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Episode #177: Christine Zhang
218 Plays4 years ago
Christine Zhang just joined the Financial Times as a data journalist on the US elections team for 2020. Previously, she was a data journalist at The Baltimore Sun, where she used numbers, statistics and graphics to tell local news stories...
The post Episode #177: Christine Zhang appeared first on PolicyViz.
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Transcript
Introduction to Christine Zhang
00:00:11
Speaker
Welcome back to the policy of his podcast. I'm your host, John Schwabish. On this week's episode, I'm very excited to chat with Christine Zhang. Christine was a reporter at the Baltimore Sun until very recently when she started a new exciting endeavor, which we talk about in this week's conversation.
Local vs National Media Experience
00:00:25
Speaker
I was excited to talk to Christine because I've talked to a lot of people in the media sector who work at national or international news organizations. And so I was excited to talk to Christine.
00:00:35
Speaker
get that local perspective of what it means to work in a local newsroom. So I hope you'll enjoy this week's episode. Before we get to that very quickly, if you'd like to support the show, please share it with your friends, your family, your neighbors. Please consider writing review of the show on any of the major podcast providers that you might listen to, Spotify, Stitcher, iTunes, Google Play and so on and so forth.
00:00:58
Speaker
And if you'd like to support the show financially, I'd also really, really appreciate it.
Christine's Career Path: From Data Journalism to Financial Times
00:01:02
Speaker
Just a couple of bucks per month helps me transcribe the show, helps me pay for sound editing, helps me pay for web services, all the things that I need to bring this show to you every other week. So let's get on to the show this week's conversation with Christine Zhang. I hope you'll enjoy it and I'll help you learn something. Hi Christine, how are you?
00:01:27
Speaker
Hi, I am doing well. It's a rainy day here in Baltimore, and I am on my way to moving to New Jersey, so it's definitely an interesting time of transition. Moving is stressful enough, but... Enough, right? Moving is like the most inopportune time. Yup. I don't recommend it to anyone who's thinking about it. Right, right.
00:01:52
Speaker
So we're gonna talk a little bit about why you're moving in a little bit. So we'll let that hang in the air for people so they can. Yeah, some suspense. Some suspense, yeah, for the show. So I'm excited to chat with you about your past work and your current work and your future work, which is really exciting. So maybe we'll just start with, you can just introduce yourself for folks and a little bit about your background and then we can just chat.
00:02:17
Speaker
Yeah, sure. So I am a data journalist. That's my official job title. And currently, I'm working at the Financial Times, where I just recently started this spring, mainly to cover the US election this year. And before that, I was in similar roles at the Baltimore Sun, hence why I'm in Baltimore currently, as well as at the LA Times.
Podcast Inspiration and Influences
00:02:41
Speaker
But I actually have a pretty varied background. My interest in data journalism,
00:02:47
Speaker
had only started just a few years ago before I became a full-time data journalist. I was a research analyst at a think tank.
00:02:57
Speaker
the Brookings Institution in Washington, DC. And so I'm actually doubly excited to be on this episode because I feel like, I mean, I don't know if you know this, but like a lot of the data stuff that I was exposed to in DC, including the policy, this podcast and blog were what really inspired me to go into data journalism in the first place and like explore different ways to communicate and visualize data in the news. And, you know, I'm not just saying that, like if you want to like check my Twitter timeline, it's true.
00:03:28
Speaker
So it's really cool to be here. That's great. Well, I'm glad we're able to talk. It's interesting because it's actually been a few folks from Brookings who have gone on to data journalism, like Chris Ingraham at The Post. Yeah. I think he and I didn't overlap. I think when I started there, he had just left for The Post, but he's been like,
00:03:51
Speaker
one of my data heroes slash inspirations as well. And I met him a couple of times and he's just really,
Local News Impact & COVID-19 Insights
00:03:57
Speaker
really cool.
00:03:57
Speaker
Oh, he's great. And here's a little, little background treat how I know Chris. So Chris and I knew each other. I was at, um, CBO at the time he was at Brookings and somehow we met up and started talking about, you know, our love of data and data is, and we decided to try to get together and learn D three together. And it was Chris, me and one other person at Brookings. And, um, after a few weeks, I was just like, okay, this is just, I'm not going to be a D three programmer. I can see it. It's just not going to happen.
00:04:27
Speaker
But that's really cool though. Yeah, that's my link to Chris. From this background, you don't necessarily have a perspective so much in looking at a local newspaper like the Baltimore Sun to a national newspaper like The Post or The Times. But I'm wondering if you can give us maybe just
00:04:45
Speaker
you know, a bit of your experience of working as a data journalist at a local newspaper like the Baltimore Sun. And I'm sure there are listeners out there who will be able to relate or, you know, have some perspective on that more than I will, of course. But just, you know, what does the day to day look like at a local newspaper when you're doing data journalism? And maybe not, as I think a lot of people at the big newspapers or the national newspapers are working on, you know, what might be like bigger projects and national projects.
00:05:12
Speaker
Yeah, for sure. Well, I mean, first of all, I think right now is a really interesting time to think about local versus national slash international news. You know, so much of the news coverage has been dominated by COVID-19.
00:05:28
Speaker
And rightly so, since it's such a big issue of our time. But I think for me, it really highlights the comparative advantages of the different types of news organizations. Right now at the Financial Times, there's an amazing data team.
00:05:48
Speaker
that I'm a part of. And many of my colleagues in London have been tracking COVID-19 from an international perspective, looking at different countries and how their trajectories have evolved. And to get that perspective, I think places like the FT are really great. And even to get like a national perspective as well for the US. But I think as a person living in Baltimore, for instance, like I am now,
00:06:15
Speaker
I obviously still follow the sun to get updates on my own community and what's happening at a neighborhood level here, right? And that's not something necessarily that the Financial Times would cover because it's geared towards a more global audience. So I think it's really interesting because I think it really
00:06:36
Speaker
for people in general highlights the different ways that both can be very useful to people.
Cultural Stories and Team Dynamics
00:06:44
Speaker
And for me, working at The Sun was really an incredible experience. I have a lot of personal attachment to the Baltimore area.
00:06:54
Speaker
It was actually the first place that I lived when I moved to the United States from China. So it's been a lot of years and I've lived in many different places since then, but I took the job at the Baltimore Sun in 2018 because I wanted to follow my dream of becoming a data journalist ever since.
00:07:13
Speaker
as I mentioned, I was in DC, but also because I kind of wanted to rediscover this place from my childhood and really understand it and how it's evolved in the intervening years. And my first story for The Sun actually represents both of these things. So my first story for The Sun was called The Gender Gap is Real for Crabs.
00:07:38
Speaker
It's kind of a strange title, but basically I was updating a project that my predecessors on the data team at The Sun had published that tracked the price of steamed crabs in more than 30 local crab houses in the area. So for those of you who don't know, Maryland is famous for its blue crab and eating it and how to eat it is like a whole thing. I'm not going to get into that now.
00:08:05
Speaker
The point is, this is actually a pretty useful quote unquote public service data set. If you're living in the Baltimore area and you want to know where you can find the cheapest steamed crab or even the most expensive one or the closest one to you, you could go to this website and find out.
00:08:22
Speaker
We had used a dozen male crabs as the comparison point. So we would call these places and ask them how much they charged for a dozen males. But when I was calling these places, I remembered that while I was growing up, my parents and I and
00:08:40
Speaker
a lot of their Chinese American friends had actually preferred eating female crabs to male crabs. So I started asking all of these places that I was calling anyway for this project how much crab houses were charging for female crabs. And it turns out that the charge for female crabs was much less than the price for males.
00:09:02
Speaker
So yeah, the article is like a first person essay that talks about the reasons for this price disparity or this quote unquote gender gap, which I think if you count if I did calculate it on average, it like kind of matches the like average pay gap between the male and females in the US. So it's like a bit tongue in cheek. I'm not like
00:09:25
Speaker
It's not that profound. I'm not going to win a Pulitzer for it. I didn't, spoiler alert. But I do think it's interesting because it's not just a matter of size or biology as you might think. It's actually a question of culture. So that was just really great to work on. It includes a fun lollipop data visualization that visualizes the gender gap at each house. So that was really great.
00:09:49
Speaker
And so one of the really great parts about working in a local newsroom is that there are so many opportunities to work on lots of different kinds of stories, like the Crab prices story, which was just kind of, I don't want to say random, but definitely like an atypical sort of story, right? And a very personal one to me, but it also had data. So it was like a really quirky thing.
00:10:12
Speaker
And I think, for better or for worse, the data team at The Sun was pretty small. When I started there, there were three people, including me. By the time I left, it was just me on a full-time basis. And the entire newsroom is only about 80 or 90 reporters, editors, and photographers.
00:10:32
Speaker
like, you can imagine what the challenges are. And I think anyone who's worked in a local newsroom can imagine those challenges. But on the other hand, there wasn't like a whole lot of room for territoriality, like, among beats or anything. It wasn't like, I don't know, there was like a crab correspondent who was like, no, you're, you know, this is my son. It wouldn't totally surprise you if there was a crab correspondent. Maybe someday.
00:11:00
Speaker
Yeah. So I think that was nice. I got to work with lots of different people. Collaboration was really key. For me anyway, I didn't feel like there were too many silos. But I think a good example of that is one of the projects that I did, which involved some collaboration with the sports department. Yeah.
00:11:26
Speaker
So Lamar Jackson, the quarterback of the Baltimore Ravens, was going to be named the MVP.
00:11:36
Speaker
Basically, everyone knew that that was going to happen, or at least like we definitely thought there was like a high probability. And so the son decided to create a special section dedicated to him. And they asked me to work with Tracy Rawson, a Baltimore Sun designer to create a two page
00:12:00
Speaker
graphical spread, visualizing some of Lamar Jackson's main achievements. And I mean, I'm not like the biggest sports person. And I mean, I only like recently learned this was embarrassing. I probably I could tell you like the basic rules of football, but that's basically that's it.
00:12:25
Speaker
I was like, oh my God, I don't even know where to start. I don't know anything. But I actually think it's a great example of why you need to have somebody with subject matter expertise in addition to somebody with data expertise or visualization expertise. Because if you don't, then you could end up creating some random chart that makes no sense whatsoever.
00:12:54
Speaker
you know, that doesn't actually take into account the different nuances of football and what data points really matter. So I'm really indebted to a lot of reporters who are very patient with me in describing the rules of the game.
00:13:11
Speaker
So when you created that piece, because I think when I first saw that piece, I think someone had taken a picture of the paper version and that's where I saw it first and then I saw it online. So with those types of pieces, are you designing print first, online first, or just both simultaneously? Yeah, that's a really good question. This is interesting because I think as much as a lot of people like to say things like, oh, print is dead or it's all digital first all the time.
00:13:40
Speaker
I think in certain instances, like special
Transition to Financial Times and COVID-19's Impact on Journalism
00:13:43
Speaker
sections of newspapers or magazines, the print version can be as compelling or even more compelling than the digital version. And in this case, because it was designed to be
00:13:57
Speaker
almost like a collectible section, not just my part in it. Obviously, it was an entire special Ravens section. It was designed to be a standalone collectible portion of that newspaper. We tried to design it so that it would fit for the confines of print before translating it to the website. Right.
00:14:24
Speaker
So you spent some time in Baltimore working on local news. Um, although I think Lamar Jackson was, you know, bigger than just local.
00:14:32
Speaker
Um, now you're moving to New Jersey, um, and you're working at, um, a different kind of place. Uh, do you want to talk about your, your move and, and the work you'll be doing or already are doing, I guess, uh, over there? Yeah, sure. So I've been working remotely for the past couple of months. Um, well, I guess not just me, everybody is sometimes I forget because I was only, it was only supposed to be me, but now everyone.
00:14:57
Speaker
is I got a new job at the Financial Times. And this job is to be the US elections data journalist, basically focusing on the 2020 presidential election in the US, which is huge. I mean, it's definitely a very big election year. And I think it's almost coming full circle because 2016 was my first
00:15:25
Speaker
quote-unquote real job in journalism, and I was at the LA Times on their data team, and that was also, as you know, a very different and unprecedented election year. So yeah, it's been pretty awesome so far. Yeah. And so it's weird right now because the election seems to, well, not seems to, has taken a backseat in a lot of ways to where we all are. So
00:15:52
Speaker
Do you want to talk a little bit about the other work that you've been doing and maybe how you're laying the groundwork for the political coverage you'll do over the next however many months, six, nine months, wherever we are? Yeah, sure.
00:16:07
Speaker
It is weird. First of all, I want to take a step back and just think about, after 2016, when I would give talks about data journalism and what it means and what do data journalists do, my most prominent example was polls and election forecasting. Since post-2016, that really started to become
00:16:35
Speaker
for better or for worse, what people associated with data journalism the most.
00:16:41
Speaker
So all of my talks were all about like, well, data journalism encompasses political polls and in some cases election forecasting, but there's like a wide range of topics that data journalism could possibly cover, including football or crabs or crime rates. You know, it's like all of these things could have data points. Maybe you don't notice it as much, but it definitely does. Now, though, I really think that like in some ways
00:17:11
Speaker
COVID-19 has been what people think of when they think of data journalism. I don't know. I'm like positing this as a theory and not as a definitive thing. I mean, I don't know, but I kind of feel like, you know, whereas like polls were what people associated with data journalism the most in the past, now it's like coronavirus trajectory charts. And like, I think there's actually a lot of parallels in that, like, with
00:17:40
Speaker
Election data there are so many nuances to the ways that things are presented like uncertainty and margins of error and like what a forecast actually is and means that can cause a lot of confusion in the same way that like.
00:17:59
Speaker
data on coronavirus can also cause some confusion. There's been, I think you did a podcast episode about data viz in the time of COVID and certainly not all the data are necessarily reliable or maybe there are some delays that may make cases appear to be decreasing when they're actually not. So it's a really nuanced thing as well.
00:18:28
Speaker
But yeah, so obviously, as I just laid out, the coronavirus pandemic is really taking over a lot of the traditional election coverage. And I think for me, I've kind of been moonlighting, I should say, a little bit as like,
00:18:49
Speaker
the Baltimore correspondent with regard to certain aspects of coronavirus. One of my colleagues in London, Federico Coco, she's a statistics journalist who does a lot of videos that explain different things in series of charts. And she had an idea of looking at
00:19:12
Speaker
crime in the time of COVID-19. And I also had independently thought of that in my mind. For Baltimore, for instance, it has the highest murder rate among large US cities. I was wondering if the fact that people might be staying in more would have an effect on homicides in the city. And Federica had
00:19:41
Speaker
a thought of a similar thing with regard to violent crimes in London. So she asked me to pair up with her to maybe talk about the US perspective, starting with Baltimore and expanding to different cities on how crime has evolved post lockdown, not post pandemic, unfortunately, because that won't come for some time.
00:20:08
Speaker
But it was an interesting exercise because I think for me, it's like I know Baltimore crime statistics. I know all the nuances of those statistics and what to look out for in terms of interpreting them.
00:20:25
Speaker
But I had to really think about ways to expand beyond just talking about Baltimore and figure out a theme to apply, not necessarily to the entire US, but to many other US cities.
Challenges of National Data Storytelling
00:20:44
Speaker
So I came across a news story from The Trace, which is a nonprofit newsroom that covers gun crimes in the US.
00:20:55
Speaker
And it mentioned how shootings may be an exception to the decrease in violent crimes. So this is where I plug the programming language R, which I use for a lot of my data analyses and where I
00:21:16
Speaker
give a special shout out to Daniel Nass who was the author of that trace article because I asked him to send me some of his R code that outlined how he was calculating crime statistics in different cities and I modified it and applied it to a selection of cities for this crime video and
00:21:39
Speaker
Basically, the upshot is violent crimes have, in general, decreased post-lockdown. I think that might be pretty intuitive. If there are fewer people outside, there might be fewer opportunities for robberies and things like that. But gun violence, shootings, assaults,
00:22:00
Speaker
homicides have in most cities either increased or not gone down at the same level as other violent crimes. For you personally, what is that shift like going from, you know, if you were doing the same story at the sun, it would have been presumably just focus on Baltimore, maybe neighboring cities like Philly and DC, but now
00:22:25
Speaker
with the, with the FT you're looking, you know, nationally and internationally. So, so for you, what is that like having to do that shift? And also what does that mean for your ability to tell the stories with data? It seems like it's a little bit, I don't know, is it easy? I guess I'll just ask, is it easier or harder to tell those stories when you're dealing with, you know, hundreds or dozens of cities as opposed to just one city in the neighborhoods in the, in that city?
00:22:50
Speaker
I would say that it is, in those cases, for me slightly harder. I think one of the maybe advantages of working in local news is that you could, if you have a national data set, you focus on the part of that national data set that applies to the city that you live in or the coverage area of the news organization that you're working for.
00:23:21
Speaker
But when the audience of your news organization is so much broader, then you have to broaden the perspective accordingly.
Cross-Continental Collaboration Opportunities
00:23:33
Speaker
So I think Baltimore is interesting in some cases.
00:23:39
Speaker
on its own, like from a national or even international perspective. But I think that particular video is strengthened by the overarching theme of violent crimes are down, except for certain crimes in these places. So in the UK, that would be drug offenses. In many US cities, that would be gun crimes.
00:24:01
Speaker
And in Mexico, which was the other place that another data journalist, Jane Pong, looked at, it would be homicides. So I think it's really interesting in the sense that it requires more creativity, I think, sometimes, in terms of thinking of
00:24:22
Speaker
common threads or themes in terms of weaving stuff together. But there's also a lot more opportunity for collaboration across continents. And it's something that it's not like I would necessarily think to look at what homicide trends in Mexico were like or crimes in London, the trends in crimes in London. So I think that's interesting.
Clarifying US Elections for International Readers
00:24:52
Speaker
Yeah, definitely. Go ahead. Oh, I was also going to say another aspect of working on the US election for the FT, even though, as you mentioned, a lot of things have slowed down in recent weeks, is that there is an aspect of writing for the US audience, but also an aspect of explaining or making things interesting to an international audience.
00:25:20
Speaker
Right? So like things like Super Tuesday.
00:25:25
Speaker
which was my first article, I believe, for the FT, was a Super Tuesday explainer. Now, I think certainly from a US perspective, a lot of people could imagine why that matters, but I don't think necessarily people living in other countries totally understand all of the intricacies of the US primary system.
00:25:52
Speaker
And honestly, a lot of people, I suppose, in the US, myself included, don't understand all of the intricacies. I spent maybe, I don't even know, hours trying to find the answer to the question, what happens to a candidate's delegates if they drop out of the race?
00:26:09
Speaker
Which you think is like a simple question and actually at that point mattered because there were multiple contenders but is actually a really complicated answer that gets into a lot of weird convention rules that nobody has looked up except for maybe a couple of scholars.
00:26:32
Speaker
And you. And me. So I mean, I was really proud that we were able to answer that question. And I'm hoping to do a lot more of that as well, like really try to ask and answer questions that may seem quote unquote simple, but actually are more nuanced. Another story that I worked on with my colleague, Brooke Fox, who's in New York, focused on swing states and what the
00:26:57
Speaker
polls and swing states are showing along with the demographic data. And, you know, one key question is, what is a swing state and what is a battleground state in the first place and people might say oh okay like that, it just makes sense because
00:27:13
Speaker
swing states are obviously states that like in 2016, Michigan and Wisconsin were states that really helped to decide that election. But the interesting thing is it's not like back in 2016, people would have predicted that Wisconsin was going to be the pivotal state at this same point.
00:27:40
Speaker
before November. So I think like it's kind of like we're just using the past to inform on what we're talking about now, but like that might not be indicative of what's the most important thing to focus on in the future. But it's also like, you know, this is kind of like the best that we have.
00:28:02
Speaker
So certainly like I think election reporting definitely gets into a lot of deep philosophical questions.
Partisan Views on Economic Conditions
00:28:11
Speaker
You mentioned earlier about polling data and I know the FT has this new monthly poll that they're running and I wanted to ask you to maybe talk about that a little bit and maybe also along with that since you're going to be
00:28:31
Speaker
you know, neck deep in polling data in the net over the next few months, how you think about communicating either visually or well, primarily visually, I guess, communicating the uncertainty and the margin of error in those polls as they come up. So that's kind of a two part question. But so really on the on that F that new ft poll, and then on how I guess you as a data journalist think about communicating the uncertainty around those sorts of estimates.
00:28:55
Speaker
Yeah, that is definitely getting into the philosophical questions of data visualization and journalism. So the FT, this was before I arrived there, actually. So the FT had partnered with a foundation called the Peterson Foundation, a nonprofit organization.
00:29:18
Speaker
to conduct a monthly poll about US voters' sentiment about the economy ahead of the election. And they've actually conducted it ever since October of 2019. So including May, whose data I think we should be getting within the next week or so, we'll have eight months of data from that poll. And it's interesting because I feel like
00:29:47
Speaker
A lot of news organizations do polls, and a lot of news organizations report on polls, especially during election time. And a lot of those polls, with like I could say with exception to questions about coronavirus, a lot of those polls boiled down to like, who are you going to vote for?
00:30:08
Speaker
Trump or Biden. And like, here's the answer. And like, you know, then we can discuss, you know, who has the lead. But this poll actually doesn't ask those questions. It focuses on economic sentiment. So the main question is that the poll focuses on is, since Donald Trump has become president, would you say that you are financially
00:30:36
Speaker
and there's a set of answers. So it's like somewhat better off, much better off, no change, somewhat worse off, or much worse off. So basically it asks whether voters think that they are financially better or worse off since Trump has become president. That's not the only question, but it is the headline question of the poll. And it takes its
00:31:03
Speaker
cue from a question that Ronald Reagan had asked Jimmy Carter back in 1980. And he basically, I think it was like the week before election day. During a debate, he asked Americans to ask themselves, are you better off than you were four years ago? Jimmy Carter was the incumbent president. The economy was in a recession.
00:31:33
Speaker
Rhetorically, I would say most voters answered no on election day because Reagan won in a landslide, of course. But the answer to that question is not so simple right now. I think that when the FT and the Peterson Foundation started this poll last year, the economy was in good shape and I think was for the incumbent president, Donald Trump,
00:32:03
Speaker
a way of signaling some positive sentiment for his reelection agenda. So at the time, it was an interesting question because if most voters said that they were not financially better off, even whilst some of the economy was doing much better,
00:32:25
Speaker
that might signal something for the election. But now it's obviously the complete opposite. A lot of economic indicators, including unemployment, are at record numbers. There's record
00:32:41
Speaker
high unemployment. So the question now is kind of like, are people going to change their answers to that question over the coming months and will that signify something for the way that they'll vote in November?
00:32:58
Speaker
essentially like will the economy matter and to what extent it will. And I think it's interesting because I have seen just by looking at the past months of data for this poll that the answers are highly
00:33:17
Speaker
partisan. So only 11% of Democrats say that they're better off since Trump became president. I think 63 or so percent of Republicans do and those numbers, those haven't changed that much since October. It's kind of like basically two flat lines one way above the other.
00:33:43
Speaker
And it might show that despite these historic, record-setting economic indicators in a bad way, historically negative economic indicators, partisanship is still extremely important in terms of the way that people view their own individual situation.
00:34:10
Speaker
And it might be almost a referendum as to whether partisan identity is more important or at least more on people's minds than numbers on certain statistical indicators.
00:34:32
Speaker
Well, I think it's also personally, you know, your individual experience, right? I think a lot of the commentary I've seen about COVID, for example, is a lot of people who are arguing that we don't need masks and we don't need socially distanced and all those techniques and approaches that public health experts are talking about.
00:34:49
Speaker
you know, a lot of people who are against those approaches, there hasn't been a huge outbreak of COVID infections that we know of or deaths in their area. So, you know, I think as soon as you start to see your friends and your family and, you know, your colleagues and coworkers start to get sick, I think it says something different. Same thing when you start to lose your job versus you sort of see 30 million people losing their jobs. Maybe it's just a different, you make a different connection to it.
00:35:17
Speaker
Yeah, I think that's true for sure. It is interesting how people view their own personal thing versus a more abstract question. I will say there's only one other poll that I could find that tracks a similar question, and it's the Economist's YouGov poll.
00:35:37
Speaker
And it asks the question, is the country better off now than it was four years ago?
Visualizing Poll Data: Challenges and Learnings
00:35:43
Speaker
And so to your point, you know, 50% of the respondents as of the beginning of May has said that
00:35:54
Speaker
the country was better off four years ago. And 30% say that the country is better off now. But again, that is like, so divided by party ID. Like, I think, again, like, a lot of a lot of answers to these questions, boils down to partisan syrup. So I, I do think that that's something to keep in mind. Yeah.
00:36:20
Speaker
Um, before we go, before I let you go and get your truck packed, uh, I wanted to come back to this, this side, this concept of uncertainty. Um, I mean, you, you, have you been working with the FT, the, the current FT poll?
00:36:38
Speaker
The, um, uh, yeah, I have. Yeah. So, so working with that and then looking ahead, like, how do you think about, um, visually at least communicating the uncertainty and the margin of error? And you know, we're all going to see that like 3% polling margin of an error number come up over and over and over again in a few months. So have you, and maybe folks you've worked with thought of, you know, talked about how, how the best way to, to communicate the, those errors and the, and the uncertainty around them.
00:37:05
Speaker
Yeah, we've thought about it a lot. Every month, actually, Lauren Federer, who is a DC correspondent for the FT, and I work together on a story about the latest findings in the poll. And I'm also in the process of designing a site to house the now eight months worth of data and to highlight the key findings. And
00:37:29
Speaker
I think deciding whether or not to visualize things like the margin of error is kind of a tricky thing. For one, it's not entirely clear to me at least how you would do this. So say for the month to month answers to the better off question, right now we've decided to visualize this on a line chart with two lines. So one line
00:37:54
Speaker
month to month shows the percentage of those who say that they are better off since Trump became president. And another line shows the percentage of those who say that they're worse off. And each of those lines will have a ribbon around it that represents the confidence interval. It's supposed to be like a visual display of the zone of uncertainty, which for any given month is about plus or minus three percentage points. So if it's like 30%, then we do like
00:38:24
Speaker
33% would be the max and 27% would be the min and that would be like the zone of uncertainty. And I think intuitively, this makes a lot of sense. But like, it's actually kind of misleading from a stats math perspective. I'm like going back to my college stats classes.
00:38:47
Speaker
Because what we're doing is drawing a confidence interval for the better off-answer and a confidence interval for the worst off-answer separately, 95% confidence interval. Which again, why is it 95%? Why don't we do 90%? I'm not even going to get into that. We're just saying 95% just to make things simple.
00:39:10
Speaker
But really, the correct thing to do is actually to draw a confidence interval for the difference between the two and visualize what the difference is. So I think maybe it's easier to think about in terms of candidates. If Biden is leading Trump by 5 percentage points,
00:39:36
Speaker
is that five statistically different from zero. Right.
00:39:45
Speaker
That's like the quote unquote correct way to think about it, not like the individual percentage point that Trump has and the individual percentage support that Trump has versus the individual percentage support that Biden has. It's the margin of error of the difference between the two is actually like the correct way to think about these things. I think that
00:40:13
Speaker
There's a professor Charles Franklin that I found a paper by. He wrote something called the margin of error for differences in polls. That has been like instrumental to my explaining this and thinking about this. But it's all just to say like those lines that you see with the ribbons around them are only visualizing the individual confidence intervals of each person or each answer or choice.
00:40:40
Speaker
But what you want is, if you truly want to find out if one is leading the other, is to visualize the confidence interval around the difference between the two. But that makes for a less compelling visual. Like, I don't know. It's super complicated for people to understand, too. Yeah. So it's not like an easy thing. And also, if you have more than one answer choice, then you're just like,
00:41:07
Speaker
it's going to look just really, really confusing. So I think the best thing to do is just talk about the margin of error in the text or to indicate it in a footnote. In a lot of the stories that we have done over the past few months about the Peterson polls, we do try to indicate what the margin of error is, not just for the
00:41:29
Speaker
poll itself, which is plus or minus three percentage points, but also for the different subgroups in the poll. So most polls actually do have a margin of error of plus or minus three percentage points. And that's not a coincidence. It's because most nationwide polls sample about a thousand people.
00:41:53
Speaker
The formula for a margin of error is roughly equivalent to 1 divided by the square root of the sample size. So 1 divided by the square root of 1,000 is 3.167. No, 3.162. I just did this. So basically, plus or minus 3, right? So you don't even have to tell people how many people you sampled. They say plus or minus 3 percentage points. You basically know. That's how much.
00:42:22
Speaker
But, so that's fine if you're looking at the overall top line questions like, are you better off versus four years ago or whatever. But when you start looking at different subgroups, like Democrats, do Democrats say that they're better off than they were when Trump was elected? Now, the poll, this particular poll, only asked 300
00:42:49
Speaker
or so Democrats. And so that sample size is now 300. So one divided by the square root of 300, just like back of the envelope calculation here is like 5.7 percentage points.
00:43:04
Speaker
So that's different from three percentage points, right? And I think it's hard because when people see this as a poll with a margin of error of plus or minus three percentage points, they automatically add and subtract three percentage points from every percentage they're going to see in the article, even though in some cases it is like almost double that.
00:43:29
Speaker
Wow. Yeah. Yeah. So like, it's hard. It's not, I don't think it's easy, but I do think that it's worth explanation in many cases. No, it's a good, important challenge and one that there's a lot of room to try to solve and to experiment with. Yeah. You're kind of that unique position where you can try lots of different things and see what people respond to.
00:43:58
Speaker
Yeah, that's a good point. I was going to say, I don't know how data visualization can solve this because I feel like my answer was like, write it in the text or put it as a footnote, which is not like really, um, you know, data-visy. Yeah. Right. But then if you do something like the needle, I don't know, like people just remotely freak out. Yeah.
00:44:22
Speaker
But, but to that point right when you try to do, I mean the needle maybe like kind of the extreme example and for those who don't know we're talking about, I'll put a link on the show notes it's this needle gauge chart that the New York Times did in the 2016 election about probability of winning. But I think, you know, trying some of these other graph types out.
00:44:44
Speaker
you know, you run into this problem that people don't necessarily know how to read these other graph types. And so you end up in this a bit of a catch 22 of, do I try a graph that maybe shows the distribution in a better way, but fewer people really understand how to read it? Um, or do I try a bar chart, but then, you know, how do I show the uncertainty in the, you know, in a better way than just like an error bar. So.
00:45:07
Speaker
Yeah, it's definitely a learning process. But I think that the learning is happening. I think that, you know, for example, I think in 2016, when Clinton won the popular vote, but Donald Trump won the electoral college, like that was such an anomaly, right?
00:45:27
Speaker
I mean, I think the last time it happened was 2000. And that was so exceptional for people. And I think that now there are a lot more election reporters, myself included, that take that as a possibility or take a look not just at the national polling average, but also at the state level polling averages. So I think
00:45:55
Speaker
it is an incremental thing. But yeah, it's happening. That's great. Well, that's right. Well, thanks for chatting with me. Thanks for coming on the show. And good luck with the move. I
Conclusion and Support Encouragement
00:46:08
Speaker
look forward to seeing all the stuff you're going to be working on and coming out with over the next few months. Thank you so much for having me.
00:46:19
Speaker
Thanks to everyone for listening to this week's episode of the show. I hope you enjoyed it. I hope you learned something and I hope you'll be able to take some of the lessons from this week's episode and apply it to your own work. So again, I hope you'll consider supporting the show either by sharing it, by writing reviews, or even financially by going over to Patreon. So until next time, this has been the Policy Vis Podcast. Thanks so much for listening.