storytelling with data: #22 Alberto Cairo & How Charts Lie

Elberto Cairo joins me today to talk about the dark side of DataViz and his brand new book How Charts Lie. Our conversation dives into the various ways visuals can mislead, why they're misinterpreted, mindfulness as it relates to graphs, and simple things everyone can do to help from spreading misinformation. All this and more in today's episode of Storytelling with Data Podcasts. Welcome to Storytelling with Data. The podcast where listeners around the world learn to be better storytellers and presenters with best-selling author, speaker, and workshop guru Cole Nispomer and Hefley. We'll cover a wide range of topics that will help you effectively show and tell your data stories. So get ready to separate yourself from the mess of 3D exploding pie charts and deliver knockout presentations. And with that, here's Cole. Hi, this is Cole. I'm very excited to have Elberto Cairo here with me today. Elberto, welcome. Hi, Cole. How are you? Thanks for having me. Doing great. Thank you. Where are you joining us from today? Are you home in Miami? Yes, fortunately. Yes. I'm traveling quite a lot these days, but this week I am at home. Fantastic. And do me a favor and look out your window. And can you describe for us what you see right now? Well, right now I'm seeing trees. And then in the background I'm seeing a few clouds because things weather I was going to leave it unstable down here in Miami lately. So it's cloudy skies. Why? How is the weather over there? Well, I was just hoping that we could, it would help us imagine that we're there with you. Is it getting very cold up there? No, but actually what I see out my window right now is a cement truck. We have some construction happening here. So if we hear some background noise, that'll help explain that. That's okay. That's fine. That's fine. I have a fun stat to start us off with, which is that we've been doing this podcast for nearly two years now. And you are the very first person that I've had the opportunity to chat with twice. Oh, wow. That's an honor. Thank you. Yeah, we talked back in May 2018 about truth in data. And at that point, you were pretty well into your writing process of your new book. Can we chat a bit about it then? Given that you've been on the show before, I'm going to skip past the normal intro stuff and point people to our last episode together where they can hear more about your background. I'll make sure I put that link in the show notes. I really just want to jump in and talk about your new book. How charts lie, right? It's said to be published a few short days from now on October 15th. Congratulations. Thank you. Yeah, it has been a long, long way to get this book published. Although it was a pleasure to write it. It's, I think that among the books that I have written so far, it's the, the one that I have most fun with. And I think that it shows in the book. But it has been a long, I mean, it's very different. It's my first book published with a published with a, with a big publisher. It's very, very different. And the process is actually quite different to publishing with a sort of semi academic publishers or professional publishers such as, you know, Wiley or pitch-fid press or places like that. The process takes much longer. Not the writing itself, but the editing, you know, the, the verification of all the facts and stuff, it takes much longer. But it has been a lot of fun. How was the editing process different this time around? Oh, wow. I mean, I had like a two, I had my editor. I have, I had a copy editor. I had a fact checker. I also, I also asked tons of friends to go over the, go over the book just to make sure that there were no mistakes. In the book, although I'm sure that there will be some. But I mean, I try to make this book as, you know, as rigorous as possible, even if it is a book for the general public. But yeah, that, that extended the process. So I would say that it took me around two years to put this book together of those writing itself was probably just five for six months. The rest was again, copy editor in fact, checking, reviewing over and over and over again, like five rounds within five rounds of copy editing. Wow. Yes. Very time consuming. Tell me more about the title, how charts lies, sort of a provocative title, which I'm thinking may have been on purpose. How did you choose the title? Well, I mean, it reflects back to books that are already classics, right? Such as how to lie with the statistics or one of my favorite ones, a mark moment years, how to lie with maps, which I consider one of the mass three books for any visualization, this is a fantastic book. However, I wanted something even bunchier and even more provocative. So I consider how to lie with charts, but there's already a book with that title that was one of the reasons why I didn't use it. And also I didn't do how to lie with charts. I wanted how charts lie because it's much more direct and much more provocative. I already anticipated that the title would would get a little bit of pushback, and I actually wrote about that in my block, anticipating the pushback, for example, oh, the book title is too negative. You're going to cast a negative light over data visualization. I said, well, you know, the first rule that I explain in the book is pay attention and read beyond the title. So if you don't do that, you will not understand what the content is about. And also I remember Hans Rosling's recommendation that if you want to bring people attention to important information, you need to be like the worst tabloid in the front and like the best scientist in the back, like the academy of so you need to be as accurate as possible. But at the same time, when you present your message at first, that message needs to have some some punch to it. And also I decided to choose a title because to acknowledge that visualization also has a negative side. It has a not a negative side, but it can be, it can have a dark side to it, right? So like if people don't understand graphics well, or if graphics are designed in purpose to mislead people, that happens. It does happen. And we need to help people prepare to that dark side of visualization. But the tone of the book, as you know, is positive. It's actually a book that could have perfectly been title. How to become a better chart reader or something like that. That's what the book is really about. Yeah, I will say upon reading it for me, it was more positive, although there were certainly dark moments within it, more positive overall than I maybe expected going in. The audience for this book, and you mentioned this briefly before, but it is different from your previous books, right? The functional art, the truthful art were written primarily for practitioners, but that's not the case here, is it? No, no, it's not. So it's a book that I wrote with several kinds of audiences in mind. So first of all, I wrote it with my dad in mind, right? My dad is a medical doctor. He was a professor for a while. He sees charts and graphs and maps in the media. And sometimes he doesn't pay enough attention to them. He doesn't devote enough time to interpret them. And sometimes he may misunderstand them. I also wrote it with school teachers in mind. So in the past couple of years, I have been doing a series of public lectures about the issues that I described in the book. And in the audience, sometimes I had public school teachers, middle school high school teachers in the audience who approached me right after was saying, how can I translate all these concepts and all these ideas to a language that, you know, 14, 15, 16-year-old can understand? And the book actually is written a little bit with that purpose. There are several examples in the book that are actually intended to be easily adapted to younger audiences. For example, the map that I have of heavy metal band concentrations in Europe, which is, I got a little bit of pushback on that from some initial readers, some preliminaries to say, well, perhaps spending three pages describing that example is a little bit too much. And say, well, but there is a reason why I spent three pages on that is because this is a template that a school teacher can use. And instead of showing a map of heavy metal bands in Europe, perhaps she can create a map of hip hop bands in the United States. And basically just a repurposed that example and use it in the classroom. But ultimately, the audience for the book is just anyone, anyone who is interested in becoming a better chart reader. So my previous books are for chart designers. This one is for chart readers. Coming back to the title and this idea of a general audience, do you think is there recognition that there is a problem? Does the general public feel like they're being lied to by charts? I don't think so. And that's one thing that that really worries me and I talked about it in the talk about in the book. So we, I think that we designers are a little bit too blind for this, but we have spread the idea that visualizations are easier to understand than words or that visualizations are intuitive or that, you know, a picture is worth a thousand words basically. That's the, I actually mentioned that in the book itself, although visualizations are really a picture, it is still an image, right? An image is worth a thousand words or a show don't tell, which is so one of my pet peeves in graphic design, right? We need to show and tell, not show don't tell show and tell. So the general public in general, I think has internalized the idea that visualizations are objective, precise, you know, that they capture the truth. There's information and marks around those words in there somehow and they approach in general visualizations as if they were again images that can be understood, you know, in the blink of an eye. What are you in the book? that the solications need to be taught, and that's the way I teach it and teach in the book. It's like the solicitation need to be taught, not as if they were images. They are indeed images, but they are much more than images. They are arguments made visual. And if you want to understand an argument, you need to read that argument. You need to pay attention to it. And I think that we all can help educate the public a little bit in this idea, in this idea, that if you want to get the right information from a chart, you need to pay attention to it. One of the reasons why I wrote it, by the way, is that in the past three or four years, I have been basically talking to people about how they read graphics, right? In particular, hurricane maps and things like that, and showing maps to different kinds of people. And I have observed that there is a huge gap in between what the designer has in mind, when we design a visualization, and what readers actually get. It's like I summarized these in a title for recent talks. I titled them, "What you design is not what people see." We need to internalize this idea that the mental models that we design and reuse to design our visualizations are usually not the same mental models and schemas that readers use to interpret our visualizations. There is a gap in there. And if we want to bridge that gap, we need to, first of all, explain how to read our graphics sometimes. If our graphics are complex, we also need to assume that the visualization alone may not be intuitive, for say. And also on the part of the readers, readers also need to become more attentive. There is a responsibility also on the part of the reader to be a little bit more attentive to the charts that we see. You've said a ton of stuff. It just in these last couple sentences that I want to come back to. And we will. I've read the book. I was lucky to get an early copy from you. And you've told me in the past, this is not a book for me. But I honestly believe after reading it, that anyone who creates graphs needs to do so. Because it's an excellent reminder both of the many things that, you know, you talk about being an attentive reader, the things that we need to be looking for to be conscious consumers of data. But then also things to definitely be reflecting on as we're creating graphs. But that said, I have a fear that the people who might need this book the most may be the least likely to read it. How do you address that? How do you get this book in the right people's hands and get them to want to read it? Well, remember that what I said before about school teachers using this book in their classes. I am not assuming that a 15 or 16 year old will read this book. It may happen and I will be happy if that happened. But that was not my initial purpose. I mean, it would be great if people who need it will read the book. But what I care more about is that mediators will read the book. One of the reasons why visualizations that are made public sometimes are not understood by the public is that I'm thinking again about hurricane maps, which is occupy a good portion of the central part of the book. Not a good portion, but 10 pages or so in the middle of the book. There is a reason why so many people misinterpret those kinds of charts. It's not because the charts are not intuitive. They are not intuitive just because they present very complex information and it's actually quite hard to make them better, to design them better. The key there is that those charts could be picked up by journalists and newscasters and be explained to the public more accurately. Therefore, the public will understand those graphics and not thanks to the graphics themselves, but thanks to the mediators. So I hope mediators in this case journalists, for example. So I hope that these kinds of mediators, you know, journalists or scientists interested in public communication and so on and so forth will pick up the book and become better chart readers and at the same time better chart explainers. Yeah, that makes sense, right? Those who can influence or explain to help the broader public. Exactly. And get the public excited about charts because one of the messages of the book is that yeah, charts can lie and charts can mislead and we misinterpret them. But you know, if we are careful with them, they are actually very helpful. They can open your eyes to things that you will not see otherwise. So there is a very positive side of charts as well. Let's talk about the negative side for a bit though. So we think of the book is divided into chapters that address specific ways that charts can lie. Can you tell us more about some of these? Walk us through the general structure of the book. Yeah, sure. So I mean, a chart can mislead you for many many different reasons. The main one is that you don't pay attention to it and this is the most common case. You just take a quick look at it. You assume that you understand the chart and you move on, but you actually didn't because you didn't spend a couple of minutes trying to decode what the chart was saying. So that's the first rule. Pay attention. Stop for a second. Take a look at the source of the data if you if you can because sometimes taking a look at the primer source of the data can dispel misunderstandings also. So what is it that the chart is measuring for example? Sometimes you don't know exactly what it is measuring unless you can consult with the primer source of the data. And I explain in the book that this is actually much less complicated than it seems. It's just a matter of spending just a few seconds just going to the premises of the data and taking a look what it comes from who created the data, what the assumptions where and what it is that it is measuring. You don't need to be a specialist or a statistician or a data scientist to do these obviously if you are that's even better. So a chart can mislead just because the data itself is not it's not good or is not appropriate for whatever it is that is measuring. It can also mislead obviously if the if the display of the data is distorted or twisted you know truncating axis in interesting ways or you know changing the scale of maps of core of left maps etc. That could be another way in which a chart can mislead right. A chart can also mislead you because this is a key message of the book because we all tend to project what we want to believe onto the charts that we see. So that's a very important one and there's a rule of chart reading that I described in the book and I mentioned explicitly which is that a chart shows only what it shows and nothing else. Everything else that you see in a chart is something that happens in your brain. It doesn't happen on the chart itself. The chart is just an interface between you and the data and and the interpretation happens in your mind. So you need to be careful with that and you need to curb try to curb at least your own impulses and your own biases. I also talk a little bit about uncertainty the fact that in many cases, the solidizations that don't don't show the possible uncertainties surrounding point estimates and sometimes these uncertainties are really important to understand what the message of a chart can be and I show examples of that. Coming up with the right verbal descriptions of the of the charts that we see is another one that I that I have in the book. So again, this is related to paying attention, is related to not projecting what you want to believe onto the charts that you see but it is very easy to come up with a verbal description of the content of the chart that may bias your understanding of that chart as well and I have a couple of examples of that in the book. In general, being ethical, that's one of the last messages of the one of the messages at the end of the book is a discussion about the ethics of visualization, the ethics of chart making but also the ethics of chart reading. There's another one that I forgot, not including sufficient information. Many visualizations are oversimplifications of data by showing just national rates or averages or mediums when, for example, the spread of the data is very wide or there are extreme values or outliers and so on and so forth. In cases like that, you need to show the data at a more granular level. If you only show the aggregates, then you are not really informing people, you're missing forming them. And you talk about the flip side of that as well right when there's too much data and it overwhelms. Exactly, you can also obscure. This is by the way an idea. This idea of finding them sort of the middle point between too much and too little. This is an idea that has appeared in the past in writings by Howard Weiner. Howard has several books about data visualization and in one of them he has a chapter about misleading charts and he actually talks about this problem. Sometimes it may chart my life because it doesn't include enough information, sometimes it lies because it includes too much information. And what can people do when they're looking at a graph to know if it's lying or if they're being lied to and actually a twist on this because I asked the tourist sphere to pose some questions for you and Tiago asked a similar question, although his framing was a bit different, which was if you had to create a BS detection checklist for charts, what would be the essential items? Well, unfortunately, there's not a quick answer to that question. I mean, the book itself is the answer to that question. The list of things that you need to pay attention to. The bad news is that we will not be able to detect all with the BS in charts, in all the charts that we see every single day. That's impossible. Just because we may lack domain specific knowledge about the charts that we see, right? However, if we apply sort of the little small, very simple principles and guidelines that I described in the book, I tend to believe that we may avoid a high percentage of the cases in which we may be misled about charts like that. Again, taking a look at the primary source of the data, well, paying attention of what has been measured, the description of the chart, whether the chart is distorted or not, and so on and so forth. Those things kind of help us avoid being misled by many charts, not all of them, but at the same time, the prerequisite to all these is, you know, you need to spend time, you need to basically stop yourself, don't rush, don't retweet the chart, which is something that I done myself mindlessly sometimes. So curb that impulse, right? There's just control yourself. Control your own impulse off. Yes, quick media consumption, information consumption. Stop for a second or for a minute or two minutes and read the chart carefully. Well, and I think that's such an important point. And it's one I've heard you make before. And in the book you comment, everyone who has an online presence today is a publisher, which has to be just a terrifying reality for a journalist, for everyone, really. Well, I'm going to wrap you in there. I am a journalist and it doesn't terrify me at all. It's great news. I think that is fantastic that anybody and everybody can publish online, that there's an opportunity for more, you know, for a broader wider and even deeper discourse, you know, and, you know, being able to be exposed to the views of scientists and statisticians and designers everywhere and without being mediated by public, by traditional publications. I think that this is great. The flip side to that though is that, as you mentioned before, I do believe that in some sense, we are all publishers today. And publishers have responsibilities. We have responsibilities. This is one of the reasons why in the previous book, in the truthful art, in which I talked a little bit about this idea, I think that certain ethical principles that are traditionally applied just to journalists and graphic designers and communicators now apply to everybody or should apply to everybody. You know, the obligation of trying to be truthful when we communicate with others, trying to be honest, trying to be balanced as much as possible, double checking everything that we put online. This is a responsibility that belongs to everybody. And I wish that these could be part of, you know, educational systems at the high school level, for example, how important it is to double check everything that we put online because we all have an ethical responsibility to create a better informational environment. I know that there are bad actors out there that obviously, you know, try to lie in purpose and twist information in purpose. But I am an optimist. I think that most people in general are well-intentioned. We don't like to lie. We don't like to lie and we don't like to be lied to. So we can take advantage of that natural ethical impulse that we are all born with or most of us are born with. And you talk about pausing for a moment and checking sources. Are there other easy things that people can do to help from spreading misinformation? Yes, absolutely. And this goes beyond the book itself. Although I talk about it in the book, perhaps in anticipation of future things that I want to write about, which are not necessarily just about graphics. So we all have biases. We all have cognitive biases. We all have ideological biases. We are human beings. That's inevitable. We are all like that. And there are several people who are a little bit pessimistic about this fact. They say, well, humans are not able to overcome their own biases. The world is not going to get better. Information on environments are going to get worse in the future. But I am a believer in the fact that we can all get a little bit better. We cannot control our own biases 100% of the time. But we can all become a little bit more mindful about how opinions are formed in our brains. And this is related to the literature of mindfulness and self-knowledge. I started reading about these many, many years ago. And for example, I remember reading Jonathan Hayes, the righteous mind, in which he explains this idea that in the traditional model of reasoning, of human reasoning, humans begin with the data, then we analyze the data, and then we form opinions. And Hayes says-- and I talked about this in how it just slide-- that's actually completely opposite. What we do is that, first of all, we form an opinion for emotional reasons. And then we try to gather data to confirm and reassure and reinforce that opinion that we formed emotionally. So that's a true fact. It happens to everybody. It happens to me. It happens to you. It happens to all of us. And you mentioned at one point that we're drawn to graphs that show something that we already believe. Exactly. Yeah, we don't look at them as carefully and as attentively as we do to graphics that actually refute what we believe. We tend to look at those much more closely because we want to refute them. Because we don't agree with them. What I have come to believe throughout the years, though, through my own personal experience is that it is possible to curve that impulse. It is possible to sort of observe yourself from the outside and become mindful of when an opinion is bubbling inside your brain and appearing on its own. Because opinions are formed, in many cases, unconsciously. You don't form them yourself. But you can force yourself to either curve that opinion consciously after you notice that it is forming. You can curve yourself and force yourself to stop for a second and tweak that opinion that is the informing inside of your brain. And there are other practices that we can all apply to become sort of perhaps better, I don't know, conversationalists or better interests. I don't know how to put it. But I mentioned in How Chad Sligh, a little bit about the literature on cognitive science and persuasion and stuff. There is a wonderful e-book by British psychologists called Tom Stafford. And Tom Stafford, he has this book, e-book title, for arguments sake, which is a very short book collecting two or three of his papers and writings. But it's super interesting. And in one of them, if I remember well, I don't remember the exact details. But key basically says that one of the most effective ways of changing people's minds, including your own, is that if you have a very strong opinion about something, sit in front of someone who you know disagrees with your opinion and try to explain to that person reasonably why you are holding that opinion, why you have that opinion. And don't appeal to emotions, don't appeal to arguments of authority, try to lay out your own case rationally using evidence, using data, using a string of reasoning that doesn't have gaps in between steps. Why is you start doing that, you realize, oh shit, my opinions don't have any base. My opinions are just, I mean, their foundations are really flimsy. And that's when you start becoming mindful about your own biases. And you start becoming a little bit more careful about what you see or what your brain makes you believe. When you see not only charts, but any other kind of communication or any kind of story. - Well, and I find this so fascinating, right? One of the distinctions you talk about in the book is this distinction between rationalizing and reasoning. Can you talk more about that and the dangers and the opportunities? - We are going way beyond charts. I usually joke that my books are not really about charts. I use writing about visualizations as an excuse to write about other things. These are the things that I'm really interested in. It's not that I don't like visualizations, so I love visualizations. But I love to use visualizations as a springboard to basically talk about other issues that are a little bit wider than these. So this distinction between rationalization and reasoning, which I describe in the conclusion of how charts lie. This comes from several books. For example, what is the title? The Enigma of Reason, the Enigma of Reason, by a couple of cognitive psychologists who explain, they are very pessimistic. These are the kinds of people who say, well, there's no way we can overcome all these biases, right? But the book is extremely interesting. It's very informative. And it explains that basically human intelligence probably didn't evolve to discover how the world really works. We evolve our intelligence to persuade other people of our own opinions and to help them or persuade them to join our groups or our tribes. Therefore, reasoning in this sense is a form of persuasive rhetoric. And that is rationalization because we also use it to convince not only others, but also convince ourselves of our own opinions, to reinforce our own opinions. - That confirmation bias, okay? - Motivated reasoning and confirmation bias, which are closely related to each other. And then my favorite book about this, if anyone is interested in getting into this literature, which is very deep, very wide, there are tons of books about this. So the Enigma of Reason is really good, but it's perhaps a little bit technical as a first book. The one that I will recommend as a first reading in this literature is mistakes were made, but not by me, by a psychologist call a Carol Tathres. Mistakes were made, but not by me. - And on the extra that we link to all of this. - Yeah, yeah, it's an excellent introduction to all this literature. I'll bring an introduction to cognitive psychology. There's ecology of biases, confirmation bias, motivator reasoning. It's an excellent intro. There's also the classic thinking fast and slow. An integral book, it's already a classic, but I find it a little bit dry, a little bit dense, particularly in comparison to a Carol Tathres' book. - All right, let's totally shift gears now and get back to your book. There are a ton of examples over the course of the book and they're really varied from lines from a hundred years of solitude to climate change to metal bands, like you mentioned, and of course a ton of graphs and maps and other visuals. So I'm curious in a couple of things related to all the examples. First off, what was your process for identifying and collecting them all? - Oh, I'm just very active in social media, you know. I just follow tons of people who tweet about visualizations. I basically, whenever I see something that interests me, I downloaded, or are you bookmarked or whatever. So I have like a personal archive. I read a lot, and this is not bragging. I read a lot of books. So about a ton of different things. And sometimes the best examples for visualization teaching don't come from the visualization literature. They come from other areas. For example, the ones about climate change, these are related to the fact that I have many friends who work in this field, atmospheric sciences and climate sciences, et cetera. And they have been dealing throughout the years with a growing literature, this information literature around their research. And they obviously are rightly a little bit angry about all this. So they have their own collections of line charts. And so there's also literature about that. So I borrowed examples from that literature. I'm also very interested in psychology, for example, in politics and economics. So I read widely and broadly. Not very deeply perhaps, but I like to read broadly. I read tons of newspapers and magazines and online, et cetera. And sometimes you stumble upon examples in the most unlikely places. - All right, so you're reading, you're following social media, you're creating these archives. And did you actually make all of the charts in the book or remake the ones that you'd found? - I remake most of them, either based on the, most of the time based on the actual data that the original ones were based on. But I use other people's graphics as well. So there are a few examples in the book that are basically just taking from scientific papers. Those are the ones that look a little bit more pixelated than the others, just because they are beat maps. But yeah, I made most of the graphics myself, yes. - Can you share one of your favorite examples from the book? - Oh, I have so many. I really like because it's so simple. And so is it to understand the one in which I show, and this was actually part of a conversation in social media. The one I showed in which several pandits were using a line chart of unemployment, or actually employment, number of jobs in the market. So if you plot the number of jobs in the private market in the United States, the curve looks like a new, right? So the employment goes down during the economic crisis, and then after 2010 or so, the curve starts recovering, right? The United States are creating more jobs, right? So it looks like a new, with an extended right-hand side arm, something like that, right? And this pandits were using the chart to basically support the idea that the Affordable Care Act, Obamacare, is good for the job market. Because if you plot the point in time when Obamacare was approved, it actually coincides more or less with the point in time when the curve changes direction. So, I mean, you can quickly jump to a causal conclusion in there, right? It's like the curve is change in direction, employment is getting better. Therefore, it's not true as conservatives say that Obamacare is bad for the job market. It's actually good, because the curve changes direction and the job market is recovering. This is one of the examples that are used in the book to say, you know, charts show only what it show and nothing else, all that this chart is showing is that there's a coincidence in time. But it doesn't really mean that one thing is really connected to the other, right? The only way you can do that is if you go beyond the chart, you look for more research and see whether Obamacare as an influence or not in the job market. I'm not saying that it doesn't. It may have an influence in the job market. I don't know. All that I'm saying is that the chart doesn't really help you prove either that idea or the opposite idea. It's not useful for that, because this phenomenon can be completely unrelated. And it's also a great example, because it's also useful to explain people to how important it is to think about alternative scenarios, you know, counterfactuals. What would have happened if Obamacare was never passed with the curve, you know, with employment recover much more quickly? Meaning that Obamacare is bad for the job market? Would employment recover much slower? Meaning that Obamacare is actually good for the job market? Or would the curve looks this look the same? Meaning that Obamacare has no influence whatsoever on the job market? We don't know. And we don't know because the chart shows only what it shows and nothing else. So that will be one of them. I don't know. Perhaps I just spoke a little bit too long on this, but I have-- No, that's good. And your explanation is a good illustration of one of the things that you say in several ways over the course of the book, which is that one of the things that good graphs do is just allow you to pose good questions. Exactly, yeah, that's one of the virtues. One of the good side, part of the good side of visualizations, right, that visualizations are, or can be, or should be, conversation enablers, right? They should enable informed conversations about important issues, only that we need to use them well. We need to read them well, right? And sometimes we don't. Another favorite example, by the way, I just remember this one, is the chart that is showed on the scatter plot of cigarette consumption per capita and life expectancy. And it's actually a positive association, right? The most cigarettes people consume on average country by country, the higher the life expectancy is. And I know that some people will read this chart wrong, because I used to read that kind of chart wrong. And I describe that kind of chart wrong myself in the past, describe it as the more we smoke, the longer we leave. And that's actually not true. That's not what the charity's showing. The charity's only showing that there's a positive association between cigarette consumption and life expectancy at the national level. But that doesn't mean that the two things are related to each other. It doesn't mean that they're not confounding factors and there are many confounding factors. And it doesn't mean that that association that you see at the national level will not disappear at the individual level. And it does disappear at the individual level. It actually reverses, because the association between cigarette consumption and life expectancy is negative at the individual level. So it's a great example that can be easily repurposed by school teachers or by professors or whatever to teach the ecological fallacy and Simpson's paradox. Right, 'cause it illustrates a couple of these things, right? Charts can lie because we're aggregating when we should be disaggregating or drawing conclusions that are. Well, we are drawing conclusions about a particular level of aggregation based on data that is aggregated at a completely different level of aggregation. And again, I will emphasize, this is something that I have done myself. And I wrote about this in my blog. There is an example of this in my first book. In the functional art, there is a charting, which I described this chart, unfortunately, this way. It's not, I mean, the chart is not wrong, per se. But the description of the chart that I wrote may bias people's perception of that chart. - Well, and this comes back to this idea of, you talk about graphecacy and the grammar of graphs that you need to have, and you mentioned mental models before, right? You need to have shared mental models for good effect of communication. So some understanding between both the designer and the reader about what the chart's about, how data's encoded. So what are some of the implications for those designing graphs? How can we know what mental models our audience is working from? - Well, we really cannot know beforehand unless that we are presenting our graphics to an audience that we are very familiar with, right? Internally in our company, for example, if you know really well the people who are going to read your graphics, you can sort of anticipate how you're going to read that. The problem is when you don't know who the audience is going to be, and then you basically just need to rely on your own assumptions on your own intuitions. There are several things that we can do though. So for example, we could try to test our graphics a little bit more often, right? And this is something that visualization designers can easily do because it can be done through, you know, you can do it formally through focus groups or an instructor interviews. There are several research methods that may help you understand how people understand or misunderstand your graphics. But you can also do it informally, just show people in your family, friends, et cetera, who are not visualization designers or statisticians or business analytics, people, whatever, show them your graphics, let them read them for a couple of minutes and then go back to them and ask them, what do you learn? Talk to me about what you learn from the graphic. And then you need to record the answers. 'Cause then if you do this a little bit more systematically and in the long term, you will start identifying patterns, you will start identifying ways in which people systematically misinterpret that particular kind of chart. So that's also research is not as rigorous as scientific research is, but it can still inform your practice as a designer. And another thing that we need to do is like going back to the reference that I made before about Hans Rosling, right? Rosling, I think that he's one of the most important figures in the history of visualization, not only because he created Gap Minder or because he was a great presenter and he was or because he wrote what's the title of his book. - Tuck fast. - Yes, he's also very important because if you pay attention to the style of his lectures, whenever he presented a visualization, he didn't just talk about the content of the visualization itself. He usually, before he do that, he usually explained how to read the visualization. And this is the show and tell part that I was explaining before. So if we were showing a scatterplot, he said, take a look at these, position on the x-axis is whatever, position on the y-axis is whatever, bubble size is whatever, color means whatever. So he was describing the grammar of the graphic, the symbology of the graphics, these are the symbols, but also how those symbols were arranged, to convey information and that's the grammar of the graphic. And then he moved on and started explaining the content of the graphic itself. That is the show and tell part that I was talking about before. Well, and graphical literacy has become, and data literacy has become sort of a buzzword lately. But that seems like an excellent place to start, right? Is for people who are presenting a graph to, you know, whether live or sending it around, if you know, if you're there live, set it up for your audience, talk them through what they're going to be looking at. I think one thing that Hans Rosling often did well, also that worked well is he'd set the graph up. But then after he did that, put the data on, he would talk about specific points and again, relate that back to what that meant or how they related to each other in a way that reinforced the encodings and how to read it and, you know, the grammar of the chart. Correct. I mean, he became, he became, so to speak, the annotation layer of his graphics, right? And we can do that. We can all add annotation layers to our visualizations, not only verbally, but also textually. It really surprises me sometimes that, you know, I would not say that it's everybody, but you know, that certain visualization designers don't pay a lot of attention to the words that we write to surround our visualizations, right? Our titles, our introductions, our annotations, our food notes. Those elements are not just, you know, byproducts or, you know, secondary components of our visualizations. They are intrinsically connected to the content of the visualization itself, and they may reinforce the content of your visualization. And I like to tell people or remind people that if you made the graph, of course, you're going to understand what it shows. Of course, you're going to understand the encodings, but for anybody else, you need to put that down. It's a mental model problem. The mismatch between the, it's the curse of knowledge. There is even a term for that, the curse of knowledge. You know so much about the content of your graphic that you assume that everybody else will know the same, you know, will be at the same level of knowledge as you are. And that's usually not the case. Yeah. And that happens generally right as well for people who are working with graphs regularly that we lose sight of the fact that the things that seem obvious to us are not obvious to the general person. Correct. I mean, it happens. Again, it happens to all of us. And one of the best, by the way, one of the best antidotes to this problem is to become a teacher. Teaching people, again, you know, beginners or young students or whatever, it makes you more careful, I would say, with all these problems. It makes you aware of how much you take for granted sometimes. And that's even with common graph types, right? What room is there for innovation? So this was another question that someone on Twitter posed to Lena asked, is there innovation in chart types and maybe more specifically are innovative charts really helping anyone? Well, all right. So we can think about these in many different ways. And I, I, I think that I address these balance between traditional and innovation in my previous book in the truth for a lot. And I'm not going to quote myself they're waiting here. But I believe that what I say is that there is a place for everything. There is a place for traditional graphic forms that are, you know, well-established, bar graphs, line graphs, even scatter plots are becoming more understandable and more popular fortunately. So traditional graphic forms that we may, may believe that a high percentage of our audiences are going to understand, well, in general. So most of the time we should be using those kinds of charts, particularly if what we want to do is to convey a message quickly, effectively, or if we want people to make decisions about, or based on the data. I think that we should default for the more traditional graphic forms, the more conventional ones in the good sense of the word conventional. They have become so common that they have become conventional. They are part of the common language that everybody uses. So if the purpose of the graphic is to quickly extract information or make a decision based on the data, it's a particular situation of emergency or whatever, then I would default for the traditional graphic forms. But I believe that our responsibility as designers doesn't end with the responsibility that we all have towards the reader or the viewer. We also have a responsibility towards the craft itself. And that means that we have a responsibility to improve the craft, understand the language of the craft, innovating the craft, and come up with better ways to display data. The scatter plot was a novelty in the 19th century. It was an unusual graphic form in the 19th century, but 100 years later, now it's part of the common language. Bargrass and nine charts, they were a novelty in the 17th or 18th century. Right now they are part of the language that everybody uses when we communicate graphically. It may happen, for example, that 10, 20, 30 years from now, a tree maps or connected scatter plots or more weird graphic forms. They look super strange and super innovative and novel nowadays. But 10, 30 years from now, they may be part of common sense and maybe part of the educational systems. So again, we have a responsibility also to expand this vocabulary, the language of visualization. I love that idea. Right? That it's a responsibility. It puts a different sort of onus on it than innovating for the sake of, I don't know, I think sometimes we do stuff differently just to do stuff differently, but the responsibility factor puts more weight on that. I don't think that that's necessarily that. You know, being playful, trying something new just for the sake of trying something new, just because you like it. I think that's a good reason to justify a particular design, a particular design decision in one context. It's only that you need to pay attention to the context. So that's not appropriate if you're going to do a dashboard for business decision making, right? Or for rapid decisions in a situation, you know, of chaos or natural hazards or things like that or risk communication. In those cases, you need to default to the things to the graphics that are more common and more easy to interpret in one sense. But if you're doing a project for yourself, you know, something that is a little bit more experimental, you know, that is not about a topic that is too controversial or it's completely appropriate and it's a perfectly fine reason to say, I want to use these wacky graphic form just because I like it. And I want to see what happens. And then I'm going to put it out in the open and see how people react. If they don't react well and they don't understand a thing, I will go back to my drawing board and try something different. But what about if they respond well? What about if I discover that a horizon chart, for example, works in certain circumstances, right? Horizon graphs were a novelty, I don't know, 10 years ago or something like that. Nowadays they are still not very common, but they are not hard to understand once you grasp the grammar and the conventions behind them. They are not hard to understand at all. It's only that they have not become part of the common language yet. Yeah. And this is the positive side of everybody being a publisher today, right? Because we can put stuff out there and find things that 10 years ago would have been impossible. And establish a dialogue with other people and be in open to responses and to critiques and to say, you know, you just get replies from people telling you, well, I don't think that this works because such and such. And not taking those comments personally, but taking them as comments on your work. And that may improve your work. Let's shift gears maybe one last time. And I want to talk for a bit about story. Andy Cotgrave responded to my tweet for questions for you and posed one that I'm also very interested in your opinion on, which is, are the techniques of lying the same as the ones you would legitimately use to tell a particular story based on the data? The same techniques. Well, I guess that they are not the same techniques, because if you really want to lie, what you do is to twist the message, right? If you want to build a narrative based on data or based on graphics and your purpose is to inform, you do it with that assumption in mind and you shape your story or your narrative to improve understanding, not to destroy understanding. The techniques are the same in the sense that you use the same, let's say, templates or structures to display the information, right? And you use the same encodings and you use the same grammar to write your story, et cetera, and so on and so forth. So they are the same in that way. What makes the difference is the intention behind that, I would say. And what about, and maybe this is coming back to this shared mental model idea, I'm not sure, but when the intent of the person creating the graph is to inform, but by doing so in a way that's trying to be influencing, the recipient or the audience feels like they're being led to a place that isn't quite true. I maybe I'm sort of talking in circles a little bit, but one question that gets raised a lot to me in workshops is this idea of, you know, they're being a way that you can show data that's going to answer a question that is not biased, which is this sort of faulty expectation of what a graph can do, right? Coming back to what a graph can do and what a graph can't do. But related to that, sometimes get pushed back just on this idea of story or of telling a story with data as if that is a negative thing. When I don't think it is when it's done in the right way. Well, I tend not to use the word story as, you know, because a story relates at least in my mind the way that I usually have understood the word story. It always has sort of like, I don't know how to put it, like an emotional component, although emotional is not not emotional is not necessarily bad. But it's like, yeah, tendentious bias. It's like, not necessarily bad. I mean, you want to provoke an emotion. When we tell a story, one of our goals is not just to convey information, but to provoke a feeling. First of all, a sensation, and then the feeling of that sensation. And that's not necessarily bad, but in certain circumstances, again, when the purpose of the graphic is to communicate effectively, right? Perhaps we should talk more about narrative, because narrative doesn't have the same implications. It doesn't have the same connotations as the word story, because narrative is mostly about structure. So it's like a step-by-step one, two, three, four, five is structure in which you try to just lay out your case as clearly as possible, right? And you basically just do it that way. So it speaks about this structure, more than it speaks about your goals when designing that graphic to provoke an emotion. I get pushed back sometimes also on the same, because I am a journalist, so I use the word story all the time, casualty, but you know, we need to be careful, because again, different audiences interpret that word in different ways. So I try to be more careful nowadays. I use the word narrative, because that doesn't have the same load, right, or the same charge. It's not a charge word, so to speak, the same way that a story is. - Yeah, it's interesting, right? Different, yeah, the different associations that people have with different conversations. - Yeah, yeah, it's completely, particularly scientists and statisticians, they are very wary of the word story. And I think that for good reason, 'cause they say, well, we don't do stories, we do arguments, right? We build arguments. And arguments are not necessarily stories, they are arguments. - I don't know, but there's tension there, right? - Absolutely, I'm not debated. I'm not saying that they are right or wrong. I'm just trying to put myself in the shoes of people who speak like that. And I'm trying to, basically speak, I'm trying to understand where they come from. And I perfectly understand what they come from. And as I said before, to bridge the gap between the way that we describe things and the way that other people describe things, the word narrative is much less loaded than the word story. Or essay, I really like, for example, the fact that the pudding, you know, the pudding, the pudding, well, you're going to put links to all this stuff in the website. - Yes, that's right. - Everybody refer to that. So the pudding, this website that collects data stories, right, and creates data stories, they don't call the restores stories. They call the restores essays. - Which is funny because I have a visceral negative reaction to essay. - I was, I was, sounds like it's gonna be boring. - Yeah, yeah, that's the bias. But you know what, that's something that I discovered as a spaniard or something coming from Spain. In Spanish, it doesn't have the same connotation. And I think that it has to do with the fact that here in schools, you're always asked to write your personal essay about whatever, and you think that it's actually boring. For me, essay, essay is a beautiful word because essay, at least in Spanish, means an argument in which you are sort of arguing with yourself or reasoning out loud, your case, to yourself and also to others. And you may reach a solid conclusion, but you may not. So an essay is open ended, it's part of a conversation. It's a beautiful word, at least in Spanish. And it speaks to me quite, I yield really like the word essay. And I sometimes try to use the word essay in the subtitles of my books. And I got pushed back from publishers, "Well, this will sound really boring. "Nobody's going to read it." And now I understand where you all come from. - Yeah, I think it does stem from school probably. Which is funny because I didn't dislike writing essays even. - I mean, I guess that you could use also the word argument or whatever, but for me, essay has different connotations because an argument sounds very strict and very rigorous. An essay doesn't need to be rigorous. It needs to be recent. It needs to be based on reasons, of course. But it can, you know, draw evidence or ideas from multiple sources, pulling them together. Again, an essay is like thinking out loud in front of other people to have a conversation with other people and also being open to receive feedback on whatever it is that you're writing about. Again, it's part of an open-ended conversation and I love conversations. - Well, when you describe it like that, it sounds lovely. (laughing) - Okay, I have a totally, maybe off the wall question, but I'm going to kick myself if I don't ask you because I was super curious about it as I was reading. It's probably not even directly related to the content of the book, although maybe it is, but your color scheme, right? It's unusual to see a book about graphs, not in full color, though clearly this was intentional. Tell us about the colors in your book and how you chose them. - Well, it's a limitation in terms of production costs. So I would love the book to be full color, but that will make the book extremely, extremely expensive. So I said, when I sent the proposal for the book, I said, you know, ideally, it should be full color. But if it full color increases the cost of the book, you know, enormously, we can just do it with two or three colors. That's how we land it into sort of these faded gray and faded red a color scheme. So it's a limitation in terms of cost. - And it looks very striking as you flip through what made you land on red? - Well, because red is very striking. But I try to tone it down a little bit. It's not, I mean, I don't know if it looks extremely red, but we actually tone it down a little bit. So it looks sort of orangey, right? Rather than pure blood red. Yeah, but it's just a matter of, I could have used orange, orange was another one of my preferences, but it doesn't print as well as red on the page. And it doesn't contrast that well against, again, sorry, against gray on the printed page. - Yeah, and it was really interesting to me because some of these graphs that you remade that would have originally been full color in gray scale and red actually work quite well when that's done thoughtfully. - All right, well, I'm happy to hear that. - And a different color that I consider was blue, also using blue in gray. But it doesn't contrast as well with gray, right? Again, it's not, I'm not going to claim that I'm the best designer in the world. I'm pretty sure that professional designers will have pet peeves around these. You should have used a different color. But again, it's related to production costs. So those are the constraints that you always have when you want to create a mass market, a book such as this one. You need to deal with those constraints. Ideally, you should have been full color. But again, that will make the book, will make it much, much more expensive. - How did you choose the cover art? - I didn't choose it. Well, I actually chose it, but I didn't design it. So I was the designers at WWW North when they sent me several options. The first one that they sent me, I didn't like at all. And I said that openly, although I'm very constructive when I give criticism. I said, "You know, I don't think that this will work." It was yellow and it had a very weird silhouette of a person reading a graphic. I didn't really like it. It didn't have a very strong message. And then I said, "You know, other books that you have published, they have very strong, very concise and very clear covers." And I'm thinking about, I mentioned this one explicitly, Charles Wheelans, a naked statistics, for example, which is also published by WW North. I said, "You know, I really like Charles's book. This is excellent." And Wheelans ended up writing a book for how it should slide as well. But anyway, his book is fantastic. You should all read it, naked statistics. But pay attention to the cover. The cover is like super simple, super basic. It's funny, it's also quite funny. So I sent that reference to the designers. And I said, "You know, it would be great to have something like this." And they came back with this idea of the two bars, in a bar graph, and then the shadow is being the complete opposite of the bars themselves. And I thought, well, this is perfect. It has great contrast. It's very concise. It actually conveys what the book is about. So let's go for it, it's great. - And you talked about, when we tuned in today, that this was the book you had the most fun writing. I want to come back to that. What made this the most fun book to write? - Well, I laughed at all. I mean, I got to use bad words in the book. I curse a little bit here and there. There are a couple of F-bongs here and there. So it's like, how can you not have fun with that? So I had fun with several examples. I don't know, a few examples that are a little bit sassy. So to speak, and like that, I got to talk about hard rock and heavy metal in the book about visualization. And I had a lot of fun with that. I don't know, and I also talked about things again, that I really care about. So I'm not going to speak very a lot about these, but if you read the conclusion of how it should slide, it gives you an idea of what I would like to work on in the next four or five years. It describes these themes that I'm most interested in. So the conclusion of how it should slide is actually an essay. It's an essay that I wrote to myself as sort of laying the ground for perhaps future books or future articles, et cetera. So I also had a lot of fun with that as well. Interesting. Yeah, because so we've had what's been a fun conversation and felt very optimistic here today. I will say in reading the book, though it was more positive than I expected, things work pretty dire at points, right? A ton of examples of things done wrong or so easily misinterpreted. But as you mentioned, you end with a much more positive set of examples also. I won't go into the details, save that for those reading. But your outlook overall on the use of graphs is a very positive one. I am a visualization designer. I'm a great believer in the power of visualizations. I think that they are great. It's only that we cannot just talk about the positive side of visualization. How great visualizations are, how fantastic they are, the possibilities that they give us. We do need to talk about that. But we also need to acknowledge the fact that we need to also help people who are not visualization designers become better chart readers. And then everybody will be happy. Everybody will be able to design charts and read them better. And then we can use charts to have better conversations, which is the whole purpose of the book. I think that's a great point to wrap things up with. Alberto, this has been a lot of fun as always. Are there any final thoughts you'd like to leave listeners? I don't know. Just keep designing great visualizations that I may enjoy. Post them in social media. I love to see everybody's work. And if I really like it, I would probably promote it and help you spread the word about whatever you're doing. I'm very interested, by the way, in visualizations, designed in non-English speaking countries. Because I tend to believe that-- and this is a problem with how charts live, by the way. It's very US-centric. And one of the things that I offer myself to do for international editions, it's going to be translated in several languages. And I say to my publisher, well, talk to the international publisher and tell them that if they want me to change a few examples and create examples just tailored to their own countries, come up with to doing that. Because I came to believe that most of the visualization books that we have nowadays are too centred in the English speaking world. And we need to deal with that. We need to see what's going on in India. We need to see what's going on in China. I don't know what is going on in all African countries. I know very little about visualizations. Other than Egypt, I'm a little bit familiar with data visualization designers and data journalists in Egypt. But that has nothing to do with my last name, by the way, because I'm from Spain, not from Egypt. But it's just completely unrelated. But other than Egypt, I don't know what's going on in the rest of Africa. That's a huge continent. So what's going on over there? Are there great visualization designers that I'm not aware of? I want to know about them. So yeah, this is just a random thought. So if you're interested, just contact me. It's very easy to find me. Add to me on Twitter, yeah. And we'll make sure we put all of your website, Twitter, all of that information in the show notes so that people have an easy time following your work and finding you. So Alberto, this has been great. I wish you much success with your work. Well, why do you have a book yourself and you've just came out? Just about a couple weeks. I say the same thing. I wish you all the luck with that. Thank you very much to those listening. Pick up your own copy of How Charts Lie. And I encourage you to get a second one to give to a friend who maybe doesn't even know they need to read it. We can all help improve graphical literacy one book at a time. Thanks very much for tuning in. Thank you. [BLANK_AUDIO]