybody was already fit enough to survive beast barracks. Now it's something else like grit mattering. But we cannot over extrapolate from that setting of really fit people and to say, well, for the general person on the street, work on your fitness, work on your grit, not your fitness.

57:55No, for the ordinary person dreaming of joining the military, maybe he or she should work on their fitness. That's the more important thing. Yeah, that's interesting. So the grit research, it's not wrong, but it's right only within the specific setting in which it was conducted. Yes. And that's true for many other studies. So a lot of studies are done on weird people. You might think, well, that's strange for me to they call somebody weird, but weird, central Western, educated, industrialised, rich and democratic. That's where a lot of studies take place. And some of them we can generalise. So if it's something like smoking causes cancer in Canadians, that is something where it might well apply the same to Latin Americans or Chinese people, because that's something which is to do with the body.

58:38But if it's something cultural, which is, say, the link between equality of pay and perceived fairness, there may well be different social norms. So you're never going to get a study on the particular setting that you're interested in. But again, perfect should not be the enemy of the good. We want to ask ourselves based on logic, is this a setting which is sufficiently similar? And are there logical reasons to believe that it won't apply here? With smoking and cancer, I don't think we really need to be so specific in terms of context. But for something more cultural, behavioral, then the context may matter more.

59:14We've established that the truth is hard to establish. What steps do you think people should take to seek the truth when they're making an important decision? You've mentioned busy people a few times throughout the conversation. I guess within that context, what should people be doing? So it's to be discerning about any evidence. And so what I have in the back of the book is just a checklist of simple questions to ask yourself following the ladder entrance. So if there is a statement? Is there evidence behind this? Or are we just misquoting? If it's a fact, is this one anecdote? Or is this here in large scale?

59:45If it's large scale data, is there alternative exhalations? So it might not be a causation. And even if there is causation, what was the setting in which it was gathered? Is it different to what setting we care about? But more generally than just evaluating studies, because studies are not the only source of information, we get information from other people's opinions, is just to go out there and actively seek different viewpoints and make it clear that you are open to different viewpoints, particularly if you are a leader or in an authoritative position like a professor, where people might incorrectly assume that they should not challenge you.

1:00:21And when there is challenge, it may well be that you want to make it clear how much you appreciate it. So let's say you're the chair of a board, and then you're going to go ahead with a strategy, and then somebody ways is an objection. You hear that objection and you discuss it open-mindedly, but people think that even though that objection is well-founded, the benefits outweigh the costs and you still go ahead. Maybe you should go to that person afterwards, or maybe you should do this publicly to the rest of the board and say, I really appreciate you sharing your different viewpoint. Even though we still went ahead with the decision, we will bear your concerns in mind when we execute the strategy.

1:01:00And in the future, if you have concerns, please continue to voice them. If you had not done that, what might the dissenter think? He or she might think, well, I had to sacrifice my political capital to raise this concern. I may have annoyed the people who proposed the strategy. I delayed the meeting by half an hour, and I didn't change the decision anyway. So in the future, I'm just going to self-censor. So if you make it clear that you do encourage different viewpoints, then this does mean that you're going to still get differences of opinion and you're going to get closer to the truth. What about at a societal level?

1:01:36What should we be teaching people and thinking about to embed these ideas that you're talking about in our everyday lives? It's to teach the value of critical thinking. You might think, well, at a societal level, is this feasible? Can we teach this in schools? And I think we can, even though, yes, statistics we might only learn when we're 16. But what I'm highlighting is statistics is not about just numbers. There is not a single equation like that. What statistics is about is looking at alternative exhalations is about being discerning. So just like we teach a kid, don't accept sweets from strangers, there's an alternative exhalation for why the stranger has given you sweets.

1:02:15It's a nefarious one. We also teach kids, well, not directly, but when they read murder mysteries, they learn quickly that it's not the most obvious suspect who committed the murder. They look for alternative suspects. Given that kids are able to establish and grasp that, I don't think it's unrealistic to teach them the power of thinking about alternative explanations. And this is valuable not just for the interpretation of data statistics, but more civil discourse. What we see right now is sometimes people cancelling those with different viewpoints. We see echo chambers. We see a lot of mental health issues when a student might be cancelled by the rest of his or her peers.

1:02:55But if we highlight that even on issues that we think are pretty black and white, there may well be two sides to it, then we are able to encourage people to have a greater respect for opinions that might differ from ourselves. And accepting candy from strangers as an analogy to reading data that confirms your prior beliefs is pretty good. Yeah, and it's actually not too far-fetched because this is indeed why things are so appealing. So just like candy is appealing, we want to accept it. We don't want to ask the question. We certainly would like to believe that study and to consume it just like you want to consume the candy.

1:03:31Yeah, awesome. All right, Alex, that's all my questions. We really appreciate you coming back on the podcast. This has been great. Thanks so much for having me back. Really enjoyed the conversation.

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