1 00:00:15,370 --> 00:00:25,770 Speaker 1: Pushkin. Hello, Cautionary Tales listeners. As I'm getting ready to 2 00:00:25,850 --> 00:00:29,410 Speaker 1: kick off my second season on the twenty sixth of February, 3 00:00:29,810 --> 00:00:32,730 Speaker 1: I wanted to share my appearance on a different show 4 00:00:32,850 --> 00:00:37,210 Speaker 1: from our friends at Pushkin and Axios. On this episode 5 00:00:37,250 --> 00:00:41,330 Speaker 1: of Axios Today, I talk with host Nila Boodoo about 6 00:00:41,370 --> 00:00:45,130 Speaker 1: my book, The Data Detective and why people believe things 7 00:00:45,130 --> 00:00:48,090 Speaker 1: that aren't true. We live in a world where, now 8 00:00:48,250 --> 00:00:51,850 Speaker 1: more than ever, you have to be skeptical. That skepticism 9 00:00:51,890 --> 00:00:54,610 Speaker 1: can be healthy, but it can also be used to 10 00:00:54,690 --> 00:00:58,250 Speaker 1: cast doubt on data and statistics that are very real, 11 00:00:58,770 --> 00:01:03,530 Speaker 1: and to spread misinformation. Listen to my conversation with Nila 12 00:01:03,610 --> 00:01:07,810 Speaker 1: and subscribe to Axios Today wherever you get your podcasts. 13 00:01:08,490 --> 00:01:13,370 Speaker 1: Here's the show. Tim Harford is a senior columnist with 14 00:01:13,410 --> 00:01:16,290 Speaker 1: The Financial Times, and he's also author of The Data Detective, 15 00:01:16,330 --> 00:01:19,050 Speaker 1: which is just out this week. Hi, Tim, welcome to 16 00:01:19,090 --> 00:01:22,090 Speaker 1: ax Yesterday. Oh thank you very much for having me. 17 00:01:22,290 --> 00:01:25,010 Speaker 1: So I have to first just ask you. The title 18 00:01:25,090 --> 00:01:27,610 Speaker 1: of this is how to make the World Add Up 19 00:01:27,690 --> 00:01:30,770 Speaker 1: outside the US, but it's The Data Detective here in 20 00:01:30,770 --> 00:01:33,570 Speaker 1: the US. Is this like a Harry Potter situation? Why 21 00:01:33,570 --> 00:01:36,250 Speaker 1: do we have a different title. Yeah, I'm hoping for 22 00:01:36,290 --> 00:01:39,250 Speaker 1: the Harry Potter sales. That would be nice. Yeah, it's 23 00:01:39,610 --> 00:01:42,250 Speaker 1: as simple as the UK publisher didn't like the US title. 24 00:01:42,290 --> 00:01:44,730 Speaker 1: The US publisher didn't like the UK title, and I 25 00:01:44,810 --> 00:01:47,450 Speaker 1: just have to explain it to everybody that I talked about. No, 26 00:01:47,570 --> 00:01:50,930 Speaker 1: I thought it had some greater significance, like statistically about 27 00:01:51,010 --> 00:01:54,530 Speaker 1: the way that Americans interpret words. It's exactly the same 28 00:01:54,570 --> 00:01:57,170 Speaker 1: book all over the world, just a different title. And 29 00:01:57,210 --> 00:02:01,250 Speaker 1: it's all about trying to help people think clearly about 30 00:02:01,330 --> 00:02:06,250 Speaker 1: the world, using, among other things, the tools of statistics. 31 00:02:06,930 --> 00:02:09,290 Speaker 1: And you're right that we might be at a bit 32 00:02:09,330 --> 00:02:11,090 Speaker 1: of a fork in the road or a moment when 33 00:02:11,090 --> 00:02:15,330 Speaker 1: it comes to statistics, particular when we think about the pandemic. Yeah, 34 00:02:15,370 --> 00:02:20,050 Speaker 1: I think so, because we've seen a tremendous amount of 35 00:02:20,330 --> 00:02:26,770 Speaker 1: misinformation and even deliberate disinformation, but we've also seen a 36 00:02:26,850 --> 00:02:31,330 Speaker 1: credible appreciation of just how life saving accurate numbers can be. 37 00:02:31,610 --> 00:02:34,250 Speaker 1: All of the questions we want answering, like, you know, 38 00:02:34,330 --> 00:02:36,730 Speaker 1: where is the virus, who's got it, how's it spreading? 39 00:02:36,890 --> 00:02:41,130 Speaker 1: What are the risky activities? Do masks help, what treatments work, 40 00:02:41,170 --> 00:02:44,530 Speaker 1: do the vaccines work? All of these life or death questions, 41 00:02:45,370 --> 00:02:49,450 Speaker 1: you can't answer any of them without good data. And 42 00:02:49,530 --> 00:02:53,050 Speaker 1: so I think people have started to appreciate that while 43 00:02:53,330 --> 00:02:55,610 Speaker 1: there is a lot of polarization, there is a lot 44 00:02:55,650 --> 00:03:00,370 Speaker 1: of misinformation, they are helping us make incredibly important and 45 00:03:00,410 --> 00:03:03,450 Speaker 1: consequential decisions. But of course, our societal problems and the 46 00:03:03,450 --> 00:03:07,050 Speaker 1: polarization aren't about the statistics themselves. They're about whether we 47 00:03:07,090 --> 00:03:10,930 Speaker 1: believe them. Today twenty one, the idea that statistics are 48 00:03:10,970 --> 00:03:14,410 Speaker 1: a lie is almost accepted fact. Yeah, although it's a 49 00:03:14,410 --> 00:03:17,170 Speaker 1: lot easier to lie without statistics. Let me tell you so. 50 00:03:17,490 --> 00:03:20,490 Speaker 1: I mean that idea goes back, way, way, way way back. 51 00:03:20,490 --> 00:03:23,490 Speaker 1: So the time of Mark Twain, people were talking about lies, 52 00:03:23,610 --> 00:03:27,090 Speaker 1: damned lies and statistics. But for me, at the moment 53 00:03:27,370 --> 00:03:32,130 Speaker 1: that I really identified as significant was nineteen fifty four. 54 00:03:32,490 --> 00:03:35,890 Speaker 1: Because two different things happened in nineteen fifty four in 55 00:03:35,890 --> 00:03:39,170 Speaker 1: the same year. You've got this, to me incredibly dramatic 56 00:03:39,210 --> 00:03:44,010 Speaker 1: illustration of these different views of statistics. There's this one guy, 57 00:03:44,450 --> 00:03:48,330 Speaker 1: Darryl Half, who wrote How to Lie with Statistics, who's saying, yeah, 58 00:03:48,410 --> 00:03:52,570 Speaker 1: it's like a stage magician's trick. You can never trust them. 59 00:03:52,810 --> 00:03:55,410 Speaker 1: It's fun to figure out how the trick is done, 60 00:03:55,410 --> 00:03:59,130 Speaker 1: and I'll show you how statistics are used to deceive you. 61 00:03:59,930 --> 00:04:03,170 Speaker 1: And then you've got these these two epidemiologists, Richard darl 62 00:04:03,210 --> 00:04:05,690 Speaker 1: and Austin Bradford Hill, who are saying this is not 63 00:04:05,770 --> 00:04:09,010 Speaker 1: a trick, this is life or death, and their discover 64 00:04:09,450 --> 00:04:13,330 Speaker 1: that smoking cigarettes dramatically increases your risk of lung cancer 65 00:04:13,730 --> 00:04:17,050 Speaker 1: has helped to save hundreds of millions of lives. It's 66 00:04:17,090 --> 00:04:20,090 Speaker 1: not not a game at all. The irony of that 67 00:04:21,050 --> 00:04:24,050 Speaker 1: bifurcation back in nineteen fifty three is that pretty soon 68 00:04:24,610 --> 00:04:28,490 Speaker 1: Darryl Half, the How to Lie with Statistics guy, ended 69 00:04:28,570 --> 00:04:31,010 Speaker 1: up testifying in front of Congress, basically saying where you 70 00:04:31,050 --> 00:04:33,650 Speaker 1: couldn't really believe all the statistics that showed you that 71 00:04:33,650 --> 00:04:37,570 Speaker 1: cigarettes were dangerous. So it was a very short trip 72 00:04:37,610 --> 00:04:43,930 Speaker 1: from here's a fun book exposing statistical fallacies. I'm standing 73 00:04:43,930 --> 00:04:45,970 Speaker 1: in front of Congress and I'm telling you that there's 74 00:04:45,970 --> 00:04:49,210 Speaker 1: no evidence that cigarettes are dangerous. It's pretty dark. And 75 00:04:49,370 --> 00:04:52,250 Speaker 1: how do you see that direct line from that moment 76 00:04:52,850 --> 00:04:56,930 Speaker 1: with casting doubt on scientists work when it comes to 77 00:04:56,930 --> 00:05:02,650 Speaker 1: tobacco and cancer, to climate science deniers or to what 78 00:05:02,690 --> 00:05:09,490 Speaker 1: we see now, Well, there's a well documented link. Now 79 00:05:09,530 --> 00:05:13,330 Speaker 1: that we've got more than four hundred thousand Americans dead 80 00:05:13,330 --> 00:05:17,130 Speaker 1: and my own country more than one hundred thousand BRIT's dead. 81 00:05:18,690 --> 00:05:23,290 Speaker 1: Now that you know those reassurances have been proved to 82 00:05:23,290 --> 00:05:26,170 Speaker 1: be false, the defense mechanism is to say, oh, well, look, 83 00:05:26,210 --> 00:05:28,650 Speaker 1: the scientist's got a load of stuff wrong as well. 84 00:05:28,890 --> 00:05:32,290 Speaker 1: So they'll, for example, points to the WHO, and they'll say, 85 00:05:33,010 --> 00:05:36,690 Speaker 1: the WHO told us that the infection fatality rate was 86 00:05:36,690 --> 00:05:38,610 Speaker 1: about three and a half percent at the beginning of 87 00:05:38,610 --> 00:05:41,210 Speaker 1: the pandemic, and nobody now thinks it's three and a 88 00:05:41,250 --> 00:05:45,490 Speaker 1: half percent. It's it's below one percent. But that smear 89 00:05:45,610 --> 00:05:49,650 Speaker 1: on the WHO is actually a deliberate distortion. Who never 90 00:05:49,650 --> 00:05:53,090 Speaker 1: said that the infection fatality rate was three point five percent. 91 00:05:53,330 --> 00:05:55,290 Speaker 1: They said something else was three point five percent the 92 00:05:55,490 --> 00:05:59,610 Speaker 1: case fatality rate, and that difference doesn't really matter. What 93 00:05:59,730 --> 00:06:03,170 Speaker 1: I think is interesting is you've got that same tactic 94 00:06:03,330 --> 00:06:07,090 Speaker 1: being used, which is I've been caught out, I've been discredited, 95 00:06:07,130 --> 00:06:10,410 Speaker 1: and I'm going to lash out and others the scientists 96 00:06:10,410 --> 00:06:12,490 Speaker 1: and claim that they've got stuff wrong that in fact 97 00:06:12,530 --> 00:06:15,610 Speaker 1: they haven't. Why is it easier to discredit arguments? Then 98 00:06:15,890 --> 00:06:19,210 Speaker 1: it's almost like an easier fight to discredit something than 99 00:06:19,410 --> 00:06:23,730 Speaker 1: to support something and prove it right. There are so 100 00:06:23,770 --> 00:06:27,530 Speaker 1: many ways to answer that question. I think, really, I'm 101 00:06:27,570 --> 00:06:30,050 Speaker 1: not sure why it's easier, but it is easier, and 102 00:06:30,090 --> 00:06:32,930 Speaker 1: we've got good evidence that it's easier. I mean, you 103 00:06:32,970 --> 00:06:36,370 Speaker 1: just have to look around at the preponderance of negative campaigning, 104 00:06:36,410 --> 00:06:41,130 Speaker 1: for example. But we've got somebody nice evidence in experiments 105 00:06:41,130 --> 00:06:45,570 Speaker 1: conducted by political scientists and psychologists. So there's one from 106 00:06:45,570 --> 00:06:49,210 Speaker 1: the mid nineties that just showed people a bunch of 107 00:06:49,410 --> 00:06:53,090 Speaker 1: arguments about real hot button issues like the death penalty, 108 00:06:53,210 --> 00:06:56,610 Speaker 1: gun control, abortion rights, the sorts of things that people 109 00:06:56,650 --> 00:06:59,810 Speaker 1: get really heated about and feel very passionately about, and 110 00:06:59,890 --> 00:07:05,050 Speaker 1: they ask people to evaluate the strengths and weaknesses of 111 00:07:05,410 --> 00:07:09,930 Speaker 1: these different political arguments. And what the research has found was, Yeah, 112 00:07:09,970 --> 00:07:12,770 Speaker 1: people find it a lot easier to come up with 113 00:07:12,890 --> 00:07:16,130 Speaker 1: arguments supporting what they already believe, and they find it 114 00:07:16,170 --> 00:07:20,330 Speaker 1: harder to come up with arguments supporting the opposing point 115 00:07:20,370 --> 00:07:23,410 Speaker 1: of view. But they found that that's doubly true when 116 00:07:23,410 --> 00:07:27,330 Speaker 1: it comes to negative arguments. People found it incredibly easy 117 00:07:27,850 --> 00:07:33,930 Speaker 1: to produce negative arguments reasons to disbelieve the political positions 118 00:07:34,050 --> 00:07:36,650 Speaker 1: that they disagreed with. And I think that that's behind 119 00:07:36,690 --> 00:07:40,410 Speaker 1: the tobacco strategy, the climate change strategy, now the COVID 120 00:07:40,410 --> 00:07:43,890 Speaker 1: denial strategy, the same basic approach. If you don't want 121 00:07:43,930 --> 00:07:46,810 Speaker 1: to believe this, it's very easy for me to give 122 00:07:46,850 --> 00:07:49,490 Speaker 1: you reasons to doubt, very easy. Indeed, doubt has this 123 00:07:49,610 --> 00:07:52,690 Speaker 1: special kind of power, it seems, it's very tempting. Even 124 00:07:52,810 --> 00:07:55,970 Speaker 1: for people who really respect the numbers and respect evidence. 125 00:07:56,570 --> 00:07:59,210 Speaker 1: It's easy for us to fall into the trap of 126 00:07:59,730 --> 00:08:03,410 Speaker 1: constantly focusing on errors and mistakes, and that, I think 127 00:08:03,450 --> 00:08:06,810 Speaker 1: just feeds into this narrative that the numbers are always lying, 128 00:08:06,970 --> 00:08:09,290 Speaker 1: that they'll never tell you anything useful, and that's just 129 00:08:09,290 --> 00:08:11,970 Speaker 1: not right. So how do we, for example, for someone 130 00:08:12,050 --> 00:08:16,330 Speaker 1: like you who love statistics, obviously, how do we not 131 00:08:16,770 --> 00:08:22,730 Speaker 1: take numbers for granted? I think, just to notice how 132 00:08:22,770 --> 00:08:27,370 Speaker 1: important they have been in the pandemic. The metaphor for me, 133 00:08:27,610 --> 00:08:31,090 Speaker 1: it's like radar. So when we developed radar in the 134 00:08:31,170 --> 00:08:34,890 Speaker 1: late nineteen thirties, that turned out to be an incredibly 135 00:08:34,930 --> 00:08:39,090 Speaker 1: important innovation in the UK. It helped us turn back 136 00:08:39,130 --> 00:08:42,730 Speaker 1: the German Luftwaffe. Then we took radar technology, took it 137 00:08:42,770 --> 00:08:45,850 Speaker 1: to the States and the United States poured an incredible 138 00:08:45,890 --> 00:08:49,890 Speaker 1: amount of money into perfecting radar and perfecting that technology 139 00:08:50,130 --> 00:08:52,690 Speaker 1: because it's just so incredibly important to be able to 140 00:08:52,730 --> 00:08:55,610 Speaker 1: see what's coming at you. And for me, statistics are 141 00:08:55,650 --> 00:08:59,090 Speaker 1: like that. They're showing us the threats, they're showing us 142 00:08:59,290 --> 00:09:02,610 Speaker 1: the weaknesses on our own system. They're showing us where 143 00:09:02,770 --> 00:09:05,930 Speaker 1: policy is working and where policy is failing to work. Now, 144 00:09:05,930 --> 00:09:08,770 Speaker 1: where the supplies of PPE are going, where the supplies 145 00:09:08,770 --> 00:09:12,570 Speaker 1: are accina going, who is suffering most, and who needs support? 146 00:09:12,730 --> 00:09:16,050 Speaker 1: All of these things. You've got no chance of figuring 147 00:09:16,050 --> 00:09:20,290 Speaker 1: out any of this stuff without good statistics, without good data. 148 00:09:20,370 --> 00:09:23,410 Speaker 1: And so it frustrates me when we sit around going, 149 00:09:23,570 --> 00:09:26,570 Speaker 1: oh yeah, lies down, lies and statistics, and we treat 150 00:09:26,650 --> 00:09:29,690 Speaker 1: it as though it's just a weapon in a political argument, 151 00:09:30,010 --> 00:09:32,090 Speaker 1: and it's so much more important than that and so 152 00:09:32,170 --> 00:09:34,290 Speaker 1: much more useful. I think part of this is just 153 00:09:34,370 --> 00:09:37,570 Speaker 1: the natural. Also, we've talked about the human nature and 154 00:09:37,610 --> 00:09:39,810 Speaker 1: just sort of our tendency to doubt. I think a 155 00:09:39,810 --> 00:09:42,930 Speaker 1: lot of us also is how overwhelmed we are with 156 00:09:43,450 --> 00:09:46,490 Speaker 1: the amount of information and statistics that are coming at us. 157 00:09:46,530 --> 00:09:49,730 Speaker 1: And so how do you personally manage that? How do 158 00:09:49,730 --> 00:09:54,770 Speaker 1: you keep from being overwhelmed with information and statistics. The 159 00:09:54,810 --> 00:09:56,810 Speaker 1: first piece of advice that I give in the book 160 00:09:57,210 --> 00:09:59,570 Speaker 1: I think has surprised quite a lot of people. It's 161 00:09:59,770 --> 00:10:06,530 Speaker 1: nothing to do with technical tips on correlations or our 162 00:10:06,650 --> 00:10:09,370 Speaker 1: squared or sampling by so any of that stuff. I 163 00:10:09,490 --> 00:10:14,690 Speaker 1: just say, whenever you see a claim, statistical claim, a 164 00:10:14,730 --> 00:10:20,650 Speaker 1: newspaper headline, ask yourself how you are feeling when you 165 00:10:20,730 --> 00:10:24,250 Speaker 1: see that. Ask yourself what your emotional reaction is to 166 00:10:24,410 --> 00:10:28,450 Speaker 1: the claim. Because so many media headlines, so many social 167 00:10:28,450 --> 00:10:32,530 Speaker 1: media posts, are designed to arouse an emotional reaction. That's 168 00:10:32,610 --> 00:10:34,970 Speaker 1: kind of that's what makes a good newspaper headline, that's 169 00:10:35,010 --> 00:10:37,210 Speaker 1: what gets the clicks, that's what gets the shares and 170 00:10:37,290 --> 00:10:42,130 Speaker 1: the likes. But if you're processing information and you're in 171 00:10:42,170 --> 00:10:47,170 Speaker 1: an emotionally hot state, you're feeling angry, you're feeling vindicated, joyful, 172 00:10:47,290 --> 00:10:50,450 Speaker 1: any emotion at all, you're not thinking clearly. So my 173 00:10:50,530 --> 00:10:53,290 Speaker 1: advice is just you don't even need to count to ten. 174 00:10:53,370 --> 00:10:58,090 Speaker 1: Just count to three, notice your emotional reaction, and then 175 00:10:58,330 --> 00:11:00,730 Speaker 1: go back and look at the claim a second time, 176 00:11:00,850 --> 00:11:05,050 Speaker 1: and you'll already be thinking in a calmer and clearer way. 177 00:11:05,450 --> 00:11:09,930 Speaker 1: I know I sound like some yoga instruction that when's 178 00:11:09,970 --> 00:11:12,810 Speaker 1: the last time you do that? Well, I do that 179 00:11:12,890 --> 00:11:15,130 Speaker 1: all the time. It's like a It's a total habit 180 00:11:15,170 --> 00:11:19,970 Speaker 1: of mind for me, because I'm as vulnerable to emotional 181 00:11:20,210 --> 00:11:24,050 Speaker 1: thinking as anybody else. But it's just a complete reflex. 182 00:11:24,490 --> 00:11:26,930 Speaker 1: When I'm on Twitter, and Twitter is a place where 183 00:11:26,970 --> 00:11:29,130 Speaker 1: there's a lot of angry stuff going on. The moment 184 00:11:29,170 --> 00:11:33,010 Speaker 1: I see something and I'm minded to retweet it, to comment, 185 00:11:33,090 --> 00:11:36,570 Speaker 1: to share, I just hang on a moment, just notice 186 00:11:36,650 --> 00:11:39,770 Speaker 1: my own reaction to it, and then I may, of 187 00:11:39,770 --> 00:11:42,970 Speaker 1: course go on and share it. But I've already started 188 00:11:42,970 --> 00:11:47,410 Speaker 1: to spot the potential errors and the ways in which 189 00:11:47,770 --> 00:11:50,130 Speaker 1: it's not just that other people are fooling me, it's 190 00:11:50,170 --> 00:11:52,810 Speaker 1: that I am fooling myself. And I'm always going to 191 00:11:52,930 --> 00:11:56,450 Speaker 1: keep fooling myself if I'm feeling highly emotional when I 192 00:11:56,450 --> 00:11:59,810 Speaker 1: see these claims. I mean, you have talked about this. 193 00:11:59,970 --> 00:12:02,410 Speaker 1: We have talked about this many times on the podcast 194 00:12:02,450 --> 00:12:05,610 Speaker 1: Acts Us today, and I'm sure anyone who is even 195 00:12:05,690 --> 00:12:08,530 Speaker 1: a mild consumer of news is aware about the idea 196 00:12:08,650 --> 00:12:13,450 Speaker 1: of checking your sources right, checking your emotions. So I 197 00:12:13,530 --> 00:12:16,850 Speaker 1: think I wonder if you feel like that's just the 198 00:12:16,890 --> 00:12:18,850 Speaker 1: world that we live in now, where we have to 199 00:12:19,490 --> 00:12:21,970 Speaker 1: remember to be vigilant about all of these things, knowing 200 00:12:22,010 --> 00:12:24,690 Speaker 1: that people are probably exhausted of being vigilant about a 201 00:12:24,690 --> 00:12:28,290 Speaker 1: lot of other things. Yes, I mean, it would be 202 00:12:28,410 --> 00:12:31,930 Speaker 1: nice if every journalist, if everyone who ever posted on 203 00:12:31,970 --> 00:12:35,810 Speaker 1: social media did all that work for us, put everything 204 00:12:35,850 --> 00:12:41,810 Speaker 1: into context, gave all the sources linked us to complementary 205 00:12:41,890 --> 00:12:44,370 Speaker 1: or opposing points of view, so we could really sort 206 00:12:44,370 --> 00:12:48,450 Speaker 1: of evaluate everything. And the best journalists do the best 207 00:12:48,450 --> 00:12:51,210 Speaker 1: sources really do that. But if they're not going to 208 00:12:51,290 --> 00:12:54,570 Speaker 1: do it, you need to at least be aware that 209 00:12:54,690 --> 00:12:57,450 Speaker 1: someone is trying to get you to feel something. But 210 00:12:58,010 --> 00:13:02,090 Speaker 1: it's important to be vigilant and to be skeptical about 211 00:13:02,130 --> 00:13:04,450 Speaker 1: the news stories that we consume. I think it's just 212 00:13:04,490 --> 00:13:06,930 Speaker 1: as important to be vigilant and skeptical about our own 213 00:13:07,050 --> 00:13:11,490 Speaker 1: filters and biases, because you can consume a diet of 214 00:13:12,090 --> 00:13:16,050 Speaker 1: really excellent news and information, but if you're constantly processing 215 00:13:16,090 --> 00:13:19,010 Speaker 1: it in a very biased way, if you're really yearning 216 00:13:19,010 --> 00:13:21,730 Speaker 1: to each particular conclusion, you're still going to come out 217 00:13:22,050 --> 00:13:24,730 Speaker 1: thinking the wrong things. Can I end by just asking 218 00:13:24,770 --> 00:13:28,050 Speaker 1: what is the what is the one thing you want 219 00:13:28,090 --> 00:13:33,330 Speaker 1: people to take away from your book. If you are 220 00:13:33,410 --> 00:13:37,330 Speaker 1: curious about the world and you want to understand what 221 00:13:37,370 --> 00:13:40,490 Speaker 1: you're being told and how it fits into a bigger picture, 222 00:13:41,690 --> 00:13:46,210 Speaker 1: it's not that hard. Ask the right questions. Be open minded, 223 00:13:46,650 --> 00:13:50,290 Speaker 1: not too open minded, but be open minded and ask 224 00:13:50,330 --> 00:13:53,450 Speaker 1: whether what you're being told is making you smarter. When 225 00:13:53,450 --> 00:13:57,010 Speaker 1: you view information like that, rather than as a weapon 226 00:13:57,050 --> 00:14:00,050 Speaker 1: that might help you win some stupid argument, you're going 227 00:14:00,090 --> 00:14:03,090 Speaker 1: to be smarter about the world. Sim Harford is the 228 00:14:03,210 --> 00:14:05,850 Speaker 1: author of The Data Detective, which is out this week 229 00:14:06,050 --> 00:14:08,250 Speaker 1: in the US, and you can also catch him on 230 00:14:08,290 --> 00:14:12,450 Speaker 1: the Cautionary Tales podcast produced by our partners at Pushkin. 231 00:14:12,850 --> 00:14:15,170 Speaker 1: Tim thanks very much for being with us. I appreciate it. 232 00:14:15,250 --> 00:14:16,330 Speaker 1: Thank you, thank you.