LISTEN TO THE EPISODE WITH ALBERTO CAIRO:
THE SUBTLE WAYS YOUR CHARTS CAN LIE WITH ALBERTO CAIRO
Alberto Cairo is not only a highly revered mind in the data visualization world, he brings a fresh perspective on keeping your charts honest in his brand new book, How Charts Lie..
If you’re looking for a way to create and interpret charts accurately and in integrity, this interview is for you.
Alberto is a celebrated visualization educator, designer, and consultant. He is the Knight Chair in Visual Journalism at the School of Communications of the University of Miami and he is also the director of visualization program at UM’s Center for Computational Science.
He is also the author of two other data viz Bibles The Truthful Art, and The Functional Art.
In this episode, Alberto Cairo provides his unmatched wisdom on chart creation and interpretation. He debunks the myth of “a picture speaks a thousand words” when it comes to visualization and shares how to create charts that are as close to the truth as possible.
In This Episode, You’ll Learn…
- What Alberto’s new book explores the different positive and negative influences that charts have on our perception of truth.
- What he believes was at the root of #SharpieGate and many other media-fueled debacles over data viz
- His thoughts about the myths of charts and how to think about them differently.
- How no statement we make is absolutely true and what we can do to try and move toward the truth end of the spectrum.
- The importance of expressing your level of confidence in facts you are presenting, but to also understand there may be other interpretations.
- The “Me” Factor and how you can use it to make your visualizations more engaging to your audience.
- Ecological fallacy and amalgamation paradoxes that should become part of a general knowledge.
People, Resources, & Links Mentioned
- How Charts Lie: Getting Smarter about Visual Information by Alberto Cairo
- The Truthful Art: Data, Charts, and Maps for Communication by Alberto Cairo
- Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations by Scott Berinato
- The Subtle Art of Not Giving a F*ck: A Counterintuitive Approach to Living a Good Life by Mark Manson
- Spurious Correlations by Tyler Vigen
- Mistakes Were Made (but Not by Me): Why We Justify Foolish Beliefs, Bad Decisions, and Hurtful Acts by Carol Tavris
- Thinking Fast and Slow by Daniel Kahneman
- INZight
- Settlers of Catan Board Game
How to Keep Up with Alberto:
Thanks for Listening!
Thanks so much for joining me. Have some feedback you’d like to share, or a question for Alberto? Leave a note in the comments below, and we’ll get back to you!
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A very, very special thanks to Alberto for joining me this week. And as always, viz responsibly, my friends.
Do you have a burning question for Alberto about how to make an honest chart out of your data viz? If so, ask away!
Lea Pica: Buenos Dias, Lea Pica here, today's guest is a royal member of the World Data Visualization Court, who's here to make an honest chart out of your viz. Stay tuned to find out who's taking us to school on the Present Beyond Measure Show. Episode Forty-Nine.
Lea Pica: Hello and Happy October, dear listener. Welcome to the forty-ninth episode of the Present Beyond Measure Show, only one away from the big five-oh. I can't believe it.
Lea Pica: This is the only podcast at the intersection of presentation, data visualization, and analytics. This is the place to be if you're ready to make maximum impact and create credibility through thoughtfully presented insights. And today you are here either because you love to hear me riff in the beginning, or you are ready to hear an absolute firehose of incredible wisdom given by one of this industry's most respected minds in the data visualization field.
Lea Pica: Fall is settling in and it's getting cold. Dang it. Really a summer person. But the changing of seasons is always a fantastic way to reflect on what we're ready to let go of and what we're ready to let in, such as new data storytelling skills. So if you're in the Boston area in a few weeks, you should come meet me at Digital Summit. I'll be delivering my signature PICA Protocol keynote. Your prescription for healthy, actionable, digital data stories. The link to registration is on the show notes page for this episode at leapica.com/049 and that is October 21st to the 22nd.
Lea Pica: And if you are really, really ready to clean out your skills closet and make room for new. Ready to take your data presentation, game to an eleven, you have to check out my Data Presentation and Storytelling Boot Camp course bundle. It is a series of comprehensive, immersive online courses that are on-demand and this is the blueprint that I wish existed when I started presenting data as a digital marketer 15 years ago.
Lea Pica: Now I've divvied up my flagship private workshop into three chunkable courses, packed with over 40 video modules, tons of resources, printable checklists, articles, every little bit that I could find in my kitchen to throw in the sink. It's comprehensive, it's practical with neuroscience and cinematic storytelling principles all woven together with a really practical approach you can use again and again so you can buy these courses separately. But however, I am offering a very special fall bundle price for all of these courses because I believe that the whole outweighs the sum of these parts. So if you want to keep your audience engaged and off their phones and inspire action on your insights, visit leapica.com/bootcamp and sign up.
Lea Pica: So, as always, I am pumped to bring on today's guest, but I have to say this interview really stands out. This was an exhilarating conversation with one of the most revered minds in the field of databases, and it sought to answer an age-old question that permeates our daily data-driven life. The question of is this chart right or wrong? But to answer, as you'll see in the next few minutes, we turn that question on its head. Not by asking whether it's right or wrong, but rather how wrong is it? Hmm. So we covered a lot of ground in this interview. So get cozy, settle in with a pumpkin spice latte and get ready to put the fall in faulty data viz. It's a stretch. I know.
Lea Pica: Hello. Today's guest is a celebrated visualization educator, designer, and consultant. He is the Knight Chair in Visual Journalism at the University of Miami and the Director of Visualization at UM Center for Computational Science. He is the author of a data viz Bible called The Truthful Art Data Charts and Maps for Communication. And he's here to talk more about his brand new book coming out in just a few days called How Charts Lie. This is a very exciting book in this field. He began his career in infographics and data viz in 1997 and became a professor of the University of Miami in 2012. And he's also a consultant for companies such as Google. I can't wait to introduce Mr. Alberto Cairo. Welcome.
Alberto Cairo: Thank you. Thank you so much for having me. That was a very long introduction. I appreciate it.
Lea Pica: Well deserved. So I am. I just wanted to have you on the show for years now. And I'm so excited that I'm able to have you on to talk about your book topic, which has been a real new passion niche in the field of data viz, which is the ethics of data visualization and the truth of it. So, you know, after I read a book called Good Charts by Scott Berinato, he really started to unpack a lot of the dogma around what's right and what's wrong and how we are responsible for trying to communicate as ethically as possible. So I'm so excited for you to talk more about your book, How Charts Lie and for me and going through it. I felt like the heart of your book seemed to lie within this quote, which was “a chart shows only what it shows and nothing else”. So can you start by speaking to what does that mean, as, you know, the essence of what you're trying to teach?
Alberto Cairo: Wow. Yeah, that that's at the core of the book. So you basically got to one of my favorite sentences in the book. All right. So I would try to be as concise as possible. So the basic argument of the book is that for four decades, many people have approached legalization as charts and graphs and maps, where pictures or illustrations that can be understood intuitively, quickly, easily. You know, we all have heard, you know, the old dogma of the old myth. A picture is worth a thousand words to that visualization. These should be as simple as possible. Data should speak for itself. That which is so, so very common. And the book basically tries to debunk all those myths to propose the idea that this realization is basically an argument made visual based on data.
Alberto Cairo: So you want to understand the chart. You can not assume that you can understand a chart just by taking a quick look at it. You really need to pay attention to it and you really need to ask yourself, what is this chart showing?
Alberto Cairo: And what is this chart not showing? And moreover, what am I seeing in this chart? That is not really in the chart, right.
Alberto Cairo: That I am imagining are my projecting my own beliefs? Am I projecting my own expectations or beliefs onto the chart that I'm seeing? And that is what I tried to convey in that sentence, that a chart shows only what it shows and nothing else. I could point out several examples. The classic there's a couple of examples in the book that discusses call variation versus causation or correlation versus causation. That's a perfect example of that. But it really applies to any two any chart that really applies to any kind of graphic that is intended to depict or to convey data.
Lea Pica: This is this is so interesting. It's about filling the gaps in what you're not seeing. And for me, what your book. Maybe get a little philosophical, if I may. You know, for me, this has been a year of exploring what the word truth versus lie actually means. And people are saying, well, this is that these are the facts and this is the truth. And a good friend of mine once said to me, no one has a monopoly on the truth and that it's impossible to observe and relay the truth as a human filter because there's no lens that doesn't create a bias. So for me, you know, sometimes I wonder, like, is it being more responsible to come out and say, like, when we humans display data to each other, we are lying as little as possible? Or we've attempted to be as least uncertain as possible?
Alberto Cairo: That's that's absolutely correct. And that is what I try I address the notion of truth and truthfulness a little bit more seriously in the previous book, that truthful because the truthful art is more for it for people who already work in data visualization or in journalism, etc. So I discuss this a little bit more in-depth, although it also permeates how charts lie, which is basically a book about a book for the general public. The title, by the way, of how shots like would have been how not to lie with charts and to make charts right.
Lea Pica: Because they're not lying.
Alberto Cairo: Exactly. They are not lying. Right. They are not deceiving. That touches lies about how charts mislead, how they meet, deceive, etc.. In the truthful out, though, I describe what I like to call a spectrum of truthfulness. So whenever you know, no chart, no data visualization, no statement that we made can be absolutely true.
Lea Pica: Right.
Alberto Cairo: What we can do is to try to move with your spectrum from which to act. We do endpoints being absolute truth and an absolute untruth right on one end until the other end. What we can do is to try to attempt to move towards the truthful, to the true, towards a truth and all the over the spectrum. But being trying to be as honest as possible, but as regular as possible, but trying to apply good analytical methods by applying good design principles and good visualization design principles that will not guarantee 100 percent that the data your chart will correspond exactly to the truth of the matter. But it will be more likely than not that you will be closer to that end of the spectrum. So what? In the book, I explained that my notion of truth is we thought with a lowercase t, not without it.
Lea Pica: I like the lowercase T. Yes because it's I think that, you know, as a culture we have grown into this sort of good versus bad and right versus wrong. It's binary thinking that. Oh, I love that. Yes, it's binary thinking rather than seeing a vast spectrum of colors and gray shades. You know, in between and saying, you know, like. And also the confidence that we come with this. I think that we're also, you know, in our field, you know, my listeners are presenting data and findings to people in a corporate setting and or people at a conference setting. And there's an expectation, I think, sometimes from the audience to say, I want the facts. I want the truth. And we are attempting to uphold that and deliver a confidence that says this is definitely what happened, rather than feeling into the idea that there is a degree of uncertainty.
Lea Pica: And for me, the risk the ethics is communicating that degree of uncertainty, making sure,
Alberto Cairo: I completely agree, completely agree that that also happens in the world where I come from, the world of journalism, the world of graphic design, this kind of binary thinking. It's very it's also very simple. Things are either true or they are not. Right. Right. And, you know, I think that this is part of like the long. I would say any occasional effort that we all need to make with ourselves and also with other people to basically make people understand that no data is 100 percent certain. There is always a crowd of uncertainty around it. Not only that, but show me the facts or give me the facts should imply always that what we really mean is give me your best interpretation, right?
Lea Pica: Yes.
Alberto Cairo: Your most confident interpretation of those facts. And if you're most confident interpretation of those facts is not 100 percent certain. And it never is. The ethical thing to do is to say, well, I feel very confident that this is the explanation. But it is another possible determinative explanation that I am less confident about, but that we should. Explore further. Right. That's the ethical thing to do.
Lea Pica: I love that. I love the idea of offering another potential possibility. This is something that I'm teaching in conscious communication is saying I don't have the concrete answer. There's always another potential story or meaning or possibility here. And I think the really cognizant analyst store data presenter is going to be forthcoming with that.
Alberto Cairo: Absolutely. It is only we should add a qualifier, though, as saying that it is still appropriate to say I am very confident. Yeah, both explanation. So it's like it's not absolute black relativism that I'm waiting for. Not all explanations are equally good. Right. This is something that I also address in the truthful are some explanations are better than others, depending on how well supported they are by the facts. But your interpretation by how rehears your methods are on someone and so forth. You can safely say that one interpretation is more confident than the other. But if you are alone, you know you're a little bit on the fence. You know, 60 percent confidence, 70 percent confidence that I think that it is safer to also disclose the other 20 percent of your possible interpretations. Right. But it is true, though, that people who are used to presenting data in business environments, sometimes they are requested by management to give them just one answer right here.
Alberto Cairo: And I am a great believer in pushing back against that. And again, this is an educational effort. And again, I'm basing this on my own experience as a journalist, wherein newsrooms we also get that kind of request. Give me the explanation and say, well, I can use the explanation. I can give you an explanation.
Alberto Cairo: I am more confident about this analysis of these a story. But we do need to disclose this possible alternative explanation that also has some, you know, some facts to back it up.
Lea Pica: One hundred percent. It's kind of pushing back and saying the only observable fact is there is a glass in front of me that contains water. But we always take the extra step and say that the truth is that the glass is half full or the glass is half empty.
Alberto Cairo: Correct.
Lea Pica: And finding that distinction, I think is key in helping stakeholders who are consuming data recognize those distinctions because we are so quick to overlay our interpretation. So I love what you're saying. It's almost like I'm going to give you my best interpretation using my expertise and my best interpretation.
Alberto Cairo: But I acknowledge that I'm a human being. And yes, I'm going to have I may fail. I may be incorrect. And these are the ways that I may be incorrect.
Lea Pica: We're not Siri or Alexa. And we might have missed our coffee this morning. Yes. So you open the book referencing a somewhat infamous election map that is hanging in the halls of the White House. I love it.
Alberto Cairo: Yeah. So I'm going to interrupt you. Sorry.
Lea Pica: Oh, yeah. Yeah.
Alberto Cairo: At least it hanged on the walls of the White House after President Trump. Two coffees. I don't know if it is still there.
Lea Pica: Exactly. Oh, thank you. Yes. Interrupt me any time to correct my interpretation. No, of course. And you speak to how maps, in particular, are among the most misused chart type. What do you think is going wrong with map charts?
Alberto Cairo: Well, I mean, based on that particular example, just to describe it with a little bit more detail, I refer to the county-level map of the results. So the 2016 election, if you take a look at that map, it looks like an ocean of red with a few spots of blue here and there. Right. In it is being used right now to argue that basically President Trump and the Republican Party won the 2016 presidential election in a landslide because indirectly, that's what that map suggests. Race. Tom's of right. 80 percent of right.
Alberto Cairo: Actually, that's the surface that is covered in red on that map versus 20 percent of blue. But obviously, that map was not designed to the purpose of that map is not to give you an idea of how many people voted for Dre.
Alberto Cairo: And it is it's a map of territory and territory doesn't vote. The challenge is that obviously that you need to take population density into account. And, you know, the fact that most of the map is red is due to the fact that the Republican vote tends to concentrate mostly in rural and sparsely populated areas on Democratic vote, tends to concentrate in big cities. That's the reason why I think we have just a few blue splotches here on there. Right.
Alberto Cairo: So Trump won the election, obviously, but he won. Losing, losing the popular. Vote. Right. So if the purpose of your argumentation or conversation that you're having is to talk about who won and by how much and by how many votes and such and such, that map doesn't really support that kind of conversation. Because that map was the sign with a completely different purpose to show who won where. Not to show how many votes each one of the candidates got. And maps can be misinterpreted in many different ways. This is just one of them not taking into account possible denominators, for example. Right. That may change the interpretation of the map completely. And this is why the whole reason why you see the book's attention and be careful, right? Don't mean fail vigilant.
Alberto Cairo: Be vigilant. Raised like don't infer from the map what you want to see in the map, because then what you are doing is on the map projecting your own beliefs.
Alberto Cairo: Also the map and therefore you will misinterpret it. And this happens to everybody, by the way, that's happened only on the right-hand side of the political spectrum. You happen to me. Happens to everybody that we need to become a little bit more attentive, a little bit more mindful and a little bit more careful. I'm not assuming that we can interpret the charter, a graph of that or a map. Intuitively, in the blink of an eye, that is an excellent point.
Lea Pica: And I think that you're speaking to something that I don't know that a lot of people are aware of what's happening all the time, which is a form of cognitive bias, which is called confirmation bias, where your own pre-established beliefs and preferences are allowing you to very quickly extract what it is you're seeking to find from a visualization. So one political camp is going to love that chart because at first glance it communicates something that they want to hear or see vs. what other you know, you had provided some other maps and I'm always forgetting the name of this chart type, but it's where you're showing a spatial representation of the United States, but you're showing each area and these hexagonal shapes.
Alberto Cairo: A Cartogram.
Lea Pica: Thank you. Yes. Which is often talked about as a more accurate way of looking at map level data, because just the inherent size and shape of countries and states they're using, ah, you know, precognitive attributes of, you know, color taking up a certain amount of space and jumping out at us? They're there like speaking to different languages almost in a way making our interpretation of regular maps kind of inherently flawed. Yeah. Yeah.
Alberto Cairo: I in the book, I talk a little bit about cartograms and I make the point that a cartogram alone can also be a little bit misleading just because it is difficult to connect the weird shape of the cartogram to the real shape right of the aim of the United States or any other country. Therefore, it is always better to pair them up with an actual map so people can compare the actual map to the card program is when we pair these two match together, we put them together that are under our understanding over that phenomena in concrete. The results of the election may improve a little bit. Yes.
Lea Pica: Yes. So I love actually what you said in the beginning about this book that this is a great book for the general public to take on. Because, you know, I actually put out a question on Twitter this morning asking your fans, what could they ask you about ethical data visualization and a number of the questions centered around the education of the consumer. You know how you have to be trained to some degree to properly consume this information. So your book for me really took this to the next level and looking at how the data looking at the data we're seeing in our daily lives, especially through the lens of the media, which is an interesting filter for us to receive our information. Right. So, you know, I would love to talk a little bit about a recent event that was, you know, quite a wildfire on social media, otherwise affectionately known as Sharpie Gate and the cone of uncertainty surrounding Hurricane Dorian. So, you know, I would love for you to give us a little perspective on what do you think just happened there?
Alberto Cairo: Sure. Absolutely. I, too. First of all, referring to the first point that you made in your question. Oh, absolutely. I mean, How Charts Lie. The title itself is a little bit deceptive because it's not a book about how Charts Lie is about how not to be lied to or lied to by the charge that we see every day in. Yeah, because even if I charge is perfectly designed again. Take it, take it. Just remember the county-level results. That map is perfectly fine. There is nothing wrong with it. It's supposed to work as it is. Supposed to work is only that we project on it or onto it. What do we want to believe already? And therefore, we misinterpret it. So as you said, in any act of communication, there are some. There is a designer in this case who created this realization. But there is also a receiver, a reader or a viewer in it happens sometimes. And this is something that I describe more and I mentioned it in the book, but I describe it in much more detail in recent talks. When we create of equalization, we have a particular mental model or a particular schema of how these politicians should work or what we want to communicate with that of this conversation. And we create a visualization based on that mental schema and then we pull it out and then a reader or a viewer takes a look at the graphic and the viewer comes to the graphic with their own mental schema, which may be different than the mental scheme of the designers. When there is a match between the mental schema or the mental model or the mental map that the designer had and the mental of modal that the reader has in mind.
Alberto Cairo: Then understanding happens. But sometimes there is some mismatch. Perhaps the reader doesn't have that and knows how to read that particular kind of charge, or because the reader has a different kind of education and so on. And that is when misunderstandings happen. And now we get to Sharpie Gate. So what happened in Sharpie Gate? Yes, to summarize. So I mean, I know a lot about this story because I experienced first hand. I live in Miami, Florida. We prepare for Dorian and we pay a lot of attention to maps by the National Hurricane Center anyway. So initially, Hurricane Dorian was predicted was forecasted around August 30 to twenty-nine to thirty the thirty-first to make landfall in Florida. The entirety of Florida was covered by the cone of uncertainty. Let me make an aside the condo. It's something that I also talk about in How Charts Lie. Many people interpret the cone of uncertainty as an area under a danger. And that is not what the great uncertainty. The cone of uncertainty is basically is ours is an absolute representation of multiple possible positions. So the center of the storm in the following five days. So it is our representation, not a threat. It is a representation of the uncertainty of the forecast. Basically, scientists are telling you the center of the hurricane could go anywhere. It could be anywhere within these cone and even outside of it. In some cases, right. Because it's not a 100 percent confidence is only 7 percent confidence. Anyway, those are the gory details that I go over a delay describing a lot of detail in the book anyway, anyway.
Alberto Cairo: So always the thirty first. The cone. The forecast for Dorian was predicted, predicting that Dorian will make landfall in Florida. And previous forecasts. All right. Forecasts from August the 30th or something like that. Had the cone, the outer boundary of the cone, touching the southeastern part of Alabama, barely. That's in the south and tip of Alabama anyway. So on September the 1st, one day after all these forecasts, President Trump woke up and sent a tweet saying the following states are going to be higher hit harder than expected or harder than forecast by Doria. Those states were Florida, South Carolina, Georgia, and Alabama. And I was completely surprised because. Why Alabama? Because you took a look at the forecast for September the 1st. That very same morning, the National Hurricane Center had issued an updated forecast. And that forecast pushes the possible path of Dorian towards the east. Right. Right. It was predicted to go over the ocean. We will still down here in Florida. We will still we were still predicted to receive some of the effects of Dorian, but it was not predicted to make a direct landfall in Florida. And certainly, it was not predicted to go over Alabama at all. Right. Right. Onset or the first. So President Obama got called out by – oh sorry – President Trump, got called out by and by many people and including the Birmingham, Alabama, National Hurricane Center office.
Alberto Cairo: They sent a tweet saying Alabama is not going to be affected by the storm just because many people have started calling them for a by the by President Trump's tweet. Right. So these people were responding and they tweeted out just to calm people down. Dorian is not going to come to Alabama. Don't worry. We are not going to have any effect. So Florida. But. So if the story had ended up in here, it will be a non-story. Right. President Trump could have just issued a correction if this can happen to anybody, right. Anybody can misread those maps, forecast maps. They are very easy to misread. They are complex to read. Right. They need to be explained in order to be understood correctly. He could have just, you know, issued a correction saying, hey, you know, I wrote Alabama. I was wrong. I apologize to Alabamians for that. These are the states that are going to be impacted by this storm, Florida, Georgia, South Carolina and perhaps North Carolina because, at that point, the storm was predicted to go over the outer banks in North Carolina. But he didn't correct that which I think that is it would be it would have been the right thing to do and this story would have ended there. I made a mistake. I'm sorry. I apologize. And then you move on. But it's enough. Do that.
Alberto Cairo: He doubled down, triple downed, sextuple downed by tweeting multiple times that earlier forecast. He was I predicted that Alabama wasn't going to be was going to be impacted. But if you take a look at those maps that he tweeted out and you know how to read those maps, well, the probability of Alabama being impacted by any effects of the storm had always been around 10 percent. And you, as always, have very low probability. So he's tweed was not 100 percent wrong. He was 90 percent. But it was 100 percent wrong that morning because of the latest forecast for he sent his tweet right there.
Alberto Cairo: The latest forecast was made by was published by the NHC around 5:00 a.m. And Trump sent his first tweet at 9 a.m., four hours later. So he was 100 percent wrong. If the tweet had been sent one day before then the tweet had would have been around 80 or 90 percent wrong. But on September the 1st, when you send a tweet, he was wrong already. He should have issued a correction.
Alberto Cairo: So one of the things that he did afterward, if you remember that he did an event in the like, I believe, a public briefing, the Oval Office, in which he showed an earlier cone of uncertainty very conveniently. I think that it was the forecast for August 30th. So a very early forecast. And the cone at that point was touching south, a sorry Georgia and Florida. And he extended the cone, mainly drawing on the top cone with a Sharpie. That's right. That's dangerous. It's funny. First of all, you do it. We all laugh about it. But it's a serious issue because course money leads and unofficial forecasts. And by doing that, I think that the dangerous part of this story is that he is undermining the credibility of agencies that lives depend on. That's the key to this story, I think the most dangerous part. So he goes way beyond the map.
Lea Pica: This is so fascinating because I love what you just said, that originally what he had tweeted was this percent wrong, but based on simply when his wrongness changed. Yeah, again.
Alberto Cairo: Well, things are never right or wrong. They are just fluid.
Lea Pica: Yeah, it is. I think Mark Manson said in his first book, The Subtle Art of Not Giving a Bleep, that we are all wrong and it's only about trying to be less wrong over time. And even that's fluid. And you know, this is really fascinating because, you know, these are tools and you can give a child a Ginsu knife and you might see some carnage or some consequences of that. And I think, you know, something you pointed out in the book, first of all, for anyone that is remotely not sure how to make heads or tails of these cone of uncertainty charts that the media are using. Your book is an excellent breakdown step by step of exactly what that chart means, and you even go as far as to show, you know, different versions of that chart. And I love your disclaimers on every chart which says warning this is not totally accurate or a warning. This is better, but still not the best. I wish more charts came with large disclaimers like that because it was your breakdown that way and showing kind of these puffs of clouds representing the possible locations of the storm and the possible areas that may be affected.
Alberto Cairo: May I interrupt you just one second?
Lea Pica: Go ahead.
Alberto Cairo: Because there is a coda to Sharpie Gate that I describe in an article that I'm writing right now. It may be out already by the time that you publish these podcasts, but I'm writing an article on the end of the stories that one of the maps that that Trump tweeted out during those days actually has a caption A at the bottom of the map. And the caption is like, it's such a perfect coda, such a perfect flow into all these these stories because the caption of that map is one of those spaghetti maps that show, yes, tons of possibilities at the center of the storm. And the caption of the map says, If anything on these graphics causes confusion, ignores the entire book.
Lea Pica: I laughed when I saw that.
Alberto Cairo: That's a great book title. Actually, someone recommended that I should use it as the title for my next book, After Woodchucks Lion. I'm seriously considering it because he's so perfect.
Lea Pica: Yeah. If this confuses you, ignore at all costs.
Alberto Cairo: Correct?
Lea Pica: I love them. And you know, even how you broke down, like when it got to the end of the day, you're seeing the potential range of possibilities with this cone. Even then you're saying most people don't know that. It's not like a ninety-five percent probability that this is the area. It's at best 67 percent.
Lea Pica: So there is an additional, you know, 33 percent.
Alberto Cairo: Yeah, the other center of this storm. I mean, as you said, a describe this in a lot of detail. The book itself is 67, 70 percent confidence level that the center of this storm will be inside of the cone. Therefore, that means that there is a one out of three probability that the center of this could be outside of the cone. But then on top of that, you need to remember that the cone only shows the position of the center of the storm and hurricanes are enormous. Therefore, if you want to assess as to whether you may be endangered, you need to mentally overlay the size of this storm over the cone. Yeah. Then prepare appallingly.
Lea Pica: It came so clear. And that's why I think this is it's so amazing that your book is coming out at this time because of this example and your closing thought on the cone of uncertainty. Was that the issue isn't inherently misleading, is that it's not a chart meant for public consumption. And yes, yet it's being thrown out everywhere. So for me, the question becomes where does the responsibility lay for breaking a cycle of misinterpretation? Is it the authors of the chart, the journalists, the audience? Where do you see that?
Alberto Cairo: It's it's everybody every day. Everybody. So I believe that I mentioned the book itself. And I certainly mention eating recent public talks that I'm doing around the book in which they show a diagram of communication that I tried to explain before.
Alberto Cairo: In one of the answers to your question is I do we have the designer, we have the readers, and then in-between, we may have the mediators. Right. And the mediators who should be in charge of explaining to the public what it is that they are seeing.
Alberto Cairo: The mediators are people like me, journalists. Right. Journalists and graphic designers who take the products from official sources such as the National Hurricane Center and many others. And we tell the public, you know, this graphic that you see over here may not be showing what you think that it shows. Let me explain to you how to read it correctly and what kinds of inferences you should make out of it.
Alberto Cairo: So it's like a triangle, right? The diagram is actually not a bi-directional path. So it's sort of like a multi-part, like a triangle shape or something like that. So there is a responsibility on the part of the scientist. That is true. And I mentioned this in the book. I think that, for example, on these just conjecture that some of the products that the No. The National Weather Service, the National Hurricane Center are putting out could perhaps be used to help change it.
Alberto Cairo: Some tweaks here and there in terms of color pallets to gain clarity. Right. I do believe that some design changes could be made. But these scientists are already doing it. They are already aware of how the public misinterprets these things. And they are working hard in making those products easier to understand. There is also the resource responsibility on the part of the public. And this is something. Emphasizing this, the whole reason I wrote the book is like, don't assume again that you can interpret a charge by taking a quick look at it. If you want to discuss that charge, you have the responsibility of reading it carefully and paying attention to it. That's your responsibility as a reader. And I emphasize these because in recent discussions about technology and data management from high tech companies and so on and so forth, and you know what? The media is misleading us and fake news, etcetera.
Alberto Cairo: The responsibility is usually put on the end meter, on the communicator, forgetting that we people, human beings, you know, we have free will. Right. We have.
Alberto Cairo: And we are responsible also to create, you know, a in good information or environments when we have the responsibility to be a Beatle, a little bit more attentive towards what we see. That's our responsibility. And we have the responsibility to educate ourselves a little bit more in order to participate in conversations in the political sphere. And then finally, finally, the responsibility to the mediator. One of the main reasons why the public cannot understand these maps is that what many journalists do is to take the cone of uncertainty. They are repurposing. They make it look more beautiful.
Alberto Cairo: They make it colorful, 3-D, etc., etc. And they just throw it up and sometimes they explain it wrong.
Alberto Cairo: You can see it all the time in newscasts and TV media, etc. The cone and other products created by the NHC, they are not correctly explained by news media. So journalists should also make an effort in explaining things a little bit more clearly, I think.
Lea Pica: Well, I think to that point, the question is, do they want to be coyer?
Alberto Cairo: I don't know whether you want to, but I believe that should do that.
Lea Pica: Responsible journalism would do that. Yeah, absolutely. Now, I hear you. And, you know, it's also understanding how many filters this information is coming down to us by the time we even see it and our attachment to a certain truth that we are biased towards. You know what I think? You know, President Trump exemplified was a deep attachment to that truth that he had communicated. For whatever reason. And for me, my goal as a data communicator and consumer is to detach more and more from any truth that I think I know and be open to all of those possibilities. And you're right. Getting empowered with tools to take deeper examination of the charts and graphs that we're seeing and maybe even block out the first interpretation that we're receiving, coupled with that chart, you know, like I almost want to cover any headline I see around data and a chart and just see the chart for what it is and see what kind of conclusion I come to on my own. Mm-hmm.
Alberto Cairo: Correct. I mean, that I think that that's the story. I didn't grab you again. No, no, no. Go ahead. Oh, okay.
Alberto Cairo: Yeah, you're absolutely correct. Well, first of all, in terms of why we believe or what we believe. So it is not enough to have an opinion about anything.
Alberto Cairo: You can't you cannot just say, I see these chart and I believe these and I believe it because I want to believe it. I believe any belief should be supported by a reason for that belief.
Alberto Cairo: And that reason needs to be done we need to be able to explain those reasons to hold that belief to other people so other people can understand why you have that belief ride.
You need to be able to reason the process that led you to have to have that belief. Right. That's the first thing you're going to just say. You know, I think that this is true and that's it.
Alberto Cairo: I think that it is true because I think that's a circular argument, as I said. So I guess I say so.
Alberto Cairo: Because basically the essence of Sharpie gate, right. You need to have a reason or good reasons, actually, that can be accepted or understood by the other by other people. So that's the core that's a core part of that chart. And that's what I wanted to say about that.
Lea Pica: No, I 100 percent agree. And you know, one of the questions we got on Twitter this morning for you, I think speaks to the heart of this. Someone asked, do you think the U.S. educational system is doing enough to teach young one's visualization, literacy? And then a second question. I felt tied right in, for that matter, students of business management or journalism or whoever is responsible for communicating data. Does it start there? Do you envision a world where in addition to trigonometry and microbiology, we're getting data visualization, literacy classes?
Alberto Cairo: Sure, why not? Although older teaching, visualization, literacy alone will lead us nowhere.
Alberto Cairo: I think its part of is it should be part, I believe, of a wider, you know, educational overhaul in which, you know, we couldn't. Food, anything that is related to obviously critical thinking and reasoning and an argument and rhetoric, but also ethics. I think that ethics should be formally taught. It's like how can you think ethically about your own beliefs and your attitudes? And visualization can certainly be part of that program. Yes. Because even in places where this kind of a recent thing is being taught, usually graphics are not taken seriously. Again, it's all about, you know, how to read an argument, for example, and understand that argument correctly. And charts are not treated as arguments. They are true. Do visuals write something secondary and again, intuitive and easy to read? And no.
Alberto Cairo: Again, we need to assume that charters are also arguments and we can decode them under. We can criticize them and we can be skeptical about them. We can listen to them and with them. And these skills can be taught now how to make this change. I have no idea. I mean, because I am you know, I am a university professor. I don't know, working in public schools in elementary, middle and high schools.
Alberto Cairo: And what I do know, though, is that public school teachers are really underpaid and overworked. So they should not be burdened them with the responsibility of not only getting more work. I think that this should be a concerted effort from, you know, the government and the public sphere professionals who work in all the areas that are related to thinking about numbers, thinking about evidence, thinking about science to help public school teachers to develop these programs. Right. We need to help a little bit with that. And that's one of the reasons why I wrote this book. There is a reason why, for example, someone called me out the other day, by the way, in one of the earlier reviews, someone was saying, well, you know, I really like this book is a lot of fun.
Alberto Cairo: But some of the examples seem a little bit off place or something. For example, there is a there is an example of heavy metal. And I did understand why there why the author is talking about heavy metal at all in the relationship to these Mabie went on for literally too long. Well, there's a whole reason why I have an example of a map showing the concentration or the density of heavy metal bands all over Europe. I wrote it because that's an example that can be repurposed by school teachers to be more serious about it or recent about maps. Instead of showing a map of heavy metal bands shows a map of hip hop bands. Right. Right. And I would relate to that example. Right. If you use examples with high schoolers, 14-year-old and 15-year-olds related to topics such as life expectancy or poverty or whatever.
Alberto Cairo: Those are very important issues, economic indicators. But they don't relate to their lives.
Alberto Cairo: But if you do turn out to reason about data based on the things that you can really relate to, TV shows, music et cetera and so on and so forth, they will and they will grasp the concept more easily. So I see the book also as a template perhaps that school teachers can borrow from and steal my examples shamelessly to say if you're a high school teacher yet you're here, feel free to just steal my examples and use them in class purpose, then completely write this like a template to read. That's the reason why I included these sort of like quirky, funny, slightly off examples that can not be taken seriously by a statistician, for example. But by a data scientist. Right. But they will be taken seriously by a high school student.
Lea Pica: I absolutely agree with you, and even for my listeners who sometimes don't know how to make their data relatable. And I encourage them to use analogies that they feel would their audience would be able to relate to. You know, some of the most incredible tableau visualizations. I think the themes center around sports and movies and the sports ones I appreciate from a professional standpoint because they are spectacularly done and I couldn't care less. I can't relate to it at all. But when it comes to this on movies or TV shows, I really get it right away. And it speaks to me. I understand it so much more easily.
Alberto Cairo: It relates to you. It matters to you. So once you can make I describe this phenomenon a little bit more in the previous book and the truth for art, which is again, more for designers. I call these the me factor than me factor. Once you introduce, you know, this validation is something that emotionally connects to the lives of the readers who are reading your graphic. Geographic will become more effective just because the people will see themselves in the graphic, right?
Lea Pica: Oh, I love that. Yeah, well.
Alberto Cairo: Let people see themselves. The data, the example that I use sometimes in classes is that year I want to show people, let's say income distribution or wealth distribution. The United States. That's a curve that is very skewed right in front of people on the lower end and much fewer people on a very long tail going all the way to billions of dollars. Right. You show me that histogram of wealth distribution. Then I say that's interesting. Per se. Right. Right. But before you show me, you show me that chart. You ask me, what does your wealth or how much money do you have in the bank or something? I knew you asked me to input that amount of money. Then you can introduce a line in the histogram showing you are here to make more money than 70 percent of people. You have less margin than 30 percent of people. That's the me factor. You're conveying the exact same information as you did before, the same amount of data, but you're putting the reader at the center of the visualization and that will make the visualization more engaging. So this is what I tried to do with these family examples. Oh, so our charts lies like try to come if you want to teach these principles.
Alberto Cairo: And I hope that the book will be useful for educators. Teach these principles on how to become better. Readers of charts tried to come up with examples that connect directly to the interests of your audience, rather than showing generic examples about unemployment rates or life expectancies, which again if they are very important issues, but they may not connect directly with the interests of your audience.
Lea Pica: That is fantastic. That's just a really excellent point. And gosh, I feel like we could talk.
Lea Pica: I want to shift gears, but I feel like that's a whole exploration of its own relatability. Yes. So when you're thinking about the kinds of charts that you should be wary of as a consumer, what are the cone of uncertainty is certainly one that you call out. Are there other charts being used in the public that you would advise people to have, like really take some caution around interpreting?
Alberto Cairo: Well, the short answer is all of them charge it because it's sharp and can mislead you, view them, pay attention to it.
Alberto Cairo: However, there are specific charts that are more prone to misinterpretations in general. There is a rally. There is an association between the complexity of a chart and the ambiguity of that chart. Right sidebar charts, for example. They are usually easier to interpret than scatter plots, for example, just because they tend to be just univariate right or to vary it at the most. But you know, I scatterplot particularly if you change the size of the bubbles in the char or change the color of the bubbles for it. According to a categorical variable they can encode for variables or even more sometimes. Right. So within more multivariate char e's the more open you may be to ambiguity or to misinterpretations. I have plenty of examples of the scatter plots in the book that I believe that is that may be prone to be misinterpreted, for example. Again, the old mantra correlation is causation, although we need to go way beyond that. I think that many people have already internalized that that mantra, although we don't apply it, we have internalized it and we need to go a little bit beyond that. And I do that in the book and talk about, for example, the ecological fallacy. Yes. Amalgamation paradoxes such as Simpson's paradox and many other issues that I think should become part of general knowledge.
Alberto Cairo: It's not just for a special, it's not just for data scientists. Again, an ecological fallacy. If you take a look at data that is aggregated, say, at the national level, you should not be fair. You know, you should not extract inferences at the individual level. Right. And the example that I put is a chart showing cigarette consumption per capita. It's a scatterplot cigarette consumption per capita per country on the x-axis. Life expectancy on the Y-axis. And you will see if you plot countries, you will see that there is a positive association, more Seagirt. It's a country consumes per person. The higher the life x rays it goes. And I can assure you and I can tell you this because I have made this mistake myself. Actually, I would say that I have made all the mistakes that I call out in the book. But this specific one is something that really pains me to have made this mistake, is to describe that chart as saying the more we smoke, the longer we leave. When the chart is actually showing that, the chart is showing that there is a positive association between cigarette consumption and life expectancy and vice versa, it's that association that is positive at the national level may completely reverse and it does reverse at the individual level.
Alberto Cairo: That's an example of a paradox. Those are also called an amalgamation paradox. An aggregation paradox. Why should a reverse? Well, because many other variables are you are not contemplating. Right. That explains the increase in cigarette consumption and the increase in life expectancy. For example, wealth. The wealthier country ease, the better the health care of the country will be, probably on average and the safer that country is and all that increases life expectancy. Obviously, people will also have more money to buy more cigarettes and they will buy cigarettes. They will smoke more. But the life expectancy that they are losing because cigarette consumption is more prevalent gets balanced out. It gets offset by the fact that they get longer lives because they have better health care and their depression. Yeah. And nutrition. There are many other factors that we are not contemplating. So amalgamation paradoxes seem a paradox. The ecological fallacy. These are all things that I think that we could explain to anybody. Anybody can grasp why this happens. If you use the right example, I have a couple of a couple of examples in the book.
Alberto Cairo: Those are Time Serious charts, right? When you showed co-occurrence of two different phenomena and you tried to imply the coincidence in time between two different phenomena and you tried to imply that there is a causal connection between the two.
Alberto Cairo: There are also examples of that in the book that is also very, very it's very easy to jump to that conclusion intuitively, particularly if you already want to believe there is a rational connection between those phenomena in the day that you show in your time serious charge.
Lea Pica: There's an incredible explanation and I loved that you cited Tyler Vigen of spurious correlations in your book because I use him as an example to show what the potential pitfalls are of dual access charts when they're on different scales, which that is an extremely commonly used chart in my field. I used to use them constantly. And, you know, his is a hilarious depiction of where that can be so misleading because. Where you place your axis is can be a subjective choice, so, you know, I think that those examples are crucial to help people really understand what's at play. And I loved one of your quotes, which was don't read too much into a chart. Particularly if you're reading what you'd like to read.
Alberto Cairo: Yeah. Yeah. That's again, that's confirmation bias, right? Yeah. Actually, the confirmation bias is connected to another psychological phenomenon called motivated reasoning.
Alberto Cairo: We all like to reason ourselves into what we already believe. We all like to strengthen our own beliefs. We are all we all do that. You should not we should not believe that we are smarter than the other guy. Right. Next, we all do that. However, I am also an optimist. I do believe that we can become more mindful of how prone we all are to project what we want to believe until whatever we see. And then we can try to curb our own impulses a little bit. We can not be 100 percent successful. We are not robots. We are not computers. We are human beings. But at least we can reduce the number of times that these may happen.
Lea Pica: Yes. And what did you call it again? Motivation.
Alberto Cairo: Motivated reasoning. There is I recommend several books at the end of how it should slide because of how CHUD sized as the entry point to all these issues raised like provides a very wide introduction to all these issues. But then at the end of How Charts Lie, I provide a reading list of books that that help me understand all these matters and great, I like their followup readings and my favorite one that deals with all these biases and cognitive problems, etc. used by psychologist called Carol Tavris with a V. Tavris and her book title is a slightly old book, right but it's a very good one. These title mistakes were made, but not by me.
Lea Pica: Very clever.
Alberto Cairo: Everybody makes mistakes except me. Right. Right.
Alberto Cairo: But there is also the classic thinking Fast and Slow by Daniel Kahneman. But by thinking fashion slows a little bit drier. Carol's book is fun to read out of and extremely informative.
Lea Pica: Yeah, I love that. Thanks for those recommendations. And I'll definitely look for that. And I think that as long as we're going through this process continually asking, what am I missing? So I have a four-step methodology that I teach for having people create data stories. And the C part of the methodology is context. And it's continually asking, what could I be missing here? What is something that my painting as complete a picture as I possibly can? And that's what I loved about your book, especially with the cigarette consumption scenario, were you just kept probing deeper and then segmenting using kind of them not sparklines, but. Of course, I'm forgetting this small multiples division in the picture of those scatter plots. And of course, the story really starts coming alive when you start to a segment. And I think that it's just a fantastic read for that process. And you know, before I dive into the last question, I want to know what gets you excited about the future of data visualization?
Alberto Cairo: Tons of different things. Well, first of all, I mean, let's say on the higher end of the spectrum of complexity and, you know, expertise, etc., the new the new technologies that are being developed greatest like expert experiments that I'm seen with, for example, our mentor reality and virtual reality to create virtual spaces through which people can explore information and integrate themselves with the information that they are exploring. I am a little bit skeptical about how useful a virtual reality will be for data visualization per se, but I am super excited about how powerful it can be for pictorial visualization. For example, I have seen simulations of, for example, 3D, a high-resolution 3D models of a human heart of a real person that you can mapping through space and you could you use your 3D virtual reality glasses to get inside that person's heart and explore it and see whether there is any offshore abstraction in it or structurally not in a vein or artery or whatever.
Alberto Cairo: Right. I see a lot of possibilities in this kind of sub-technologies, innovations in data visualization, per se, right. New charts being created.
Alberto Cairo: So expanding the language of data visualization by new types of a grasp of maps, new ways of conveying information. That's how the higher end of sophistic Sherman expert être. But I'm much more excited about the other end, which is that. Which is what I tried to do with How Charts Lie, which is to basically bring everybody else up to speed with how exciting all these technologies are, right. It just basically promote the idea that anybody, everybody can benefit from learning a little bit of data visualization. And today all the tools are. I mean, most tools are available out there. Many of them are free. Yeah, many of them are open source or just a matter of, you know, reading a couple of intros to the field, a couple of books that introduce you to the or Colombo's or whatever they talk about, you know, certain principles. They should we should apply and then start experimenting. There are plenty of free tools out there. People can experiment with it. Right. One of them that I use constantly in classes is a tool that was developed by the Department of Statistics at the University of Auckland in New Zealand, which is the Department of Statistics that created the R programming language.
Alberto Cairo: Well, besides R, people in that department also design a beautiful open source, super easy to use tool for data analysis and data visualization? Call INZight with a Z INZight.
Lea Pica: Inzight. Oh, okay.
Alberto Cairo: It's a wonderful little to basically it is basically a shiny application. Those of you who use ours. Know what I'm talking about. But it's a graphical user interface to are what he does in the background is to create our code. But you don't see the code. All you see is a drag and drop whizzy way winter like super easy to upload a data set on, start exploring it visually and why they decided. They decided because they wanted to create a tool that was useful to teach statistics to high school students. That is simple to use. That is easy to use, and that makes a statistics visual. Because once you start making numbers, visual and physical numbers become more understandable and they also become more approachable. So these are the things that get me excited at bringing everybody else up to speed with all these technologies when they're with the wonders that all these technologies can open our eyes to.
Lea Pica: That's fantastic. You know, I love the excitement around a deeper fluency. Visualization, and I definitely can't wait to check out that tool and play around because I could definitely stand to play with more opensource do the whole thing, is that.
Alberto Cairo: Yeah. InZight. Another wonderful thing about it, besides being a free and open-source, he said he has two versions. One of them you can download and install on your computer if you want to have it installed. But the other one, which is the one that I use, didn't need to sell anything. It works on your browser web-based. It's great, but I need local. That means that the data that you're putting there is not sent to any server. It stays in your browser. It stays on your computer, but it works on the browser. It's a browser-based. Yeah. Excellent. Super. I mean, it has its limitations. So obviously you should not try to upload 1 terabyte of data to crash. But first, my data sets. Right. Which is what we usually use to teach beginners in beginning classes like, you know, 2000 rows of data or something like that. It works beautifully.
Lea Pica: Oh, that's fantastic. And I'll make sure to link to these tools. And all of the books that you've mentioned are going to be on the show notes page. And I also I am also excited in some ways around augmented reality. I remember testing out either TripAdvisor or Yelp, it might have been Yelp. And I was walking on the street and I said, hi. I really need to eat something right now. But I don't know how good any of these places are. And I saw like an AR button of some kind and I tapped it. And as I moved and panned my phone around, it overlaid reviews for those restaurants and data, all that. So around those restaurants. And I was like, this is amazing.
Alberto Cairo: Yeah, well, that's a wonder of technology, right? Yeah.
Lea Pica: So we've come to our final question. So think very hard here. Imagine this very plausible scenario you are playing as Settlers of Catan and looking to unify two settlements when suddenly it pulls you in through a vortex. Back to the moment you are about to present the first piece of data or first insight you're ever going to present. What would today you say to yesterday you said to us. No, about presenting data.
Alberto Cairo: Related to Settlers of Catan.
Lea Pica: I mean, it could be if that's what you were going to present.
Alberto Cairo: If I needed to go back in time and explain to myself what I mean or tell myself any advice about presenting data to my previous I myself to my previous I.
Alberto Cairo: 20 years ago. Right. I don't know. I guess I think that it will be. The fact that explained before, right, that visualization or a chart is not just a picture, it's not just it's not just an illustration, right. It's a depiction of evidence. It's a way for people to understand information better, including yourself, including including the creator of the. Of the chart. That would be.
Alberto Cairo: I think that the best advice that I could give myself 20 years ago. Because, again, I was trained as a journalist and as a graphic designer, not as a statistician or as a data scientist and a hard. I had to learn all the other skills that are needed to the side of these Alsatians the hard way. But making by making mistakes, by being called out about these mistakes and correcting myself and trying to try to do better. And I also will tell myself, play more board games that play more board games, particularly strategic board games. I laugh at strategic board games and not only Settlers of Catan, but also, I don't know, more advance a strategic historical simulation. It's a wonderful game by GMT. A company called A Here I Stand, which is a historical simulation of the religious wars over after Sir Protestantism. Each player runs a country and you need to run the economy and the politics and the religious issues. And so it's a lot of fun. But what those games teach you to do, I think, is not that they are fun. They are fun, but they force you to think critically, strategically in an organized manner, taking into account tons of data that you need to analyze in your head in order to forecast what may happen in the future and then prepare for different scenarios. Plan A, plan B, plan C. So they are great educational tools, I think. And some of them may be used to teach things such as probability. For example, Rice like Settlers of Catan, which is said based on a dice rolling in several actions that you need to take during the game. You can sort of try to calculate what the odds are that you will get the right result in the dice or not, and you can cut and you can compute those. Right. And I think that it's great to teach children probability theory at this 6, et cetera, through these kinds of devices, through games. It will be another device.
Lea Pica: This is fantastic. I've recently gotten my son, my 7-year-old son, hooked on Chinese checkers. And these games are an edge for me because I tend I grew up on very linear thinking games like adventure computer games that had one path and one end. So for me, the idea that there isn't necessarily a specific destination and you are in it thinking non-linear early, that is an amazing growth mechanism, I think because as children we're taught there's one rate, there's one answer to everything.
Alberto Cairo: And so that is the black and white, the black problem that we talked before. Right, the binary thinking. So I don't think either or binary thinking. And these console games, as you say, there is actually a term in the board game world that we use to refer to these games that may provide tons of different paths to winning or losing. We call them sandbox games. It's a sandbox. You can basically create whatever you want with the game. There's another old classic called Civilization. Yep. Which is not related to the computer game, by the way. It's a completely separate game. If they have any. Yeah. So is by a company called Avalon Heal from many years ago, but it can still be bought. Second as a second-hand copy suddenly on Amazon and it's just a pure sandbox game. Each person begins with our civilization and it is your responsibility to grow that civilization, to acquire new technologies, to improve the well-being of your citizens, to defend yourself. If you're attacked or go to war, you need resources. Although war is not recommended by the game, you can win the game and win it. More often than not, you will win the game if you don't enter any war. We just lost our great message.
Alberto Cairo: Don't go to war. War is very costly. They are very expensive.
Lea Pica: So the path to victory is peace.
Alberto Cairo: The path to victory is peace and enlightenment. Just build more and acquire more technologies. There, it's a great game.
Lea Pica: This is fantastic, especially parenting advice. You're welcome. Everyone listening. This is really just so amazing. I'm definitely gonna go back and start to think about like engaging with some of these games to think differently. So my question is, does Fortnite count as some of that non-linear? Or is it just is the message of strategy missed?
Alberto Cairo: I have. I mean, I have not played Fortnite seriously. My older. So I cannot speak about it. What I would say, though, is that I have played both board games and the computer versions of some of these board games, and the experience is not the same. Right. This social element of board games is what makes these games interesting because then you will not just deal with the uncertainty that can be computed, which is basically what you may get in a dice roll. That's why when you can't compute and that's what a computer may provide, the experience that a computer may provide is basically you fight a game, so you play games. The artificial intelligence of your computer, which is basically just computation, is pure mathematics. But when you work and play with other people, you also have to deal with more ambiguous uncertainty with the uncertainty that can not be computed. Right. And you need to learn how to deal with that and way gaming to your forecast, coming to your predictions that non-computable, no, you know, not math, non-mathematical level of uncertainty is also extremely relevant to make decisions in real life. And again, it's not mathematical, but you need to take it into account. So there is a difference in the experience. I will recommend people to play for perhaps fewer video games and or play more video games that have the social component. I'd like you to collaborate with video games or whatever, but I still tend to believe that the physicality also of the board game creates a completely different experience around the game itself through the participation of four-fold, the old friends right around the table.
Lea Pica: Of course, in it. And there's also an energy of the brainwaves being exchanged as you're in people's presence. I mean, that's a measurable effect in some cases. And that that variable of, you know, you don't know what this person is going to do is a huge factor. Well, Alberto, I have to say, this one is one of the richest and most comprehensive conversations I've had around data and ethics and certainty and uncertainty. But unfortunately, our time has run out. So tell the listeners where they can keep up with you.
Alberto Cairo: Sure. Well, first is very easy to find me online. So a simple Google search is my last name is. It is quite unique. So it's Alberto Cairo, Alberto with a know. And then Cairo is like the city in Egypt. Although I'm not from Egypt, I'm from Spain. My website is albertocairo.com. That's my professional website. I also have a personal blog that I try to update as much as possible. The web blog is thefunctionalart.com. The functional art is my first book actually in the market publishing 2012. And the blog is still thefunctionalart.com. And then on Twitter, I am on Twitter @ Alberto Cairo, also on Facebook. I'm on LinkedIn. I am active everywhere. So it's very, very easy to find me.
Lea Pica: And I really appreciate that you are so interactive and responsive on social media. You're not in some ivory tower watching the conversation from afar. And I appreciate that so much.
Alberto Cairo: I appreciate that. I appreciate that you say that. Really thankful for that.
Alberto Cairo: I tried to be just because I am a great believer in the fact that knowledge doesn't reside on individual brains. It resides on networks. And again, you are not smarter than the other guy or the other girl or that the other person or the other woman or the other man or whoever that other person is.
Alberto Cairo: In the interaction between like-minded people who have honest conversations and informed conversations about these challenges and charts and graphs and data on whatever that understanding may happen. So that's why I tried to reply to people who write to me nicely. I mean, your message to me, I will probably not reply. But if you do start to engage in a nice conversation about a particular saying, I will probably reply as soon as I can. Yeah. So I said for people to do that.
Lea Pica: So by interacting with the individual, you're actually helping to level the collective high of consciousness.
Alberto Cairo: And I will be or being open to being wrong about something. The right to correct yourself sometimes. Yeah. That social part of the conversation.
Lea Pica: Yes. That's a true growth area to own responsibility for a mistake. I love that. So Alberto's book is available for pre-order now and it goes officially on sale on October 15th. And the link to that will be on the show notes page as well as all of the Web sites, all the places to contact him. All of the resources. And I have to tell you, I truly love this book. I was up late nights reading it and it will be an invaluable resource in your library. So I just want to thank you so much for taking this time. Time to be on the show and speak to my listeners, it's an honor. And I hope our paths crossed again.
Alberto Cairo: It was my honor. I really appreciate these conversations. I enjoyed them very much. I must have said before, I'm a great believer in network conversations, chatting with people and meeting other people on the. I'm glad that you like the book social media.
Lea Pica: Excellent. Well, thank you again and take care. Thank you.
Lea Pica: And I have to say, after reading Alberto's book, I envision anyone who creates, communicates or consumes data that this will be a vital primer for them to having educated conversations with yourself and others about the world of numbers that we live in. So to catch all of the links and resources mentioned in this episode, please visit the show notes page at leapica.com/049. I would love if you could leave me a comment or suggestion or even a question for Alberto because I want to hear about the challenges you face when presenting the information. If you like what you've heard, please hop on over to iTunes to subscribe, leave a rating and review because not only are they quite appreciated, but they will help this valuable information get into the hands of other practitioners just like you. And I'll leave you with today's presentation, inspiration by none other than Mr. Alberto Cairo. And you've already heard this today, but I feel these quotes bear repeating. And that is don't read too much into a chart. Particularly if you're reading what you like to read. A chart shows only what it shows and nothing else. My take, We must begin to detach from the dogma of trying to prove who is right and who is wrong with data. Because those two extremes simply do not exist. Use a discerning, well-trained eye when creating and consuming charts. One that not only looks at what is present, but what looks for what is not. And ask yourself what are my own experiences, preferences, beliefs, and biases? How are they influencing the conclusions I'm making right now?
Lea Pica: Only then do I feel that we can gain the confidence to answer our original question by saying this is our best guess of how not wrong we are. That's it for today again. Hop on over to leapica.com/bootcamp to get all signed up for the training you wish you had when you started crunching data and you won't find anywhere else.
Stay warm. Namaste and Namago.
