From Polling to Prediction Markets: How Reliable Can We Predict Elections?

Introduction to election polling

0:00Hello, and welcome to the Harvard Data Science Review podcast. I'm Liberty Vittert, the feature editor of the Harvard Data Science Review. In this month's episode, HDSR Editor-in-Chief Shali Meng and I are taking a closer look at election polling, how polls are conducted, what they can tell us about voters, and the challenges facing modern survey research, all in preparation for the Harvard Political Analytics Conference 2026 taking place October 8th and 9th.

0:30in Washington, D.C. We're joined by two leading voices in the field who will also be speaking at the conference. Anthony Salvato, Ph.D., is CBS News' Director of Elections and Surveys, and really the voice of elections, and Michael A. Bailey, author of Polling at a Crossroads, Rethinking Modern Survey Research, and the Colonel William J. Walsh, Professor of American Government in the Department of Government and McCourt School of Public Policy at Georgetown University. We talk about what goes on behind the numbers, the changing landscape of election polling, and what researchers and really the public should keep in mind when interpreting survey results.

1:09So let's get into the numbers and what they really mean.

Inside the CBS News decision desk

1:12So, Anthony, walk us through what election night, and let's go with a big election night. So let's go with, like, you know, the 2026 midterms, 2028 presidential, whatever it is, a big election night. What does it look like at the CBS News decision desk? What do you know at 7 p.m. that the public doesn't? And what's really the hardest part of calling a race responsibly?

1:42Sure. One of the things to start off with is that I'm really proud of the fact that in recent cycles, we've put the decision desk front and center on our broadcast, which is a way of being transparent and going after and addressing some of the questions like the one you raised. How is it that we make projections and what is it that we're seeing? The idea being that we want folks to see things at the same time and just as we see them.

2:14And that's also part and parcel of the reason that I present some of that data directly to the viewers. So I'm always grateful for the chance to do that. In terms of specifics, early in the evening, we always start to see the exit poll. And while that doesn't necessarily tell you who's going to win any particular race, it does give you an early sense of the dynamics that are taking shape. What was on voters' minds and what types of voters might be inclined to go one way or the other.

2:48That starts to set the stage. One of the things I like to talk about or show people is that at the decision desk, we look for patterns when we're describing a race and when we're getting or approaching a projection. What I mean by looking for patterns is even though many of us who stand at the touchscreens will point the audience to a particular county, a key county. Watch this one. Watch that one. And that's all fun and that can tell an important story. But to actually make a projection, and this is something I'm sure that the listeners of this podcast know, what you're looking for is consistency in the data.

3:26And so that will be across many counties or across many precincts. We will be looking for shifts from past vote. We have models that form a ratio estimate of the current vote to the past vote. We evaluate whether or not those models are pointing in a consistent direction, and we will start to get usually multiple models of a race in real time. One of the other details in there is we see differences in vote type, not just differences by region, geographic region, urban, rural areas, how people vote, but also in the fact that the vote by mail has often differed and often favored Democrats.

4:13So there's this process at the decision desk of trying to learn from the individual counties from reports what types of votes are being counted as well, and we're transparent about that also. So when all of that comes together and we see a consistent pattern, that's when we can start to make a projection, and we've been describing things along the way. It obviously gets really intense as you start to go about 8, 9 o'clock Eastern, and many states' polls have closed.

4:45But as I often remind people, it often involves a few cups of coffee, and more recently, things have been gone late and even into the next day, which just keeps it exciting as we go.

The breakdown of random sampling

4:58Anthony, what you just said always reminds me that, you know, first, how hard the work you all have been doing. It's incredibly hard. And part of the reason it's so hard is that, you know, whatever you can do obviously is influenced by the quality of the data. You talk about, you know, being consistent, but we also have seen how polls are consistently biased, consistently wrong, because the difference between people who report, people respond, who people don't. And that's, I think, Michael's topic in your wonderful book, polling at the crossroads, and you argue the random sampling paradigm is effectively broken down.

5:35How did that problem show up in this cycle? Did the pollster handle it better or worse than the last three elections now, 2016, 2020, 2024? Every election, we see this problem persistent. Yeah, I mean, basically, as you alluded to, the polling paradigm of calling 1,000 people, they all answer, and then you ask them who they're going to vote for. That, like, is amazing. It's amazing, powerful, amazingly powerful, as everyone knows. It's unbiased, and we can exactly characterize the degrees of confidence about it. We are light, light, light, light years away from that.

6:07Maybe one pollster, the New York Times, tries to do that, but again, as you guys know, they're getting 1% response rate, and they're on the high end of things. And, by the way, like the, and this will get in as we talk more about waiting, you know, like there's a lot of groups that really aren't that hard to know what's going to happen, you know, like, you know, white non-college rural residents or, you know, lawyers in big cities or, you know, like those groups are pretty easy. But there's a few groups that are really, really hard. Hispanics tend to be hard, and they're moving, and maybe moving fast, and could determine the election in lots of places.

6:42Young people, like, they're not going to show up to vote, sadly, in my view, but they're not going to show up to vote. But to what extent do they not show up to vote in the midterm election? And so for those groups, you'd be lucky to have a half a percent response rate. And so there, and that's what my book is part of a project, is, like, we have to really think about when we look at any subgroup, and that's basically the way we're going to solve this problem, is we're going to divide the electorate into subgroups, and for each subgroup, we're going to put a number on it, and then we're going to patch together those subgroups. And within those subgroups, that's where things get really, really tricky.

7:15And within the groups that are the most interesting ones, it's even harder. So they're really flying pretty blind. And so one of the things you're going to hear a lot of, and it's actually true, is that pollsters do pretty well on average. But my claim is they're doing okay when it's easy, right? So, like, you can kind of, like, if you know what's going to happen, you know what's going to happen. But in New York City, is Mom Donnie going to get young people to turn out to vote for him? Pollsters said no. They were wrong. He did way outperform the polls. So in general, polls are kind of fine. But, like, you really have to be careful when it really matters.

7:48And that's why it's still going to be an adventure, and Anthony's going to have a busy night. He can look at those polls going into the election, and you've got the Republicans losing in Idaho. And, like, do you really believe those polls? Maybe, maybe not, right? And that's going to come down to actually what happens on the ground, precinct by precinct. You know, I think one of the biggest problems that we see with midterms is turnout. You have these levels, right? You have primaries. We have low turnout issues. We have midterms. We have low turnout issues. And then all of a sudden you have the, you know, the 28 election where everybody wants to come out and vote for the president.

8:21And so how much do you all think that really affects the ability for pollsters to have any idea what is going on? Is it more of a turnout problem than a sampling problem? And is this something that you think there's going to be a way to figure out how to fix in the future? And Anthony, I'll start with you.

Turnout models and behavior

8:43Yeah, let me put my polling hat on because I do these polls. And this comes back in context to some of what you were just discussing. Let me vigorously defend polling for a moment with this as a guide. First thing to do is separate measurement of attitude and measurement of behavior. They're two different things, of course. Attitude is anything we see in the polls like, you know, how do you think the economy is doing?

9:15Or how do you rate a particular candidate? But behavior is what you're talking about, Liberty, right? Which is turnout. What are you going to do? And there's a measurement difference in that. The attitudinal measurements, of course, we can all talk about questions. And certainly some of that will come into modeling or sampling. In the turnout case and behavioral measurement, you can sometimes have an expressed behavior.

9:46I'm going to do this. But things get in the way in the human experience, right? You might have a busy day on election day and not make it to the polls, even though you meant what you said when you told the pollster that you would definitely vote. The other part of this is that we have never had so much good data about voters. And this is so important to polling. And quite frankly, it's one of the things that makes polling good when it's good, which is you can take a publicly available voter file.

10:24Every campaign has them and everybody uses them. And you can see people's turnout history. You don't know how they voted, of course, but you can see who's turned out. And you can build and you can backtest very good models for who is, at least based on their history, likely to turn out. And so you can, if you choose to, and some pollsters do, choose to sample or even oversample folks who are going to be more reliable voters, you can assign a turnout score to every voter in your sample and you can incorporate that voter history.

11:04So every poll you see is a model, and you are seeing that pollster's model of an electorate. Is it easy to do? No, it's not. But one of the things that's really incumbent on all of us pollsters to do is explain to people that these things can vary, not simply because of sampling or whether it's a random sample or not, but also because there are people making these decisions.

11:39There are people who have lives and might be on the fence or might not get there, and also because let's not forget there are campaigns out there who are working to change this on the fly. And, of course, pollsters sort of tried out the usual thing, well, it's a snapshot in time, yes, but there's a campaign out there that's trying to motivate that person who might have told you, the pollster, that they are maybe going to vote, and they're trying to get them to change that point. And we have to allow for that. In fact, we have to, in some ways, celebrate it because we are measuring something that's ongoing.

12:13Otherwise, that's why they have campaigns. So these things can change over time, and we need to tell people that we are capturing part of that dynamic and that it's ongoing, and that's part of the story. So I really, really strongly agree with Anthony who was making that point that pollsters are modelers now, right? But then as we start unpacking that, I think it keeps us in business, by the way, because it's very, very difficult. But one of the first things, again, setting the paradigm of the old pollsters who just did a survey, they wouldn't have to know anything about anybody going into it, right?

12:51They would just get a random sample, and it would just bubble up, and they could just look at that. Instead, they have to know all these things and make these models, and the models are really assumptions. And so two things that I think are really important about this. So one is you have to, and this is literally what's happening, and it's every pollster is doing this in their shop right now, is you have to say, what's Republican turnout going to be? You're like, I don't know. If we had a random sample of 1,000 people, we could just ask them questions, and then we'd just see, you know, of the Republicans, who's going to turn out to vote.

13:21And there's a little wonkiness of people can change parties and so forth. So it's not trivial, but you would get that. Here instead, they're going to say, they're going to do a sample of 1,000 people, and they're going to get, let's just say they get 150 Republicans. No pollster ever would say, ah, I think 15% of the electorate is Republican. They're going to be weighting that group up or down. And how are they going to weight that group up or down? It's more complicated than I'm saying, but the basic idea is they're going to say, well, in general, what's the share of the electorate that is Republican in previous midterms? They're going to take their weighting sampling frame, and they're going to put that into their model.

13:55And then if they are right, right, and if they have the weights right about what percent is going to be Republican, they're in business, and they're going to get it right. And that's what happened a lot. Like when you go from 2020 to 2024, you can look at 2020, how people turned out when there's Donald Trump running against a Democrat, and you can look at 2024. And so 2024 did really well, actually, in part because like the behaviors were really, really quite predictable historically. To take a canonical example where that wasn't the case, and I'm not saying this is like always the case, Barack Obama, when he was running in Iowa, the people going into that election had a turnout model of who's going to show up for caucuses.

14:34And it's in part based on who turned out before, and they have pretty good data about who's going to turn out before. And they used those models, and then they would predict something that was very, very wrong because the underlying assumptions were no longer true. And that moving target, that's where the challenge is, is to keep up with that moving target. The second point is then within each cell of the sampling frame. Again, there's other methods, and they don't literally always have cells in sampling frames like I'm talking about, but they're effectively very similar. Within each cell, you have to decide the people who did respond, are they different than the people who didn't respond?

15:07So, again, like the real interesting groups tend to be Hispanic voters, tend to be non-college white voters. You know, there's other young people. Those groups are really, like, really kind of hard to predict, or we really, really want to know what's going on with those folks. I really, as a statistician, I really love both of your emphasis about Oster is a modeler because, you know, there are sometimes there are general perceptions there. Well, you know, this is all science. Well, it is trying to be scientific, but there's also lots of arts to it. There are lots of judgment.

15:37And I think that's really incredibly important to emphasize.

Polling methods and prediction markets

15:42At the same time that I, Anthony, I want to ask you a little bit, if this is not a confidential, about the kind of pooling methods CBS, you know, uses. And the reason I'm asking this is really trying to get your input on the kind of tradeoff between these different methods, right? There are these online up-in, these are probability panels, these are traditional phone pooling. Now, people even talk about using AI to summarize in public opinions, you know, so that's all the tools available. And I want to, you have such experience, I want you to talk a little bit about what did you have learned?

16:16What are things come out, has a clear tradeoff, or which is better, or things we should be just, you know, watch out carefully because they all have serious pros and cons. Yeah, I thank you, and I'm happy to talk about it as part of the transparency, like I talked about before. Sure. The first thing I say is that there are really thoughtful pollsters out there doing good work across various types of methods. There are folks who are making phone calls to people, often using voter lists like I described, but there are many who are doing good work that way.

16:50It's no secret that takes longer and is often more expensive to do, but it's out there. And there are folks out there using text-to-web methods now. And by and large, and this is something we've done a lot of in more recent years, there are people who use panels. And what I mean by panels is there's a great deal of pre-recruitment in some form that goes on all throughout the year by companies and firms that establish, build, maintain these panels.

17:25And these are people in principle who have agreed to be part of the panel and take surveys on it. And what we do and what we make use of from these panels is you can pull a sample from them with known characteristics of those folks. And you can also, and we'd also do it such that you can validate someone, can confirm that they're a registered voter, confirm that they live where they live.

17:57And then they're part of these panels. And it's very convenient for them to take a survey because they get a secure link or they use an app as part of the panel. The other advantage of that is if we do want to do things like recontact or see movement, individual change over time, you can, if you wish, and we do this on occasion, go back to that same panelist and look at how they've changed or moved. And what we find is that we not only get very reliable and good data, but we also have the ability to do things very quickly.

18:37So we can turn around a representative sample in short order. Now, over time, over the years that I've been doing this, some of that I've seen change. And there were times years ago, it's quite a few years now, but we did more polling on the phone. But the big picture is the industry, like all good science, evolves and adapts. And if you think in terms of polling and you think of the frame, the platform that you use to reach people, what you want to do is always be ready and develop capabilities to reach people where they are, to reach people in ways that are convenient for them to express their opinions.

19:26And that is part of the science of it. And it's about taking advantage of new technologies in that way. You know, we can't not talk about betting and prediction markets. You know, you have Kalshi, you have Polly Market. You know, these are legal, heavily traded and increasingly cited by journalists right alongside polls. Are they a competitor to polling, a complement, or are they going to be something else entirely?

19:59And what information, if any, do the markets capture that polls miss or vice versa? I would put them in like kind of a complement and that says two sides to that. So one side to that is they're going to take basically similar information and then kind of push it. So it can like kind of take the starting point and move one way or the other. But it's also a complement in the kind of negative sense is that the weaknesses of polls, they roll right up into the into those prediction markets.

20:30Right. And so it's not it's kind of fun. It's kind of dangerous in other contexts. But I think in polling, it's it's kind of harmless in a small net value add, I'd say. But I don't think case after case when the polls miss, I don't know that we've had a case where like the betting markets have have really, you know, seen beside the scenes and seen what the polls are going to get wrong. I also think compliment the way I look at it is for those of us who study or try to gauge opinion out there, they are an expression of viewpoint for those people who are trading on the markets.

21:07We can do well to see what those folks are thinking and what they're willing to trade on. I also think you can use them or look at them to understand what people think of what's called sort of downstream effects of one race on, you know, on others. So some markets might have will a party take control of the House or the Senate and then a poll comes out or some rating comes out for one of those races that goes against that grain.

21:43Does the market for the overall Senate or House control then move, you know, attendantly? And that's a way of, again, looking at at least among traders, that kind of confidence that they're expressing in in the available information out there. One thing that the prediction markets can definitely do is they can be instantaneous. So if there's some gaffe or something, you know, or some new news leak of something, then obviously the prediction markets can respond. And that's going to take pollsters, you know, a week or so to nail that down.

22:17Polling usually is not just about who's winning, who's losing, because it's lots of things about what the vote is thinking, lots of other things. Right now, initially, when I start thinking about these marketing stuff, seems like you're going to shift attention or it become winning and losing. That seems like will be a negative, you know, distraction. You know, since you're losing lots of information or about the voters' behaviors, so on, so forth. But you just remind me, it could also be, again, in terms of at least for a group of people, seeing how they express their confidence.

22:50And, Michael, your point of something being instant, that's actually really important, right? There's a time temporal component is obviously important for all of the things. So it's just like a little bit of follow-up question on this. Like, do you guys see this particularly from a research angle? What are things like, for example, for someone like me as a statistician or for Michael that we do research? You know, are there some good thesis topic here for PhD students to work on those things? Hey, you know, this is something really quite interesting. So one is there's just a general kind of discussion of what really happens and kind of what this conversation is.

23:26I do also want to point out, and I think this just is one of, for me, at least one of the themes as I think about how polling works, is that there's margin of errors. And so you're going to see every polling firm is going to have a margin of error. And that margin of error is calculated on a scientific basis for a random sample with 100% response rate. Like, what? Like, so I call that poll washing. So that's just a number that you made up in, like, another world. And it kind of maybe sometimes, you know, has some insight or something. It's not, like, 100% irrelevant. But, boy, the scientific basis of the concept of margin of error and the practice of what we're doing is a very big gap.

24:04In fact, I mean, I end up being like, I kind of maybe come off as being, like, negative about polling. I'd say just there's all these vulnerabilities, and in spite of that, polling does pretty well. But at the same time, it just is, like, insane that they're calculating the margin of error based on these assumptions that are just, they're just not there. And or, you know, they use sample size, and the bigger the sample size, the better it is. Well, some sample sizes are terrible. In a random sampling world, the bigger sampling size is more precise. It is. It 100% is. It's a scientifically undeniable fact and a true random sample. In what we have, and this gets to, like, the quality issue, you know, like, some people have a lot of data, and it's terrible.

24:38They go, just go get it on the internet. Now, if people click to, you know, rage bait, right? Or it could be from, you know, a foreign country, or it could be bots, right? A big N means nothing, right? It's all in the, has the data been carefully collected, and just all the quality controls you have to go into that.

Communicating uncertainty and error

24:52So, like, as we characterize uncertainty, I think the default that people do, and they kind of feel like they're being sophisticated about looking at margin of error and so forth, I'm just, honestly, I'm a little wary of that, and I think it's a much harder problem. So, I think successfully communicating uncertainty starts with appreciating or understanding how a general audience might come to probability. And we know from studies, and you all, of course, know, that sometimes people take probabilities towards the end of the scale and basically push it to zero or 100.

25:31And sometimes your heart's in it, in a sense of, if one is told there's a 10% chance of something wonderful happening, you might pursue that. Oh, well, that's, you know, we get, but 10% chance of something horrible happening, and suddenly that 90% goes away, and you're thinking, oh, wait a second. And I better avoid this situation.

25:57That's what I mean by how people interpret that.

26:01And what I mean by appreciate it is, how do we find a way into the explanation given those circumstances? The other part of it is, and it's a little counterintuitive for most folks, you all are talking about margin of error and, you know, traditional computations, all that. But you're really coming at that by saying, how do we not be colossally wrong? You're not coming at that by saying, everything is going to be exactly precise all the time.

26:35What you're doing is narrowing a band of not being wrong. And that's not necessarily how most people approach things or think. And sometimes it's not satisfying for that. People see numbers, and they see, you know, we say science around it, and they think, oh, it's going to be absolutely precise. But think about how many things in life are just really good estimates. And that's one way to approach it, to say, look, it's amazing we can get this kind of an estimate down around this range

27:13and be happy about that, and think that's really cool. I think it is really cool that we can say, you know, how people feel about given issues or given topics apart from elections. And I think that when people do turn to polls, apart from this elections and things that you mentioned, I hope and I think they're looking for the fundamental answer to the fundamental question of, who are we?

27:43And what is this country and the people around me, us, thinking and doing? Which are much larger questions that can be answered by measuring public opinion and, in fact, have to be for a functioning democracy. And so that's part of the larger ethos of it, I think. And can I just add one more thing to that? I think as we're thinking about, like, the error, like, just think of Wisconsin gubernatorial, or think of the famous Iowa poll, Ann Seltzer, Iowa poll, with Harris up by eight or whatever the exact number was in the 2024 cycle.

28:20Like, those are just wrong, and I think they're far outside of the, you know, margins, you know, they're six sigma away or whatever. They're vanishingly small probability of happening under conventional depictions of margin of error. And so that then helps us, I think, put it into buckets. I don't know that I have all the right buckets, but there's the sampling error, which responds to sample size, which is, like, historically, that is the science. And that still is going to be somehow there, although I care about the quality of the sample. But then there's just the modeling error, right? And there was just assumptions.

28:51And, I mean, like, someone went and redid the Wisconsin polls, and they used different weights. And they, hey, I get the right answer. Well, like, they didn't know, ex-ante, what the right weights were. So there was just a range. And it's really hard to quantify that, right? But there, just as a source, is that they, in this case, the percent of people who said they were very liberal was too high, and they really should have had a weight target for them to be lower. But ex-ante, we don't know that, and we could have been wrong in either direction, honestly. So there's the weighting. And then, in addition, within all those subgroups, there's a non-ignorable, non-response, which is just you've got to have the proportion right of whatever the group is.

29:26And then within the group, whatever answers they gave us, they could be unrepresentative of the whole population. So there's at least three sources. And then I think this is more, I think the active pollsters are also worried about just bots and inauthentic responses, which maybe that just folds into some version of non-ignorable, non-response. But there's really a lot of sources for this, and only one of them is something that we can scientifically measure under very limited circumstances. We can and should offer people scenarios that are, apart from statistics, that are real-world political scenarios when talking about possible outcomes.

30:05So it's not to say, oh, there's a margin of error, and hey, throw up your hands, I don't know, it's somewhere in here. But you say, okay, the weight that you describe changing and getting a different answer means that if a certain group of folks do show up, even though right now you don't think they will, there's going to be a different outcome. Okay, that's a political story, and it's a real-world human behavior story, which is part of why we do this. Okay, and I've done this in the past.

30:36If young people do turn out, even though they say maybe now they're less likely, then this party is going to do better. And that's a way of taking the uncertainty and putting it into human terms, into political terms, which is a larger story that I think the public will have an easier time with, and that's just an essential part of the role.

Campaign finance and magic wands

31:02So, well, we have about 30 more questions, but we are going to wrap it up with our final question that we always end with, and that is our magic wand question. Anthony, I'm going to start with you here. If you could wave your magic wand, how much in the United States could any single candidate spend on their campaign? And that includes money raised, their own money, PAC money, everything.

31:33What's that one number that they could spend? Well, from a polling perspective, I tend to think of money spent as what are the effects of advertising? And if you look around the world, all of us, whether we're getting products or watching something, seeing a show, et cetera, how much effect does advertising have and what does it take to reach us?

32:05And there's obviously so many creative ways nowadays. It's not just seeing a commercial for this, it's targeting folks, building a profile, consumer profile of someone, pushing them something to, you know, in their interest. I get a lot of snowboarding emails and ads because they know I like to snowboard. There's all of that out there going on. So what I don't know what the number is for how much someone could or should spend, except that I am always interested in the dynamic that happens

32:45when people are either trying to be persuaded of to one side of an issue or another. That is fundamentally why we do what we do or what public opinion does and how much it shifts given certain variables and given certain inputs. So as long as that debate, discussion continues, then that is what it's all about.

33:17It's not so much the number as it is the dynamic. Michael, what are you thinking? Yeah, so it's funny, especially early in my career, I spent a lot of time thinking about campaign finance. And so I think in general, like the raising of the money can be problematic, obviously, and, you know, if it moves you towards where the contributors are. But I think the trick of a good politician is to figure out how to raise money in a place where you are also very popular. And I think we saw that, by the way, where that didn't work was there were some Democrats in the primary season who saw a lot of money coming from AIPAC.

33:51And then, oh, like you raised a lot of money. And then they just, that money was a negative, negative, I think, clearly. And so that also then highlights, I think, to say that money determines elections. Like it matters and it can matter in certain contexts and not in others or whatever. But if money determined elections, we'd have President Jeb Bush. You know, we wouldn't have President Donald Trump and others. And, you know, again and again and again, we have wealthy, self-funded people just go down in flames. So I don't think it decides elections, even though I also do appreciate that there are legitimate concerns in there.

34:23So anyway, my answer is going to be, I actually think I would instead of, I think, is it perhaps your question was phrased in terms of a maximum or maybe it wasn't. I would just say a minimum that every candidate, you know, gets to spend $10 per constituent on, you know, whatever it is, advertising or staff or whatever. And then that actually, then that means in every district, both sides get to tell their story. And I don't, you know, I think, honestly, you're a Democrat and you're in a heavily white, rural Mississippi area, no amount of money is going to win you that election, right?

34:53But still, you get to tell your story. And I don't think that's bad for democracy. And then in some of these competitive places, honestly, the big winners are just the candidates themselves that don't have to spend all their time in a cubicle making calls, right? But I actually almost would define this more as a minimum than a maximum.

35:12Thank you all for listening. And if you'd like to continue the conversation in person, Anthony Salvato and Michael Bailey will both be speaking at the Political Analytics Conference 2026, taking place October 8th and 9th in Washington, D.C. To stay updated with all things HDSR, you can visit our website at hdsr.mitpress.mit.edu or follow us on Twitter and Instagram at the HDSR. A special thanks to our executive producer, Rebecca McLeod, and producers, Tina Tobey-Mack and Erin Kieswetter.

35:44If you liked this episode, please leave us a review on Spotify, Apple, or wherever you get your podcasts. This has been the Harvard Data Science Review. Everything data science and data science for everyone. Thank you. Thank you.