Cautionary Tales | Stats + Short Stories Episode 178

Harford is an economist, journalist and broadcaster. He is author of "How To Make the World Add Up", "Messy", and the million-selling "The Undercover Economist". Tim is a senior columnist at the Financial Times, and the presenter of Radio 4's "More or Less", the iTunes-topping series "Fifty Things That Made the Modern Economy", and the new podcast "Cautionary Tales". Tim has spoken at TED, PopTech and the Sydney Opera House. He is an associate member of Nuffield College, Oxford and an honorary fellow of the Royal Statistical Society. Tim was made an OBE for services to improving economic understanding in the New Year honors of 2019. His newest book “The Data Detective” was released in the U.S. and Canada earlier this month. 

+Full Transcript

John Bailer: Everyone has a podcast nowadays. Whether it's about sports, politics or features some of the most fascinating discussions on the current state of statistical communication in the world. No matter the topic, it seems like someone, somewhere is talking into a microphone about it. Getting someone to act on your podcast however - that's a lot more rare. Tim Harford is our guest and the host of the new podcast “Cautionary Tales.” as well as the author of the books “Messy,” “The Undercover Economist,” and “The Data Detective: Ten Easy Rules to Make Sense of Statistics” – released this month in the United States and Canada. Tim is a senior columnist at the Financial Times, and the presenter of Radio 4’s “More or Less,” the series “Fifty Things That Made the Modern Economy,” and the podcast He’s an associate member of Nuffield College, Oxford and an honorary fellow of the Royal Statistical Society. Tim was made an O-B-E for services toward improving economic understanding in the New Year honors of 20-19. Tim it's great to have you here. We're excited to talk to you about, you know, your new podcast I kind of accident the world and Cautionary Tales, and particularly about a hero of mine and of many and that's Florence Nightingale, and you wrote about her recently in the pandemic so welcome Tim.

Harford: Thank you very much, Nightingale, of course, will be no stranger to fans of Stats and Stories oh you had a wonderful episode with Alison Headley with a Victorian data visualization expert. Not so long ago. But what I have. What I am able to contribute to the ongoing love affair of statisticians with Florence Nightingale is that in Cautionary Tales I can reveal that we have managed to persuade Helena Bonham Carter. A list as a Lister to play. Florence Nightingale herself. I've written a cautionary tale about Florence Nightingale about her experiments with data visualization and Cautionary Tales, they're true stories and then we have real actors who come and play these parts. And when my producer said we're going to ask Helena Bonham Carter to do it I thought well yeah good luck with that. Just a little podcast. But I was absolutely delighted that she agreed to do it. I mean she's actually not, not the only amazing actor that we have. But I realized there, there may be a reason why she was interested because I realized that she is in fact distantly related to Florence Nightingale Florence Nightingale was part of the Bonham Carter family, and she's campaigned to raise awareness of nightingales work and so maybe, maybe that was what hooked me in I wasn't aware of the connection but I've heard snippets of her performance, and it is amazing, absolutely amazing.

Bailer: You raised the bar for our producer, by the way. He's probably quaking in his boots now about what the expectations are for this show.

Harford: Well you know it's always good if you're trying to raise awareness of an issue if you can just have an incredibly famous person related to the person that's even better. It does no harm at all. But, I mean, as I think Stats and Stories this will know Nightingale was a remarkable figure in the history of statistics, and a lot of different stories told about her they're not they're not all true. But the one that I really wanted to explore was at her experiments with data visualization specifically I think a lot of people will know she, she went out to serve as a nurse to lead a contingent of nurses in the Crimean War in the mid 19th century to a very difficult experience there, she became a sort of patron saint of Britain she was the most famous woman in the British Empire except for Queen Victoria herself and she was. As you may know, the first female Fellow of the Royal statistical society. But what particularly interested me was, she was going toe to toe with the British Medical and military establishment, and she was very explicit, she said well when I am outraged or when I'm enraged I revenge myself with a new diagram that quote. That is amazing. And she talked about, she was going to frame her diagrams, have them framed and hung on the wall at the Ministry of war and the, the barracks and the various areas where the people she wanted to influence would see them and she was going to send copies to Queen Victoria and Prince Albert he was going to send copies to ambassadors, she was going to send copies to the press, and to the Houses of Parliament. It was very very conscious of the power of a good chart to communicate a story, and that is what I'm trying to explore in this particular Cautionary Tales and also the downside, so what are the risks when that's when you, that's what you're trying to do when you start trying to persuade people. What are you what are you willing to cut corners for? Are you willing to skew the dice in your favor. Did she do that or did she not and these are the things I'm trying to explore.

Rosemary Pennington: What is your approach to storytelling and Cautionary Tales, so for the, for someone who has not listened, what are they, what are they going to, if they're not listening to Florence Nightingale, what are they normally going to encounter, they listen to this.

Harford: Well one of the other Cautionary Tales is about Martin Luther King and his most famous speech contrasted with a gentleman called Gerald rapper, very famous in the UK, who was a jewelry entrepreneur who gave a speech which blew up his empire and cost hundreds of millions of dollars and asking, What did Martin Luther King do successfully that Gerald Ratner failed to do or vice versa and, and there's a twist. There's a twist in the story. There's another one called the Dunning Kruger hijack, which is a hijacking of an aeroplane by the stupidest terrorists imaginable. And, like, why are some people so very far outside their sphere of competence.

Richard Campbell: And each I think he's looking right at me

Harford: It's just a guilty conscience Richard. In each case, what I'm doing is I'm trying to tell these true stories. I go to the historical record or the speeches, the biographies, the histories. I'm writing scripts for actors. Most of the scripts are, again, based on the things that we know they said but sometimes you know I would be based on a close paraphrase or something or media report. But the idea is to tell this true story. And it's always a story of something going wrong. Sometimes it's very funny sometimes it's absolutely tragic. Every now and then there's a happy ending. Because you've got to keep people guessing. But in each case, I wanted to bring social science to the story. So I want to explain to people about the Dunning Kruger effect, for example, or about the neuroscientific research into improvisation that tells us about Martin Luther King speeches, or in the case of Florence Nightingale What do we know about data visualization, and when it persuades or fails to persuade.

Bailer: Well, I'm afraid that's all the time we have for this episode of Stetson short stories, Tim thank you so much for being here.

Harford: My pleasure. Thank you.

Bailer: Stats and Stories is a partnership between Miami University’s Departments of Statistics, and Media, Journalism and Film, and the American Statistical Association. You can follow us on Twitter, Apple podcasts, or other places you can find podcasts. If you’d like to share your thoughts on the program send your email to statsandstories@miamioh.edu or check us out at statsandstories.net, and be sure to listen for future editions of Stats and Stories, where we discuss the statistics behind the stories and the stories behind the statistics.

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The Last Legs of Local Journalism | Stats + Stories Episode 166

Penelope Muse Abernathy is a former executive at The Wall Street Journal and The New York Times, is the Knight Chair in Journalism and Digital Media Economics at the University of North Carolina. A journalism professional with more than 30 years of experience as a reporter, editor and senior media business executive, she specializes in preserving quality journalism by helping news organizations succeed economically in the digital environment.  Her research focuses on the implications of the digital revolution for news organizations, the information needs of communities and the emergence of news deserts in the United States.

She is author of “News Deserts and Ghost Newspapers: Will Local News Survive?” — a major 2020 report that documents the state of local journalism, what is as stake for our democracy, and the possibility of reviving the local news landscape, and she is the lead co-author of “The Strategic Digital Media Entrepreneur” (Wiley Blackwell: 2018), which explores in-depth the emerging business models of successful media enterprises.

Episode Description

Cities and small towns across America once woke up to their local newspaper on their doorstep. Over the last several decades, though, those newspapers have begun to disappear a University of North Carolina at Chapel Hill study showing that disappearance has heralded the rise of news deserts in the United States. That’s the focus of this episode of Stats and Stories

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Rosemary Pennington: Cities and small towns across America once woke up to their local newspaper on their doorstep. Over the last several decades though, those newspapers have begun to disappear. A University of North Carolina at Chapel Hill study showing that disappearance has heralded the rise of news deserts in the United States. That's the focus of this episode of Stats and Stories where we explore the statistics behind the stories and the stories behind the statistics. I'm Rosemary Pennington. Stats and Stories is a production of Miami University's departments of Statistics and Media, Journalism and Film, as well as the America Statistical Association. Joining me are regular panelists John Bailer, chair of Miami statistics department and Richard Campbell, former chair of media journalism and Film. Our guest today is Penelope Abernathy. Abernathy is the night share in journalism and digital media economics at the Huntsman School of Journalism and Media at the University of North Carolina at Chapel Hill. She's also a former executive at the Wall Street Journal and the New York Times. Abernathy's research focuses on the implications of the digital revolution for news organizations, the information needs of communities, and the emergence of news deserts in the US. Penny, thank you so much for being here today.

Penny Abernathy: Well, thank you for devoting time to discuss this very important and interesting topic, I think.

Pennington: I'm just going to ask you to describe how you define a news desert in your work.

Abernathy: Well, my own definition has evolved over the last 5 to 10 years. I think my initial definition was based on a lot of research that have been done starting at UNC in the 1970s around the importance of local newspapers, good local newspapers, and actually setting the agenda for debate of public policy issues that were important to us as residents of a community and would affect the quality of our lives as well as the lives of future generations. So, I initially defined it as a community without a newspaper. And that's important because newspapers have been vitally important to us in this vast country of ours, of kind of establishing not only our connection to the larger democracy, but also in terms of building a sense of community from all of that. So, but how it has evolved since then is I've tended to look at it as we've had the rise of digital alternatives, as I would look at alternative media like ethnic media public broadcasting. What I now say today is a news desert is a community that lacks the readily available, easily accessible access to some sort of the critical news and information we need to make important decisions.

Richard Campbell: Maybe could you talk about some of the numbers this is the stats and stories show just some of the losses over the last few years that you've documented in the just-so magnificent study on news deserts and ghost newspapers you've been doing now for some time? Could you talk a little bit about that?

Abernathy: Well, there are two ways to look at loss. One is loss of newspapers and one is loss of journalists, and so let me talk about the loss of newspapers first, which is where we started right? Between over the past 15 years- between the end of 2004 to the end of 2019, we basically lost a fourth of all the newspapers in this country. That's 2100 newspapers, and of those 2100 most were in small and mid-sized communities. So, one way to look at it is you lose a newspaper, you lose the person who's going to show up and cover what might be a- look to be a routine school board meeting or a routine County Commissioner meeting that turns out to have on the agenda something with huge implications for you. So, you lose the reporter that shows up to do that. When you lose the reporter, we have lost over the past 10 years, we have lost more than half of the newspaper reporters in this country. That's 36,000 reporters. So, and disproportionately we've lost reporters there at the state and regional level. So, one way to think about it is when you lose a reporter at the state and regional level cover for a newspaper, is you're losing the reporter that covers an important beat that binds and region and a state together, like education, like health, like environment. So, you know what we've lost I think in all of this is the ability to understand how we're related to our next-door neighbors. Or how we are related to people in another part of the region or the state that share the same problems and issues we do, but because we don't have that kind of unifying look that says this is important to me locally, this is important to me regionally and in a Statewide matter. We don't have the ability to know what the important issue or do much less set the agenda for how we need to solve these problems.

John Bailer: So, Richard thanks for asking the stats question, man. I mean I do wrong that's like that's taking the arrow out of my quiver. So, Penny let me follow-up and just ask you to explore some of the drivers of this phenomenon. What are some of the things that have happened that have changed and that have kind of pushed the newspapers out of business? Abernathy: Well and let me just say there is there's certain things that have happened that have been unforeseen, right? So, one of the things that we do look back in the year 2000 everybody was It was kind of fashionable to say newspapers are going to be dinosaurs right? We are entering the digital age, the information is going to flourish, your next door neighbor can put out is over his or her own podcast, newspaper, whatever you want to call it from any of that. And what that assumed I think is that there would be the development of a business model that would- a digital business model. So, if we look back 200 years, the for-profit business model that had sustained between nine thousand and eleven thousand newspapers at the turn of the century basically collapsed, and that business model was built around getting eighty to ninety percent of the revenue that supported news rooms from advertising, so that collapse there was an assumption of digital model would develop and in kind of the most stark terms, what we can say is the digital business model has not yet evolved. All right, because in part of the issue is there are between 75 and 80 percent of the revenue- even in small markets in terms of digital dollars- goes to the two Tech Giants Google and Facebook, which do not really create local news. So, we've had kind of a vacuum occur in terms of news being created at the local level. And if you think about it, if they're taking a seventy-five percent of the revenue digital revenue out of there that leaves television stations, newspapers, digital outlets to kind of fight over the other scraps. There's just not a business model now that has replaced the Pratt model.

Campbell: So, one of the phenomena I think that's going on here that I think is interesting with the loss of local journalism- we've just had this election a lot of people sort of don't understand Donald Trump's popularity and I think of being part of it and I think workings institution has done some work on this. I think you have. With the loss of local journalism, the default. For news has become for many particularly conservative people in rural and small-town areas has become talk radio, which is on all afternoon as mostly conservative, and in the evening Fox News. And I've heard George Packer talk about this from The New Yorker that he's talked to some local editors in small towns across this country and he said one of the changes that they see is that people aren't sending in letters and talking medical issues anymore. They're talking about often Fox talking points or conservative radio talking points. Could you talk about have you seen that? And you know, I to me if you really under want to understand the tremendous popularity of Donald Trump, this is- I think this is one area to look at that people just aren't following local news anymore because it's not there.

Abernathy: Well and let me just say I think it's a little more complicated than just saying its conservative talk radio with conservative television. I think the other half of the equation is the algorithm driving propensity of social media and the internet. So, what gets said on the conservative channels gets amplified in social media, and what also happens is that because of the loss of journalists, because of the laws of local newspapers, you have to turn to social media to find out something, right? I mean it’s just not as being covered locally. You don't have a way to know it. So, a classic example is the pandemic most recently, right? I mean you Derek Joseph it was not being covered locally in my local newspaper. I live about 80 miles outside of the Chapel Hill bubble. And so, I get was almost impossible to find any statistics on anything. Right and so even most recently my husband said to me that the local hospital had run out of beds. This was about a month and a half ago in our hometown and Marburg and I said, how do you know? And he said the funeral director posted something on his Facebook page. Now in a way in a way that's probably a more reliable source than you would normally get because of funeral director probably is in touch with what's happening there. He may even be on the board of directors at the local hospital. I'm not sure, but that's how desperate you are to get information. You just don't have somebody covering something is vital to the health of your life, to say nothing of the quality of your life on the local level.

Pennington: You’re listening to Stats and Stories and our Guest today is the University of North Carolina at Chapel Hill's Penney Abernathy one of the things that I was reading about recently Penny was the rise in some of these communities where there's a dearth of local news of these newspapers that look like news, but are you know pushing an agenda and I wonder if you have started looking at some of that in your work on news deserts and how that sort of is influencing how you were thinking about this issue?

Abernathy: In fact, I think there are a couple of ways to look at what is happening. If you look back over the last decade what we can say is that we had the emergence of a new type of media baron, especially in local news. With the private equity and the hedge funds who just kind of rushed and swooped in after the recession, bought newspapers at just rock bottom prices, and manage them these same way they manage a widget factory, which is and go in and you cut cost and try to get it to profitability and you either then harvest it, sell it, or just shut it down if you can't do it, right? So, if you Look at the decade between 2010 and 2020 you see the rise of these huge conglomerates, right, that own as many as 600 newspapers. That's a grill disconnect with the community that the newspapers supposed to be serving, right? So, as they've also been responsible for shuddering an enormous number of newspapers or merging them together. My fear is we don't know yet know who's going to own the next decade? Right and what we have seen is a huge proliferation over the last year or two of what some people call Pink Slime a right is these partisan base news outlets and some of them are being very deliberately targeted at what are news deserts, right? So, and part of the problem with these partisans’ sides, is that there's no transparency tool right? I've looked at a number of them. You don't know who's funding them. You don't know what the agenda is. You don't even know whether the reporters reporting on him or even local or may be based in another country or another part of the country and supposedly writing up press releases or whatever. There's also a pay-to-play element to their so my concern is if you look at it- just from the company needs the information needs of a community, we're in danger of actually bringing partisanship down to the very local level. And I mean, I think most people don't vote on somebody for the local school board based on whether they're Republican or Democrat. They're voting based on what they want to do in this local school system and what they think the priorities are there.

Bailer: I'd like to just quickly follow up on what one of the things you just said, which was the information needs of the community. Yeah, you've already talked about the idea about- essentially this trend from look there's this trend from local to National this trend from broad coverage these Regional interests. It also from a broad perspective to more customized algorithmically determined focus. But what are the information needs for the community that are not being served? This by these trends?

Abernathy: Well, what I harken back to is what the FCC could produce in 2012 and they basically brought a group of social science scholars together and said identify what I need as an ordinary resident ordinary community to know about so that I can make wise decisions about the quality of my life. The quality of my life, of my children and the quality of future generations. And they identified eight topics of the dscc has them on their website. I use them when we're judging whether a news site is actually news site. It includes things like do they cover education, environment, health, governance, infrastructure, economic development, politics and public safety. I think I got all eight there. That's a first for me. You know, we have used that when we assessed a whole range of things for instance in the 2020 report, we looked at Facebook share with us 300 some odd pieces of local news that ended up on their local news feed and what we concluded is when an algorithm is choosing your news and there's a dearth of local news, what tends to happen is the majority of the news that you give fruits Facebook's local news feed is either related to crime and not even is kind of a wackadoodle crime type thing, and it's also related to human interest types of things. Right? So, you know, we look very specifically at North Carolina, and among the things that we found in North Carolina is when a hurricane was coming through, the warning for the hurricane actually appeared two days after the hurricane was already out to sea. Now if you think about it that’s because it took that long to get enough shares for it to rise to the level that the algorithm picked it up as being an important local news.

Pennington: This makes me think of the argument that we sometimes make when people are arguing over what journalism should be and what we should prioritize, because you know, a lot of the conversations in a lot of our classrooms is, you know, you wanted to give a mix you want to sort of form your community about things that maybe they think they don't care about what that impact them. But also give them a little bit of the of the human interest, and it feels like the argument that we've been making in classrooms that if you just let people choose all they're going to choose as a human interest stuff, which might be might be interesting for them but might not sort of have the same importance to their lives. It sounds like in this examination of Facebook, you've found some evidence of that.

Abernathy: Right, right.

Bailer: So, give us some hope here.

Abernathy: Let me just say here I think that there’s several silver linings in this thunderstorm that is overhead I think that whatever you can say about the pandemic I think that it has helped raise awareness among the [] of how important it is to have local information. And have the facts and the data and the stats right there so that you can make wise decisions just by what you do that day and we saw digital subscriptions from that. now what’s been discouraging to me is that roughly 50% of people in a poll wide survey by Pew Research Center found – said that they were not getting the relevant local news they needed. But 75% said they weren’t aware that there was any financial difficulty, right? So, there's a huge disconnect. Now what I have seen over the last two years is the industry has awakened to the problem that they need to they're going to need help other than just kind of generating it, and we have for the first time, I think, in congress with bipartisan support for a number of policies a that leaves giving short-term support to news organizations going forward on the other, hand short of policy, I also have studied business models- sustainable business models, and I have concluded that if you are in a community with average to above average population and economic-growth prospects and have a publisher/owner founder of a digital site that is truly connected to the community and understands the community's needs and expectations. Then you have at least an average chance of creating a very Diversified for-profit nonprofit or of hybrid model, where I continue to be most concerned is in economically struggling communities that have lost the news organizations and disproportionately that's where we've lost newspapers, because of the collapse of the for-profit model.

Campbell: Penny can you- one of my frustrations that on is the national news media is not covering this as a national problem. It's localized coverage and this I think is partly a problem of Journalism doesn't do a very good job of covering journalists, and I think you mentioned that before, but this reminds me a little bit of the Catholic abuse case where it took 20 or 25 years before realized this wasn't a local problem and Community. This was a national and international problem, and it wasn't until the spotlight team at the Boston Globe made this a national problem. Do you have any hope that the national media will focus on this more and not just sort of treat this as oh here's what's going on in this is community not on an isolated story but one that we all need to be a pay attention to because frankly some of the national newspapers and organizations are doing very well financially, and local systems aren’t. And like we're trying to raise money for our own foundation here to support local news in Southwestern, Ohio. You can't compete. You can't get money because it's all going to National organizations right now, and it is a good time to raise money for journalism, but not at the local level. Abernathy: No, I think you raise a good point. I've actually been impressed that over the last year and a half the number of national and international news organizations that have actually approached me and have done major pieces. I mean, I've actually been quite impressed with the documentaries that have been done on German public television on Japanese public television. They see the US as kind of the canary in the coal mine. So, I think there is in many ways kind of it is come back in on National ones from the international organizations that have actually picked up on this survey that we did on the US. So I think in that case we are beginning to see that, but I do think one of the things that I think we need we overlook a lot of times is when I came here every was in 2008 to become the night chair. Everybody was very concerned about whether there was going to be a business model for the New York Times right? So we tend to look at our media as top-down, when in fact, there's a lot of investments and research that shows that as much as 85% of the news that feeds our democracy comes in through local newspapers, and one good way to think about that is look at the Ahmaud Arbery case right, which was actually first covered by the Brunswick paper in Georgia, but it took the Atlanta Constitution Journal and the New York Times to amplify that into a national story. So, when we lose that kind of on the ground reporting and the connections from the on the ground local reporters to the state and regional reporters who then amplified up to the national level, what we're losing is all of that news at the state and regional level of below.

Bailer: So, I'm curious a little bit about the packaging of news. You know, if you look now, you know from 2000 to 2020 that's another generation news consumers whose experience with news tends to be snippets in the context of social media, not to be a caricature of this but just it's a different model. So, what are what are the things that have- that might need to happen to engage, kind of all, generations in this in this new experience of regional news?

Abernathy: Well, I think there are several experiments that are out there. One is that I think what we've learned is that we form habits early in life and that we tend to take those habits through- with us through life. So for example, I was working at the Times when they first set up nytimes.com, was involved with that. Was that the Wall Street Journal as they were doing wsj.com, but it took me until 2013 to actually start reading the digital editions of the New York Times and The Wall Street Journal routinely. And it took me another year and a half before I was comfortable enough to say this has more value to me now because I've changed my habits. Yeah. I can't remember the last time I picked up a print newspaper, right? But it took- I think about how long it took me someone in the industry to basically change my habits. So I think that part of it is we haven't looked at loyal news consumers in ways that we need to get- transition them and we haven't done enough to figure out how we transition millennials and gen-z-ers into a different form, so, I've been really impressed with some of the electronic newsletters that have come out and think that they may be a business model going forward, or an introductory offer into something larger, right? But I mean we can't know right now what you get is going to be like 10 years from now. So, we need to not only be caring about the people who are still with us the use consumers that are still with us, but also about building that next generation and building the need to be informed and making people understand this is important to our democracy to our society and most importantly to you and the quality of life that you have. Pennington: Well, Penny, that's all the time we have for this episode of Stats and Stories. Thank you so much for being here.

Abernathy: Oh, thank you for having me.

Pennington: Stats and Stories is a partnership between Miami University’s Departments of Statistics and Media, Journalism and Film, and the American Statistical Association. You can follow us on Twitter, Apple Podcasts, or other places where you can find podcasts. If you’d like to share your thoughts on the program send your emails to statsandstories@miamioh.edu or check us out at statsandstories.net and be sure to listen for future editions of Stats and Stories, where we explore the statistics behind the stories and the stories behind the statistics.

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Statisticians React to the News | Stats + Stories Episode 155

Check out the “Statisticians React to the News” blog here

Ashley Steel is a statistician and quantitative ecologist with experience in academia, government and international organizations. She wrote “The Truth About Science: A Curriculum for Developing Young Scientists” which guides middle school students through the process of conducting research.  She also designed and taught a course on statistical thinking at the University of Washington, Seattle, where she is affiliate faculty.  Passionate about the value of probabilistic thinking in every-day decision making, she volunteers at science fairs and supports teachers in understanding statistics.  

Megan Higgs is a statistician, freelancer, and owner of Critical Inference. She has experience in academic research and teaching, as well as consulting and scientific collaboration in many disciplines. She believes in the importance of raising awareness about limitations of current uses of statistical methods and inference in scientific practice and communication.

Episode Description

There's a lot of statistical information shared every day in news stories. Everything from COVID cases to economic data is Quantified help us better understand our world. But do news presentations really help us do that? And what do statisticians think about the way journalists are covering their work, that’s the topic behind this episode of Stats and Stories with guest Megan Higgs and Ashley Steel.

+Full Transcript

Rosemary Pennington: There’s a lot of statistical information shared every day in news stories. Everything from COVID cases to economic data, to athletic performances quantified to help us better understand our world, but do news presentations really help us do that? and what do statisticians think about the way news media presents statistical data? That’s the focus of this episode of Stats and Stories where we explore the statistics behind the stories and the stories behind the statistics. I’m Rosemary Pennington. Stats and Stories is a production of Miami University’s Departments of Statistics and Media Journalism and Film, as well as the American Statistical Association. Joining me are regular panelists John Bailer, Chair of Miami Statistics Department and Richard Campbell, former Chair of Media, Journalism and Film. We have two guests joining us today to talk about a new blog sponsored by the International Statistical Institute; Statisticians React to the News. Megan Higgs is a statistician, freelancer and owner of the blog Critical Inference. She believes in the importance of considering the philosophical and general practice issues involved in using statistical inference to inform science management and policy. E. Ashley Steel is a statistician and quantitative ecologist and considers herself a quote statistician who is passionate about statistical communication, end quote. Higgs and Steel are also the minds behind the statisticians React to the News blog. Thank you so much for being here. That was a lot of statistics and statisticians one introduction to this program. So, but we’re so happy to have you here today.

Steel: Thank you very much. We’re happy to be here.

Higgs: Yeah thank you so much, Rosemary.

Pennington: Uh, I just want to get this conversation started and ask you how you decided that you wanted to create this blog? Sort of what was its genesis.

Steel: So, I think we all read the newspaper and so much of how we understand the world is based on observations and they’re often structured into data. And that’s what statisticians do. We think about data and what it means, and I think in many ways journalism and statistics have a lot in common right? We help people understand what’s happening in the world in different ways and when I was reading the news about, you know, it was about COVID-19 and there were all these data flooding us constantly and I was spending a lot of time applying the statistical skills that I have from my professional life in understanding the news. And I started to wish also that I had other kind of information like what would a virologist say? What would an economist say? And I came to understand that our professional lens, our expertise, really helps us understand one aspect or one facet of the news, and it seemed like a great opportunity to share that; to share how statistical training and experience helps us better understand the news and the data around us. And I want to also credit Peter Guttorp , another statistician who was working simultaneously on an editorial piece and we felt- we just got to discussing how important it would be for us to better communicate how statistics can better help people understand the news.

Bailer: Are their particular skills that-statistical skills that you think are critical for consuming the news and reading the news?

Steel: I think statistical thinking skills- of course, if I’m talking about statistics, I’m going to get around statistical thinking that came really quickly. So, nobody needs to really do a lot of calculations when they’re reading the news. And I think people confuse statistics with calculations. So I don’t read the news with a calculator by my side but I read the news with this understanding of how does how many observations we saw influence how much we should believe the conclusions, but I read it with an idea of what are different kinds of probabilistic events and if we’re just looking at the last the last events in the chain what do we now about the earlier events in the chain. I read the news with all these kinds of probabilistic thinking skills and I think it helps me. There are pieces that are missing from other disciplines, but these skills help me.

higgs: Yeah I can chime in there. I feel like from my perspective there’s a maybe an understanding of the complexity that’s going on behind the scenes in terms of how the data are collected, any analysis that went on and then the interpretations based on the results that can’t really be summarized simply and neatly and briefly in a news article. And so, I feel like that’s something as statisticians reading it we have this nifty understanding. So I feel like that was one of my excitement or motivations to be involved is to kind of help get that perspective out there and not that we can explain all the complexity but just to raise or spark the curiosity about what else is there that can’t be covered in the news article but the blog could provide the perspective and knowledge that there is more there.

Campbell: Well I would like to ask a question for whatever reason you allowed John to and this is what I want to know because you start out with telling this story of our governor who has taken three tests that we know about one positive, two negative, and Ashley and Megan probably don’t know this but our governor went to Miami, most of his kids went to Miami, and I want to know if the DeWine family shows up in Oxford do I have to avoid them?

Bailer: Well, they probably would avoid you Richard. That’s a different question. I guess it’s just a- that’s one of Ashley’s posts about the conditionality of arguments, I think her recent post talks about that. well that’s, so Richard noticed that Megan and Ashley allowed me to contribute to this as well and as part of the promotion of this within ISI’s purview of the public voice and that’s one of the things that Peter Gunthorpe has been a proponent of as well. But Richard, in terms of the question, this was one of the things that the blog involved which was reacting to the news. And it was pretty amazing to see these stories on consecutive days where a positive result was followed immediately by a couple of negative results and there were different tests and there were different ideas that were going on. What does that mean in terms of statistics and I guess we could have just as easily called it deconstructing the news from a statistician’s perspective, but it was reacting to that. so, in terms of answering your question I don’t- if they are travelling here you probably don’t have to worry about it.

Campbell: Okay that’s all I need to know.

Pennington: I do have a kind of related question. So there are several posts on the blog obviously given the moment we are living in that are about COVID because it’s sort of the unavoidable topic of a lot of statistical things right now because the stats for the pandemic are everywhere and trying to understand them is sort of a mission of all of us I think. But you know six months from now maybe if this pandemic has sort of, you know, obviously not gone away but is less than some what other kinds of things would you like to see? are there other ways of thinking about statistics that you’d like to see people reading about on the blog? Or do you have things in the pipe you’d like to talk about?

Steel: Yeah sure we expected there to be a COVID-19 theme to with obviously because of the times we are in, but I also feel like COVID posts that have- sorry let’s stop that for a second. So the COVID-19 related posts that are already published have some underlying messages that can really be applied to other stories as well but coming up shortly we have we’ll be talking a little bit more about official statistics, so there is world statistics day, so we’ll be covering more about that, we’ll also be covering more about election statistics I believe, when we get closer to November and the US Presidential election. Hopefully for other countries as well. One of the really important things is having this international perspective that comes from, you know that’s an expectation because of sponsorship of the International Statistical Institute but also a really exciting part in something that really drew me to wanting to be involved as well, so we have had posts so far from Brazil, Italy and Sweden, and have commitments from contributors from Palestine, New Zealand, Puerto Rico, Philippines, Thailand, Nigeria, Switzerland. So, we really are trying to get out. So, what news items will come up hopefully will be country dependent, as well, which is fun and a new perspective.

Bailer: So one aspect that you discuss about this blog is having topics that might be thought of as timely, as reactive to a particular news story, and others that might be evergreen is sometimes said n this business, that would be themes that continue on. Can you talk a little bit about that distinction between the timeliness and that mix and what are some of those kinds of evergreen topics that you could envision covering here?

Higgs: That’s a great question. So, we originally thought of these neat categories of these timely versus evergreen, or a specific story versus a general reaction which is how we’ve categorized them on the blog. You know as things go, nothing is turning out to go as neatly as categorized, so most of them have a particular story that they are referencing and talk about but then they go on to provide general takeaways and lessons so we learn. So, it’s been really hard for me to actually categorize those. But I hope that for some of the evergreen topics that we’d hoped to have more kind of general statistical thinking type ideas like Ashley mentioned in the beginning, some really underlying things to spark curiosity in people about how they’re reading news has statistics or statistical information related in it but I think it’s great to hone in to have that one reference in articles as well.

Higgs: I think if the blog evolves and there are more and more stories we’ll start to see links between stories and that will naturally lend itself to more and more topics that are more general and I think that will be exciting because it will demonstrate to readers how one statistical idea can help us understand climate change, racial inequity, immigration patterns and sports. It’s one foundational idea and I’m excited about that.

Campbell: I wanted to ask about- and if Megan wants to follow up but I’ll forget my questions if I don’t ask them right now. So, it’s a two-part question, one, do you know if journalists are reading the blog yet? And two, what can they do better? What can journalists do better?

Megan: Do you want to go first Ashley?

Campbell: Go ahead.

Ashley: Okay well I don’t know; you and Rosemary might be the first journalists to read the blog.

Campbell: That’s sad.

Higgs: I hope that’s not the case but you’re the first- you’re the only two that I know of for sure who have read it. But yeah, part of what we’re doing is trying to promote to a wider audience, maybe Ashley can talk more about audience later, but I’ll answer my version of your question about what I think journalists can do better. I think that my kind of main frustration lies within what happens in journalism regarding reporting of statistical information is the simplification that must happen. You have to tell a story in a very limited number of words and so what journalists and scientists as well tend to do is oversimplify and lose the complexity and I think that has to happen to some extent to be able to do our jobs, but I think that we can be a lot more mindful about the language that we are using in order to not oversell the simplicity, not present it as answers and like we’ve used statistics to reduce n=uncertainty which is not what we have done in any of these cases right. But that’s how this is kind of used is that calculator get us an answer type of way. So, I think a little example beforehand, it’s really common to use the word determine in reporting because that’s kind of a nice term to grab on to. That was a term I outlawed from my classes. We do not determine anything with statistics. So I think- and that’s a qword that creeps in to journalism a lot and I understand why and most of just don’t, you know, once you think about it once you’re like oh, that makes sense that I wouldn’t want to use that term, but even- I’m going to stop for a second while I look at my little example that I made, because I- so for example you might say researchers determine that X causes Y. right a very exciting headline, where we could easily change that to still be something short that’s more like early research suggest that X may cause Y in some people. So, like those two to me are just so different, and I think that when I read that as a statistician, I take away something very different. So, I’m kind of hoping that’s kind of the little pieces of curiosity in those type of reporting that we can promote.

Steel: And I think there’s also a demand issue, so if people are demanding really certain exciting results then it’s very hard for journalists write in a way that expresses how science moves with a bit of slowness and awkwardness and we have uncertainties that we’re constantly revising and if this blog can contribute to a better market for journalists so that if people want to consume news that has those uncertainties, they don’t just write it off as oh, we don’t know yet, they read it as an exciting step, that would be fantastic.

Pennington: You’re listening to Stats and Stories and today we’re talking about- oh, I’m going to start that over again I was going to say statisticians, for some reason I keep switching those words around. Three two one Charles. You’re listening to Stats and Stories and today we’re talking about statistics and news with Megan Hicks and Ashley Steel. Now you both have sort of expressed a commitment to sort of the public communication of statistical information. You have the critical inference blog Megan and Ashley you said this is something you’re passionate about. Why? I mean because it feels like it’s a step beyond traditional training. So you know to teach journalism is difficult because I know how to do it but then when you sort of step outside the actual doing to the discussion about it it sort of a more difficult place to sort of- I said sort of a lot- to manage or to be in. so I wonder what was it that made you feel so compelled to take that step to try to push for clearer communication of statistical information?

Higgs: We’ve also been working on a course at the University of Washington on statistical thinking and it includes statistical communication and all of that work is really because I believe it’s maybe too light of a word. I know that people can make better decisions in their personal and their professional life if they understand probabilities and data. And so the more- somehow that has been handed to the statistical community, that kind of knowledge, and I think we kind of need to do a better job at spreading it out again and sharing with people why is it interesting? Why do you need to think about this? How could you make a wrong conclusion or a wrong decision and how could you improve on your decision making? Even from parenting you have to take all these different kinds of decisions, they’re all probabilistic. Your own health decisions about whether to go on vacation, now we’re living in a world where every decision should you go grocery shopping at four o’clock or five o’clock is a probabilistic health related decision and I think the world can be a better place if more people understand statistics.

Bailer: In terms of parenting I always used random reinforcement.

[Laughter]

Bailer: You know keep them guessing. So the question that I’m going to suggest that Megan and Ashley you guys might have a question for Richard and Rosemary let’s put the journalists on the spot and lets say play journalists, you have a chance here to say what are some topics or stories that you would like to se covered as part of this blog?

Campbell: Well one of the things that has been one of my pet peeves as we have interviewed a lot of scientists and statisticians is the question of random sampling and why that wasn’t done more often and I think that we would know a lot more I think, that’s one story although again I think as you guys have pointed out this is a hard story for journalists to do because most of them don’t have the training or background to do this. I think from what I’ve learned it’s a lot better than it used to be. I think there’s so much good journalism out there that does things that are helpful to me. So that’s one story that I would like to see tackled, and journalists go after. I think I’ve heard a lot of complaints about this, but I haven’t really seen good reporting on that particular story.

Pennington: For me, so I did, in my past life medical and science stories were one of my beats and so I did a science literacy shop for journalists a million years ago out in San Francisco, and one of the most useful things I ever got was that a statistician came in and walked us through how to read a study. And so I think maybe down the road it might be worthwhile, especially if you’re thinking of you know journalists or the public coming to the blog and reading about statistics , like how do I read a study if I see something in the news I think looks interesting and I want to go and read the study how do I read it in a way where I don’t need to know maybe all of the layers of the statistical analysis but can get enough to understand whether there’s some validity or something worth trusting in this data is something that could help journalists as well as laypeople.

Higgs: Yeah that’s a great idea.

Steel: That’s a fantastic idea and one of those posts that I hope will arise, maybe even regularly, are posts that are of the category- five questions to ask yourself when you’re reading a news story. Because helping people know where to even focus- it’s very similar to what you’re saying, how to read a study, but ask yourself, did they mention sample size? Did they use a word like determine, prove, so I think we can make a lot of progress there but it’s going to take I think first a collective of statisticians to come together about all these different news stories so that we can agree- well maybe not. Statisticians don’t agree very easily, but we can come together with a good pol of questions that we can organize and provide in a very useful and efficient format.

Campbell: Well I want to turn the – go ahead.

Steel: Oh I was just going to say that to me it’s really important even if a person, you know we can give a person the skills to look into something or give them advice for how to look into a study and how to look further, but the reality is that most people are not going to have to do that it’s something Even just the recognition that there is more to this story. Like there is a rest of the story that is not being covered here I think is incredibly important. And to just get people to sit with that and realize it and not let that part kind of gloss over.

Higgs: That’s a great- I love the description of thinking about the resto of the story, and I’m going to hold that close. One of the phrases that I use a lot is efficient skepticism. Like we’re so skeptical that we can’t read anything without thinking it’s false or it’s wrong; that won’t work, we can’t get any information. But we shouldn’t be reading it- we should have some principles for how we can efficiently have enough skepticism to guide our understanding and I think that that’s a good principle also.

Steel: Yeah that’s moving skepticism to using curiosity, which I really- I used to use like healthy skepticism, but I think I really want people to be curious about the rest, like what else is there?

Campbell: Id like to go back and return the focus to statistician.

Steel: Yes.

[Laughter]

Campbell: So, Ashley you talk about the course on statistical thinking and you do a pre-course survey. You talk about in the stuff that I’ve read about this you talk about things that even statisticians get wrong, in terms of statistical thinking and I want an example.

[Laughter]

Bailer: I think that’s an idea that works for journalists.

Steel: I think there’s a lot of, of course statisticians get things wrong all the time, that’s how we lean. Like any other profession we learn and then we figure out what we did wrong last time and how to improve it the next time. There’s a lot of ways that the human brain just thinks about probability wrong. A lot of these were described by Daniel Conman and his Nobel Prize winning work, and I heard an interview by him where he described how he’s been studying these things for 20 years and he still makes incorrect assumptions. And I think that kind of humility- and I’ll get to an example in a minute, but what I think that kind of humility that we are naturally going to make certain kinds of mistakes is really helpful we are naturally going to see a cloud of dots make a pattern tell a story. We’re just going to do it. And we have to have a little red flag in our minds that says wait, wait wait. How many dots are there? How likely is that story? Could I have seen a similar cloud with just a random chance? And I think of it a lot like optical illusions. So, we see optical illusions and we see that one with the three lines and the arrows and we instantly think this is an illusion, this is a trick. I have to be careful. And I think there’s a lot of things like this about probability that we just can learn that when you see a small cloud of points in a pattern we should think wait, stop, let me check. When we see a probability we can think wait, stop, conditional on what? There’s a lot that we can do like that.

Higgs: I think that humility is a great point and kind of bringing it back to the blog too, one of my visions, I really don’t want the blog to become a statistician as hero narrative, or a scientist is hero narrative. I think that’s one of the problems that underlies communication and trust in science and journalism. So, but I think that’s all related to what Ashley was saying.

Bailer: I really like that characterization. That’s really nice. I mean the collaboration between statisticians and journalists I think is generally something you might find accepted and celebrated in this group.

Steel: Absolutely. I want to say something about the audience because in this interview was the first time I’ve thought of journalists as audience because I haven’t been thinking that we’ve somehow been telling journalists what to do. I’ve been thinking of it as a collaborative process. And I think there are so many audiences. Like one I’ve had was teachers, and I’ve imagined with this typical thing when some poor kid has to learn calculus, instead of probability which would be so much more useful, and they say you know like, why would this ever be useful? I’ll never need this in real life. So not only teachers are teaching statistics and some kid says I’ll never need this in real life. They have just an arsenal of ammunition all the ways that statistics can help them understand real life. And I think somebody made a comment on the blog about how they hope this can also enliven statisticians and help them remember the joy of communicating and the importance of- I mean we all get a little tired of our work over time and I think that it can help statisticians remember how valuable our work is and how much we need to communicate it better. So, I think there’s quite a lot of different audiences. It’s not meant to be a corrective blog; it’s meant to be an expanding ideas blog.

Higgs: Yeah I think the words that we came up with were instead of narrowing it down to groups in the audience it was scientifically literate and curious or something like that. maybe even scientifically literate is too restrictive. But yeah just curious about another perspective on the news.

Steel: So, I have a really funny story because I posted this on my personal Facebook page of course, when I wrote a blog and I didn’t expect personal friends who aren’t’ statisticians to have too much to say about it and I got the following comment back, and it said what’s the risk of a slightly overweight 36-year-old Egyptian drummer will be hospitalized from COVID if he gets a vanilla milkshake later tonight? And I was thrilled because of course it’s silly; it’s super funny, but it also says this person read the blog, thought about how those ideas that are about a newspaper article can also be applied to real life decision making and I was- to me, that’s the audience for the blog. For people to make these connections and to sort of highlight statistics is for so many personal decisions.

Pennington: I was certainly one of those kids who was like I hate math, I don’t understand, you know I was like, what am I going to use this for and then I took statistics in grad school and I had to learn regression by hand and all of these things, which was not fun but at the same time oh wait a minute, this applies in so many places and there was like that practicality in the way it helped me understand things. And I was like if someone had been able to convince me that this was math could lead me to down the road, maybe I would have cared a lot sooner in my youth, and have paid a bit more attention in those classes, but it was like this really a-ha moment in grad school like oh, stats is actually really great. So yeah it would be great if the blog could provide those moments for other people like maybe Richard.

[Laughter]

Bailer: Were going to have to do a golf episode or a golf article.

Campbell: Hey I want to know when we are going to drive calculus out of the high school curriculums and put in statistics, because calculus turned me off of math.

Steel: Any day. I am right there with you.

Campbell: Why aren’t we doing that?

Steel: There are some- it depends on the high school, but they do have-

Campbell: Well a lot more of our students are coming with statistics courses, but still I bet it’s fewer than half.

Steel: I hope that this blog can help fix a item bit of that because one of the problems is we don’t have enough teachers who love statistics at the high school level who know how to do statistics and can communicate it well. So, my daughters’ statistics teacher was the drama teacher. And that just made me bananas. And so, I think we need to get people excited from all kinds of different career paths about statistics and then we’ll have teachers and then we’ll have students. Megan Sorry.

Higgs: No, I just wanted to say something after. So, I think that is so important, but I want to put a huge caution warning that you don’t have to come through math to become a statistician. So, I did not come from a math background, I came from a science background. I started another Ph.D. before actually realizing like why are we using statistical methods like this like what is going on? Like I don’t have the background to do this and why am I being asked to? And then went back to stats. I mean I didn’t mind that, but I liked that part of it, but it doesn’t have to come from that. so even thinking about high school education I would almost rather my kid’s science teacher teach stats than a strict mathematician because that feeds into this calculation get an answer myth of stats that we’ve got to get rid of that. we’ve got to be in that dirty gray area and embrace that rather than the get the right answer mentality.

Steel: It’s interesting because I also didn’t come from a math background. I liked it enough and then I switched majors and I went on and then in graduate school I had to go to a statistical consultant. And it was such a horrific experience. They spoke to me in words I didn’t understand. They told me everything I was doing was wrong, and I left the room and I said I’m never going to be subjected to this again I’m going to learn it myself. And of course it’s fascinating but it’s a reminder to me when I go and I’m trying to communicate with people how easy it is to turn people off and scare them and there’s so much more to statistics than the very careful calculation that you get taught very early.

Pennington: Well that’s all the time that we have for this episode of Stats and Stories. Thank you guys so much for this conversation.

Bailer: This has been fun thank you.

Steel: Thank you that was really fun.

Higgs: Yeah it was great.

Pennington: Stats and Stories is a partnership between Miami University’s Departments of Statistics and Media, Journalism and Film, and the American Statistical Association. You can follow us on Twitter, Apple Podcasts, or other places where you can find podcasts. If you’d like to share your thoughts on the program send your emails to statsandstories@miamioh.edu or check us out at statsandstories.net and be sure to listen for future editions of Stats and Stories, where we explore the statistics behind the stories and the stories behind the statistics.

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The Most-Viewed Washington Post Article Ever | Stats + Stories Episode 142

As researchers and medical professionals struggle to get a handle on the COVID-19 pandemic, journalists struggle to tell the pandemic’s story with many news outlets increasingly turning to info graphics and data visualizations to help them do so. Visualizing data for news is the focus of this episode of Stats and Stories with guest Harry Stevens.

Harry Stevens joined The Washington Post as a graphics reporter in 2019. He is part of the team that won the 2020 Pulitzer Prize in Explanatory Reporting for its climate change-focused series.  He previously worked at Axios, where he designed news graphics and worked on data-driven investigations. Stevens's journalism career has also included stints at the Hindustan Times in New Delhi, India, and the Salt Lake Tribune in Utah.

• Full Transcript

Rosemary Pennington: As researchers and medical professionals struggle to get a handle on the COVID-19 pandemic, journalists struggle to tell the pandemic’s story with many news outlets increasingly turning to info graphics and data visualizations to help them do so. Visualizing data for news is the focus of this episode of Stats and Stories where we explore the statistics behind the stories and the stories behind the statistics. I’m Rosemary Pennington. Stats and Stories is a production of Miami University’s departments of Statistics and Media, Journalism and Film as well as the American Statistical Association. Joining me are regular panelists John Bailer, chair of Miami’s Statistics department and Richard Campbell, former chair of Media, Journalism and Film. Our guest today is Harry Stevens. Stevens is a graphics reporter at the Washington Post and produced a story in March about how disease outbreaks spread exponentially and how to flatten curves. It’s since become the most read story in Washington Post history. Harry, thank you so much for being here.

Harry Stevens: Thanks so much for having me. It’s great to be here virtually with you guys.

Pennington: Yeah, so your story went viral and I remember seeing it everywhere and it has so many different kinds of – I mean, so many different kinds of visualizations, how did you as you were thinking through the story decide how you were going to visualize this information?

Stevens: Sure, so the idea for the – so basically for people who haven’t seen the story- it features a series of very simple simulations of bouncing balls moving around in a rectangle and when the balls collide either with the walls of the rectangle or with each other they bounce off in another direction. And so you start out by making one of those balls quote-unquote sick, which is just to say making it a different color than the rest of them and then when a sick ball collides with a healthy ball, the healthy ball gets sick too or becomes the same color as a sick ball. And so, you can watch these simulations play out over the course of thirty seconds or so, and see how the disease spreads first slowly with the first infection being transmitted and then very very quickly. And then you can introduce certain parameters into the simulation; so you can try to put up a big wall in between some of the balls so that they can’t get to each other or you can make it so some of the balls don’t move, and by changing the parameters of the simulation you can sort of show ways that disrupt the network effect of spreading disease and give people a sense of how to slow down the spread of something through a network. So, the idea of the bouncing balls came to me actually just from sort of some fun experimentations that I had been doing on my computer over the weekend. I’m not- I don’t have, like, a computer science background, so a lot of the code that I’ve learned to write has just been from me doing these kinds of experiments on the weekend and reading tutorials online. So, one of the things that I was always curious about was collision detection. And so, what happens when two circles occupy the same space? They’re moving at a certain angle and a certain speed so when they’ve collided with each other what happens, and how do you represent that in code? And so, I had done some experiments with it- actually a series of experiments. I think the first one I did was two years ago and it was just like how to make it so that if a circle hits the side of a screen it bounces off in the other direction, which might be easy if you know geometry, but I had to look up all the formulas for like what is the angle of reflection based on the angle of incidents and how to make the ball bounce. And then once you’ve done that you have to figure out how to make the balls bounce off of each other when they hit each other, which is much more complicated. And I think like a year later I was like, yeah, I want to come back and revisit this code, and so I got the balls to bounce off of each other. And I mean there are things that are probably more interesting to like computer science people than to maybe all of your listeners, but just making the algorithm efficient because you have to compare the position of every ball to every other ball at each tick, but there are ways to make it more efficient so that you can add more balls to the simulation and it doesn’t crash your computer, and there’s all kinds of interesting things here. So, I had just been doing these kinds of experiments long before COVID-19 was a word that anybody knew, and I just thought they were fun to look at; they were fun to watch. And I think that that’s kind of part of the key to the success of the story is that like, even if you’re not talking about a disease, even if you’re not trying to teach somebody anything, it’s just visually engaging to watch these balls bounce around. And so that can draw people in or give them a door to step through to what it is that I am trying to teach them. So yeah, so like I had the bouncing ball thing already working, I had already written the code for it mostly; there was still some bugs like sometimes the balls would get stuck together and I had to figure out how to make it so that that didn’t happen, but it was mostly- that part was done. So, we were in a room with some editors and some other graphics reporters talking about you know, like, how can we move our coverage forward of COVID-19? And it was early March, so at that point, the President was still not taking it seriously. A lot of people across the country still hadn’t internalized the idea that the problem wasn’t necessarily them getting sick; the problem was that they could pass it on to somebody else. Like there were still people like spring-breakers who were just like, you know, I just want to party, and I don’t care if I get sick, as if that were somehow a brave position to have. But it’s like dude, it’s not about you getting sick, it’s about like killing my grandma. And so when you have these simulations you can see very easily that like, one transmission earlier on can infect somebody all the way across on the other side of the simulation very very quickly, even though the original sick person never had any interaction with the healthy person over on the other side. And so I think just seeing that maybe was something that a lot of people needed to internalize this idea that like things can spread very very quickly in a network if we don’t do anything to try to slow them down.

John Bailer: So, Harry I mean the question that was really burning when I saw this was, you know, when will the vaccine or effective treatment be available for simulitis? I thought it was really effective that you basically abstracted the kind of key features of this story of infection. You know, you didn’t get caught up on kind of all the other nuance that’s part of this, you know, how long people are – you know, would they be contagious? You know, what’s the pool of people that are susceptible to this? And I thought, you know, how did you kind of boil that down? What were some of the things you thought about in sort of extracting those key features?

Stevens: So, after publishing this story I worked on another story about these supposed SERI models, which, for those of you who aren’t familiar with those, it’s like an agent-based model where people, or the agents pass through these sort of forced stages of the disease. So, it started as susceptible which is the S and they become exposed which is the E, and then become infected which is the I, and then R stands for like removed which means they’ve either recovered or they’ve died. And so, when you’re building these models, there are certain parameters that you have to take into account like, with a real- so you’re trying to model a real disease. So, you’re like how long is the infectious period? Like, how long do people remain infectious for? How long is the incubation period? Like, how long does it take from when you were first exposed to when you become…? Anyway, there’s all sorts of- what’s the contact rate? And various other parameters that you have to build into these models to get them to reflect anything close to reality. And even then it’s a model, so it’s not supposed to be reality; it’s just supposed to give us some idea about future potential outcomes. And anyway so, that’s a long way of saying I didn’t know any of that when I started doing this story and even by the time I published it- like when I published it I didn’t even know what an SEIR model was. And I actually think that was, ironically, kind of helpful for me. Like, that I didn’t know how complex it would be, or all of the things that I could take into account. Like it made it so that there was no way I could do anything other than something that was really simple. Like, I knew that I wanted it to show something spreading through a network because I thought that the idea of exponential growth was not something that was intuitively understood, certainly not by me and I don’t think by most people. And so, if you can just show that, then that’s really all that I wanted to show. And one conversation that I had- I was talking to Lauren Gardener who is an epidemiologist at Johns Hopkins University way back in February and we were just talking about the model that her team uses to try to forecast the growth of Coronavirus- it was very very early on at that point so, so much was unknown. But she was talking about just the complexity of the model that they use and how it was computationally extremely intensive. So, like it would be hard to run that in a browser. And that was the conversation that really helped me understand that like there was no way that I could model a real disease. That was where the idea for simulitis came from. Just like we’re not trying to forecast a real disease, we don’t have to map the ticks of the simulation to any kind of real unit of time you know there’s not like a second of the simulation represents a day or an hour or a month; there’s no mapping to real-time because there doesn’t need to be right? Like that’s not what you’re trying to show, you’re not trying to forecast an actual disease. You’re just trying to show people how network effects work and how to slow them down. And so, I think that by having a very simple goal, that helped like really limit what the design space- like, it helped focus us and limit what I was trying to accomplish, and that made it so that it was easier to teach something that was like simple but important.

Richard Campbell: So where did this come from? Because you both are able to do this, but you’re also a very good writer and reporter-

Stevens: Thank you.

Campbell: So, that’s unusual in journalists, don’t you think?

Stevens: It is unusual; I think less unusual than maybe it was a decade ago. I think that- so I started out in journalism as a writer and a reporter which I think has been really valuable for me because I learned about, like, how to collect information, how to interview sources, how to frame a story before I learned any of the graphics and code stuff. So, all the graphics and code stuff are building on top of that foundation that I already had, and like how to explain thing to people, how to tell a story and how to find sources. So, I’m glad that I did it in that order. I also got- like I went to journalism school in 2013-2014, and I took a class on data visualization. So, I had never really thought very deeply about information design, and that class really opened my eyes up to how powerful it can be. How much information you can communicate visually? And so, like, taking that class was really helpful to me; it also introduced me to JavaScript. I’d never written code before, so we just did like some basic stuff, but it was enough of a building block. Like, once you know about stack overflow then you can pretty much learn anything.

Pennington: You’re listening to Stats and Stories and today we’re talking to Washington Post graphics reporter Harry Stevens. So, you- before the Washington Post you were working in Axios and have worked a few other places and have done a number of different kinds of data visualizations. When you are approaching a story that is going to be data-rich that you want to help an audience understand through this sort of graphic presentation, how do you think about- how do you approach that storytelling? Because you are- even though it is a graphic, right, and you’re dealing with data, you still at the end of the day have to communicate some kind of story. So, how do you approach that when you’re thinking about the kind of graphics you’re going to use in a story?

Stevens: Sure, so I mean making a news graphic is similar to writing a news story in that like you have to consider the information that’s going in and like how you’re collecting it. So like, if you’re just writing a story and reporting it, like you go find sources, you interview them, you maybe find documents, you read the documents and figure out what they mean, whereas with graphic story usually you’re finding datasets, but you have to interrogate a dataset with the same rigor that you would interrogate a source or a document. So, you have to figure out how that dataset is deficient, how the data was collected, whether there are certain biases inherent in how that data was collected. So, the same kind of reporting that you would need to apply to any sort of journalistic endeavor, you need to apply when you’re working with a dataset, and then once you analyze the data- I mean, so, if you’re doing a story with data, it’s not like you just have a dataset and you’re just playing around with it like for whatever might come out of it. Like, usually you have a hypothesis and you’re trying to see if the analysis bears out that hypothesis, and like a lot of times it doesn’t, and you don’t have a story. And that’s the same with any kind of reporting. Like you, might think that something is going on at City Hall but then you interview everybody and they’re like no, that’s not happening, and then you don’t have a story. So anyway, once you’ve done the analysis- like for me the graphics side of things is really the fun part. I try to make things that just look really cool and that are engaging to people and really fun, and so I mean part of it is like I can only make things that are cool if they communicate the central idea. So, like all of the aesthetic decisions for me come from the purpose of what I’m trying to communicate. So, like, I don’t know if you guys play chess but in chess, there’s – like people say tactics flow from a superior strategy so I look at that in the same way as like making a graphic. The aesthetic decisions flow from like your communication strategy. So, like what is it that I want to tell people? What do I want to get out of this? And then you know try to delete everything that doesn’t serve that purpose and then like once I’ve really gotten that it’s like refining it. So, you know, making it look beautiful or making it look clean, or you know, adding some kind of visual flair, but generally, it’s a balance right? Because you do – in the news business you need to make something that catches people’s eye and that is really cool, but you also need to communicate something as well. So, they have to work hand in hand but I guess if you have to get rid of one you’d get rid of the flair because you need to communicate something and that’s the most important thing.

Bailer: I’m curious: as you go through these types of representations, you know dealing with the uncertainty and the inputs. You know, I’ve seen that you’ve looked at a couple of different scenarios that might play out in a simulation. Do you have other ways that you help recognize and convey the fact that these models do have imprecision, they do have uncertainty that are part of it. And the input that’s provided in these models isn’t known and possibly can’t be known. So, what are some of the things that you’ve done that try to convey that uncertainty and variability?

Stevens: A lot- every journalist I think or like graphic journalist that’s covering COVID-19 right now is grappling with this problem. So, another story I did that I had mentioned earlier was about how these SEIR models work. And so one of the things that we did there was again we used a fake disease because we weren’t again trying to say anything about COVID-19, we were just trying to help people understand how the models work so we use simulitis again and this story, by the way, did it do nearly as well in terms of traffic, but I think that it was a bit more. I don’t know, I think that for the people who liked it and really wanted to dig in, I think that they enjoyed it, but for that one we just let people adjust the parameters themselves to see how that might affect the output, and then we tried to explain it using quotes from real epidemiologists that we had interviewed about like how grappling with uncertainty is at the very core of what they do. So, like the purpose of these models is not to like open up your crystal ball and tell people exactly what is going to happen. It’s just to help people who need to make important decisions understand like the range of possibilities and like how their decisions might affect the outcome. So like, you know, I can’t- no epidemiologist can tell you how many people are going to die of COVID-19 or how many people are going to be infected or when we’re going to hit the peak and when it’s going to start going down. Like, there’s just no way to do that with any kind of certainty. Like even people who predict the weather, for example, get it wrong all the time. And they’ve had many many many more decades to deal with that phenomena and to prepare their models, and there’s probably more certainty going in, but you can’t just get it right every time because nature is chaotic and it’s hard to predict the future. But the point is like, again, not to predict the future, but just to understand how our decisions can affect the range of possible outcomes. So that’s one thing that story tried to communicate other places have done a pretty good job. I think like 538 now has a tracker of all of the different model outputs and so just comparing them with each other I think is useful, like wow, there’s a really wide range of possible outcomes that these things are predicting and trying to explain to people. Like, what are the inputs going in? But yeah, I mean inevitably there’s going to be skepticism on the part of the public, I think, about a lot of these models because there’s like a sort of general misconception about what their purpose is and how they function. And then they’re inevitably quote-unquote wrong because they didn’t predict the future correctly and then people say the whole model is useless. But of course, that was not what the purpose of the model was, to begin with.

Campbell: So, one of the things I love about your work, and it follows up on John’s question about uncertainty, is journalists are really good at telling about what just happened or what happened yesterday, but you’re talking about what might happen. So here’s a story idea you already may be thinking about that because universities and colleges all over the country are thinking about should we open in the Fall, so it seems to me that you have a model that would suggest what happens at a place like Miami University when all these kids from all over the country come back, they’re not- you know, they’re living in their own places, they’re gathering they’re having parties on the weekends. Can you do a model that’s going to show what might happen as we decide whether we’re going to stay online for another semester or whether we’re going to you know try to go back to some kind of business as usual? Help us out here.

Stevens: I would definitely leave that to the professional epidemiologists because like, I’m not a statistician. For the story that we did on like the different disease models, we managed to find like a really basic SEIR model because it like has to solve these ordinary differential equations and I didn’t know how to do that so, fortunately, somebody, like a scientist, I think it was at like Los Alamos- he was a smart guy anyway and so he had written the code for this SEIR model that we used- far beyond the ability I had to do it myself, but certainly like it’s a really important question and like you mentioned, like it does seem, I mean, if I think about my life as a college student and then add like an extremely infectious disease that spreads quickly through that experience, I can imagine basically everybody getting sick. So, it definitely does seem like a very dangerous situation and a recipe for disaster. Particularly because it doesn’t sound like there’s going to be a vaccine any time soon, so I definitely don’t envy the college administrators that have to deal with this.

Pennington: Harry, you have mentioned math and coding, two things with which most journalists don’t get in undergrad and also are terrified of. I speak as a journalist who was thankful the only math classes I had to take were logic and statistics when I was an undergrad. So what advice would you give to journalists who want to try to work with visualizations but might be semi-scared of what goes into it, right? Because it does seem like it’s this black box where I need to know all these things and I can’t do it well, what advice would you give to someone who wants to explore this?

Stevens: So, I had the exact same experience in undergrad. Like, I had to take a math class for my – to graduate and I took logic. Now going back, like, I wish I had taken more math classes because it ended up being something that I use in my job all of the time and you know, there’s like- there’s just a knowledge gap between me and like people who have had more formal education that I work to close all of the time, and I wish I didn’t have to. But, that being said, like- so, when I started doing code and working with spreadsheets like in journalism like I didn’t know much math and I still don’t really know much; I know a little bit more but yeah, I mean a lot of times just measures of central tendency, like average and median. Those are like pretty useful mathematical tools to help you try to figure out what’s going on and you don’t need to know a lot of math to do those things, and a spreadsheet will actually do them for you. So, a lot of times it’s just about like using the tools that are available. Like, you don’t even need to know how to code. Like, I did sort of data journalism pieces for a couple of years without knowing really how to code well; I used Excel- in fact, I still use Excel for a lot of data analysis. I mean, if I’m going to do something that’s a little bit more complicated or needs to be reproducible yeah like I’ll use [inaudible] or I’ll use JavaScript, but a lot of times I still use Excel. And I think that like, I mean Excel is not great, but it does introduce a lot of errors that you have to be aware of. Like it will change your dates for you without you wanting it to do that, and various other things that can definitely be problematic, but it’s like it’s still a better tool than nothing. And so, I definitely think, like, if you can learn how to do a pivot table in Excel, you’re going to know a lot more than most other journalists. Like if you want to start to use data in your reporting just go online and Google how to use a pivot table and suddenly you’ll realize that like you have this new superpower that you didn’t have before. So, I think you learn incrementally, and I guess- it’s part of it is like having the mindset like this is not too scary. Like I’m just going to try to learn one thing at a time and get better as it goes.

Bailer: Yeah, I’m going to ask the complementary question. As someone who got out of writing because I did despise the subjectivity of assessments when I was in [inaudible] and I decided that, thank God, that there was a place for people like me where I didn’t have to deal with it, but yet now what I do is- what I do more than anything else is write. So what kind of advice do you give to the people that are coming from the quantitative side that are doing data analysis and modeling, but still recognize the importance of that communication and integrating the important part of the story that goes with this? What kind of suggestions do you have for folks like me?

Stevens: Yeah, that’s a great question I think one thing you can do is read The Elements of Style; that’s a great book. I read it and it helped my writing so much. You know, use strong active verbs, delete unnecessary words, you know, verbs are stronger than adjectives, stuff like that. The other thing is like empathy is the biggest thing both for writing and for making graphics. Like, you have to have empathy with your readers. Things are just hard to understand, generally, in life and so you have to work really hard to make things easy to understand. Like, really try to put yourself in the shoes of one reader and think like, what are some possible ways that this sentence might be difficult to interpret, or this paragraph might be difficult to interpret? And just make it better that way. I really think just like having empathy with your readers or your viewers can make your work so much better.

Pennington: Well Harry, that’s all the time we have for this episode, thank you so much for being here.

Bailer: Yes, thank you for being here.

Stevens: Thanks for having me. This was really fun.

Pennington: Stats and Stories is a partnership between Miami University’s departments of Statistics and Media, Journalism and Film, and the American Statistical Association. You can follow us on Twitter, Apple podcasts, or other places where you find podcasts. If you’d like to share your thoughts on the program send your email to statsandstories@miamioh.edu or check us out at statsandstories.net, and be sure to listen for future editions of Stats and Stories where we explore the statistics behind the stories behind the statistics.

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Making the Census Count | Stats + Stories Episode 106

Mike Ananny is an Associate Professor of Communication and Journalism and Affiliated Faculty of Science, Technology, and Society at the University of Southern California’s Annenberg School for Communication and Journalism. He studies the public significance of networked news infrastructures and the politics of algorithmic systems.

Mark Hansen is a professor of journalism where he also serves as the Director of the David and Helen Gurley Brown Institute for Media Innovation. Founded in 2012, the Brown Institute is a bi-coastal collaboration between Columbia Journalism School and the School of Engineering at Stanford University -- its mission is to explore the interplay between technology and story.

News Counts is made possible by a grant from The Knight Foundation

+ Full Transcript

Rosemary Pennington: The Census is something most people don’t think about until they get a Census form in the mail or meet a Census worker in person – but taking a census of those living in the United States is one of the most important things the government does. Census data influences the creation of voting districts, determines how many members of the House of Representatives a state sends to Congress, and can impact the types and how much funding communities receive for various projects. Helping journalists communicate to their audiences the importance of the Census to American life is the goal of a new project and that is the focus of this episode of Stats and Stories where we explores the statistics behind the stories and the stories behind the statistics, I’m Rosemary Pennington. Stats and Stories is a production of Miami University’s Departments of Statistics and Media, Journalism and Film as well as the American Statistical Association. Joining me in the studio are regular panelists John Bailer, chair of Miami’s Statistics Department and Richard Campbell former and founding chair of Media, Journalism & Film. Our guests today are Mark Hansen, Director of the Brown Institute for Media Innovation at the Columbia Journalism School and Mike Annany an associate professor in the Annenberg School of Communication and Journalism at the University of Southern California. The two are working together on the new project News Counts which aims to help create quote a robust national conversation about the serious and imminent challenges facing the 2020 Census. Mark & Mike thank you so much for being here. How did this project come about?

Mark Hansen: That’s a good question. I can tell you, personally, I’ve been involved in the census for a while. I did some work with the Census in the 1990 Adjustment case, and back when I was in graduate school. As when the Census came around and started to be a thing in the news, I saw more and more need for responsible reporting around why the Census is important. I can’t remember how Mike and I—a mutual friend of ours brought us together because we both had an interest in the Census. And Mike –

Mike Ananny: Yeah, my geeky background story is- so I’m Canadian I’ll put that on the table-

[Laughter]

Bailer: It’s okay.

Ananny: I actually tried to volunteer for the Census I think in like 2000 when I was a student and was refused because I was Canadian. But I’ve been fascinated by the Census for a very long time. More as a social phenomenon, than a scientific endeavor, and then now as a Journalism professor. I think I was motivated by the idea that journalists rely on the Census a lot. They rely on the idea of the Census, on the data of the Census, but they don’t necessarily know that. There’s a lot of- especially as local journalism has become decimated and newsrooms have shrunk, and these kinds of things- that it was going to become even more important this time around for journalists to fight for the power of the Census and figure out what it meant for them to use it. So, I was motivated to try to help journalists do their role as story-tellers with census data.

Campbell: Can you tell us just a little bit about what the NewsCounts network is? Or what you hope it will be?

Hansen: Yeah we started with the idea that- as Mike said- that the local newsrooms are having a tough time of it and that it’s more than likely a typical newsroom doesn’t have any [inaudible] memory about what happened last time [inaudible] to either sort of track where their community is in terms of people in numerators been hired, that kind of thing. And also perhaps not really realizing the extent to which the Census is important to their local government, the statistic keeps moving around, but I’ve heard anywhere between six and eight hundred billion dollars come back to the local communities based on census counts. So, our thought was that we could help build up some of that expertise by bringing in academics, demographers, social scientists, statisticians, computer scientists, data scientists- bring them into the newsroom or in dialogue with the newsroom, and perhaps community groups, to sort of pitch sessions, if you will. To try to come up with good stories and help find good stories and then also help with the technical lift to tell those good stories. So the network was an attempt to cross a number of different disciplines to bring good journalism to the public, so they understand the stakes that are behind the Census. Especially, once the citizenship question appeared.

Campbell: Okay one of my- as a Journalism professor- one of my favorite quotes that John and Rosemary have heard me use a lot is “the job of a good journalist is to make the significant interesting”. That’s from Bill Kovach and Tom Rosenstiel, and their book The Elements of Journalism, so how are you going to make this interesting? We know it’s significant, you also mention the problem, the news deserts we have- I mean we’re kind of in one right here in southwestern Ohio right now. This is a really important story, I think building a network- I mean, getting things that are local- local news outlets interested in this- is going to be a particular challenge. Especially in newsrooms where the staff is decimated. So have you thought about that particular problem? I know nationally, I think, there’s going to be a lot of stories about this and there already have been in The Times and The Post, but this is a story that’s going to affect people locally in their regions, and those newspapers need to be doing that so tell me a little bit about that challenge.

Ananny: Yeah, I can speak to that a little bit. It’s one of the guiding principles really is, that the local newsrooms know their communities best. So I don’t think that Mark or I or this network would ever try to talk down, say “here’s what the stories are, here’s what they should be”, but more to offer some starting points for local journalists. I think when we’ve spoken with folks in local newsrooms, they know intuitively the importance of the Census, and they know it but they don’t necessarily have concrete examples. So one is setting up a conversation among local newsrooms, where people- and some journalists have already told us this – they’ll say, “Oh yeah, I did a project about library funding”, or something like that, and that acts like a bit of a spur to some of the other folks. It’s sort of a comparative approach where local newsrooms can get ideas from each other, so that’s sort of one thread. And the other is the pop centers and the local demographers and the social scientists who are also in these local communities who are not journalists, per say, but are so familiar with the data infrastructures and have a lot of – almost story prototypes already, because they’ve been working with the data and the process for so long, that I think a lot of the motivations and ideas and story frameworks and leads- in a way, could come from them. From demographers and the pop center folks who haven’t been journalists per se, but have a lot of ideas about what the important stories are. That’s the two mechanisms that I think that this network could really bootstrap.

Hansen: And I think if I can add- ultimately, the Census story- I mean there are obviously national policy level questions but ultimately the Census story is a local story. In that, it will vary tremendously from place to place. And with this census, you are seeing the involvement of civil society groups who are helping their constituents understand the importance of the Census. I mean these groups are involved – probably like never before, and I think to help bring in, as Mike mentioned- demographers or social scientists- you know, people who have- and local governments who have an understanding of what it means locally to be counted and where that count has impacted the community, I think that’s going to sort of- the sum of all those parts is going to be truly amazing story, but it’s going to be told ultimately locally and by the specificity of local community.

Bailer: I find this really cool, I’m very intrigued but this, the idea of having a particular framework and lead that might be suggested. It sounds like there could be even story templates that are developed that could be locally developed, locally expanded and flushed out. Do you have particular examples of some of the types of story frameworks or leads that you have thought of initially?

Ananny: Well, I think that yeah, for me- one of the big buckets is sort of economic development set up stories. So the Census data’s relationship to economic development. So we speak very specifically- you know, in Los Angeles there’s a lot of conversations around gentrification, for instance, and the economic dynamics of gentrification. So I think something like gentrification which is actually something that is happening in a lot of mid and larger cities around the US. It has a cultural component, a demographic component, it has an economic development component, but I would – for me the bucket, or category of gentrification, is one I think is one that is really interesting, to think about how census data could inform that kind of storytelling. That actually links to – what I would not want this network to be doing is trying to create brand new, from scratch, themes or stories, that these newsrooms and locals have never really thought about. Ideally, I think we would say let’s look at what you’re already doing, think about what you’re already doing, don’t try to reinvent yourself as an expert in some brand new area. But think about how both the census data and the relationships to the local demographers could enhance or could help the storytelling and the reporting that you’re already doing. So gentrification is one of those examples that readily come to mind for me because it’s such a complex phenomenon, and there’s a data component and a local relations component that’s already happening. The story of gentrification is already being told in a lot of cities across the US and has local flavors that Mark has been talking about.

Hansen: I think form, a story that I’ve been spending some time with population divisions in New York City and they’re concerned with things at the local level like disaster preparedness, right? So a hurricane rolls through or something like that, and being able to have an accurate count of where people over the ages of 70 are along the coast or that kind of thing becomes really important, so it becomes like a- again, it’s sort of hard to – these stories once you start to unpack them, come so easily because it’s hard to find an aspect of our lives that don’t require that kind of base map of the Census. To understand where people are living.

Pennington: You’re listening to Stats and Stories and today we’re talking with Mark Hanson and Mike Ananny about a new project they are helping launch called NewsCounts. I use census data a lot in a multimedia class I teach where I have students dig around in the data and then create an infographic from it, where I try to teach them how to think about infographics. But what I often see my students do is struggle to find the story in the data because there is so much data in census material. What advice would you have for journalists to not become overwhelmed by the data, and how would you see this network helping journalists move through this material?

Ananny: So I teach data journalism at Columbia, and I try to avoid the “here’s a big data set, find a story in it”, for exactly the effect that you’re describing. People get lost pretty quickly, it – you almost want to teach them the flip, which is there’s an issue that I’m interested in, whether it was disaster preparedness I mentioned before or gentrification, or more locally in New York there was a discussion about closing Rikers, for example. You have a particular problem that you’d like to address. What data sets are there to assemble? And then that guides your path through that data set. It can be very difficult even in a statistics class to say, “Here’s a data set, now go find something”.

Hansen: I totally agree with that, so I teach a class on the history of journalism. It’s sort of history from the US news from kind of revolutionary war period forward, and one of the things I have students do with data sets is have them do a historical comparison approach. Well, let’s go look into the archives and- not quite randomly, but I have students do one which is words and depictions of women in different time periods in newspapers. And we look at how even from titles that are used or how women have been used from sources and are not appearing in news sites, newspapers in different time periods. So we’ll use something like how people being depicted in the news over time, and then that becomes a thread that we can follow. And then I say okay well let's go try to use data from a census in a different time period, and then let’s go try to use data from the Census today – how have the data in the Census shifted and where might those shifts have happened and how are those shifts represented in news? So I sort of takes a historical approach. And often students are really- they get jazzed pretty quickly about these historical questions because it lets them explore different kinds of folk theories, about why might shifts have changed and where do those shifts come from?

Ananny: You know, it’s interesting, in one of my classes a student had picked up a topic he was interested in gentrification, and when a beginning data student picks that up you go “uh-oh”.

[Laughter]

Ananny: Because as Mike has indicated there’s like a rich topic and so you’re going to want – you know, it’s like the “white whale” of statistics, right? The curse of dimensionality, right? So what he had done was to use the Census to find neighborhoods in New York City where the median income had gone up significantly in the last ten years or so, and then use the Bureau of Labor Statistics to figure out what- in those neighborhoods how had businesses changed? So, what was the makeup and changeover of businesses? And then married it all with Google Street View, which in New York is available back to 2005. So we can go back ten years and see the change on the street of what had happened, right? Because, some of these gentrification questions, when you come in midway-

Pennington: Right.

[Laughter]

Ananny: -you don’t know what’s happened before. So there was a kind of nice melding of data, but again it started with that base map of the Census, and what’s there on the ground.

Campbell: So one of the things that’s interesting to me – we had John Thompson on in 2017, about a month before he resigned. He was to direct the Census and I think he directed the Census in 2000, is that right? I think he did. One of the things that he told us in our podcast with him is that 40% of people who don’t self-respond, and the challenges of getting people to respond to this. Partly, some are just suspicious of government but I’m thinking we have the challenge of- this is going to be the first time we’ve done online responses, and these are stories to me, you know, first of all the people that feel like the Census is not important so they don’t respond initially, so have you thought much about this and how – I mean this is one of the things we’re worried about, right?

Ananny: Yeah, especially you know like somebody who’s living in Los Angeles- who’s a large undocumented community that’s in Los Angeles- and that’s right in my neighborhood, that’s a really strong phenomenon, and I think you’re right, and that’s where the delicacy for me of the local journalists who know the local communities really well is powerful, because you’re right. The absence of the answering of the Census is a story in itself, but it’s a story that needs to be told in a way that is super respectful and careful of the precarity of the people who are even outing themselves as people who have not answered the Census or has- so how to report on that absence. And I think that’s a perfect marriage of looking at the meaning, the statistical meaning, of those absences in the data set, and then doing the interviews and the more ethnographic, close observation, that truly only happens when you’re a journalist who has a relationship with the local community. I mean at a neighborhood level. These are people that you see every day, that’s the only way that you can have the trust to be able to tell the story of that absence. So in Los Angeles- in a lot of other cities, not just in L.A.- but it’s a huge, really delicate thing to try to report on.

Hansen: I think part of our project is to make it clear to communities for members of the community who view this as a choice, even though it’s sort of legally mandated, but they see it as a choice, that the tradeoffs that are being made when they make that choice. By not filling in the Census appropriately, all these other things are being tagged to your presence in this community. All these different programs are being funded in a certain way because of counts that are happening at this level, in a local area. So there are consequences locally, if the count is it-if there’s an undercount of a particular group, so I think that’s why you see so much [inaudible] and make sure that communities understand that things are safe, that filling out the Census is an important act. And I think bringing journalism into that is critical because we have a role that is unique in society and can really help people understand that trade-off they’re making.

Bailer: It seems to me that a lot of times that controversy is what drives people to read stories about the Census. I mean, I think that when you talk about all the money that comes back to communities, that’s clearly a critical element of the impact of the Census, or representation or other kinds of allocations, but it seems that when you see the Census getting a lot of attention, it tends to be because of things like the 1990 adjustment, and some of the controversy that seems to be associated with that for many groups and then most recently some of the discussions of the citizenship questions on the most recent census. So how do you balance the eyeballs being attracted to controversy stories associated with the Census with some of these deeper impact importance of the Census questions?

Hansen: I think the way I would – there are two ways. One is to recognize that there sort of a national level controversy story that some people- some audiences might not be distinguished from sort of the local phenomenon that’s going on so that if you’re reading or watching the big central TV news mostly coming from east coast large media organizations, then you might have one image of the controversies that you’ve described, but for a lot of people, on a local level the controversies- yes they’re important and significant and meaningful but they play out really differently, and I think articulating the tradeoffs I think that articulating the tradeoffs that Mark was talking about, sort of like yes if you see this as a choice then understand that a choice has consequences. Sort of separating this national narrative which, agreed- feeds on controversy and feeds on this drama of the question- separating those from the local dynamics is really key. And that’s what we see with local news audiences, are really different than the ones that are plugged into the national news audiences. So paying attention to and keeping those people in mind as your primary news audience is I think the best.

Ananny: And the other thing is that I think some of these national controversies, whether it’s an adjustment or maybe the new moves around differential privacy, those are also, I mean – it’s a hard thing to count everyone in the United States, right? And so ultimately there are technical questions that arise, mathematical questions that arise, and I think even at a national level- and this is a bit outside the scope of the project, and I apologize- but even at a [inaudible] level there is something that we as statisticians should be doing to help the public understand how these mathematical concepts are impacting what- on the face of it would seem so easy just by using the word “count”, right? And you just walk around going “one, two, three”, but in fact, it’s really hard and there are all these [inaudible] and I don’t even [inaudible]

Pennington: So what does success look like for NewsCounts, as you guys are thinking about outcomes?

Hansen: For me, so I think success would have a few different dimensions to it. One would just be helping this – I think of it as almost his community that’s just below the surface, that’s almost information that just needs a little bit of help or infrastructure to nudge itself over. So one would be to see stories emerging from collaborations among local journalists. So that would be a huge thing for me, if I could see local journalists in small markets who would not necessarily had the infrastructure or the power or the time or the resources to do that reporting on their own. If they partnered with one or two other newsrooms and then together did a story that would be huge, I think that would be great. The other dimension I think about is the relationship with the local social scientists and demographers in their communities, so that it’s less about cross-relationships among journalists, but it’s more about looking within and around your own community, and reminding yourself maybe or discovering new relationships that you didn’t know you had with your social scientists. And then the third dimension that comes about is a network that lasts beyond the Census, so that it is something that doesn’t just get stood up for this one particular challenging census, but that journalists and demographers and social scientists take it on for themselves and say, “Oh, well we’ve built something here that could apply to a whole other area that we haven’t thought of before”, instead of just relationships. So those are, for me, the sort of three dimensions of success that I would love to see happen.

Ananny: For me the lingering, I mean obviously getting a good count, right? Making sure that we don’t mess this up. We live with the President for four years, we live with the Census for ten, right? But we get a clean count, would be an obvious outcome. But as Mike was saying, leaving behind this relationship [inaudible] that has never – that had been involved now with computer science or statistics group to tell a census story maybe a year down the line has another data related story that they would like to tell, they know who to call now, and are comfortable because they have a working relationship. And at the same time maybe that rubs off a little bit on the computer science and statistics so that they have a little bit more of a public mission. So, if they see something in their communities they can bring it to a newsroom and feel comfortable with that kind of dialogue.

Bailer: So what do you want someone who’s interested in participating to do? So you have somebody who’s a statistician, data scientist, journalist, demographer, whatever specialty- how do they plugin and what is it that they would do?

Ananny: The first, easiest thing is to get a website. NewsCounts.org and if you go there, there’s a button at the very bottom of the page that says join NewsCounts and you’ll get messages from me and or Mark and our team that gives other ways of participating.

Campbell: I did that yesterday and I still haven’t heard from you guys.

[Laughter]

Ananny: And our personal email addresses are on there as well. I’ve been contacted by a few newsrooms actually, and what I think we would like to do is- as quickly as possible- start doing some matchmaking and bringing people together. So I would encourage the listeners, the statisticians, computer scientists, data scientists, data- people of all stripes, you know, let the conversation with the news station in your area and get things rolling. Or if you have a conversation already going, let’s talk about it and see what works and what was hard for you or what was easy. Because we’re coming to this as facilitators, if you will, so we’re coming to this with design process in mind, with pitch process in mind- so we’re not coming in saying these are the stories you should tell and this is the data you should use – we’re coming more to listen and to help facilitate conversation so that something authentic can come from the localness of it all.

Pennington: Well, Mike and Mark that’s all the time we have for this episode of Stats and Stories. Thanks so much for being here.

Both: Thank you.

Pennington: And good luck with NewsCounts.

Ananny: Thank you so much

Hansen: Thank you for the opportunities.

Pennington: Stats and Stories is a partnership between Miami University’s departments of Statistics, and Media, Journalism and Film, and the American Statistical Association. You can follow us on Twitter or Apple podcasts or other places where you find podcasts. If you’d like to share your thoughts on the program send your emails to statsandstories@miamioh.edu or check us out at statsandstories.net and be sure to listen for future editions of Stats and Stories, where we explore the statistics behind the stories and the stories behind the statistics.

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