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Speaker 1: Y'all keep sending me your Instagram polls and saying who

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Speaker 1: likes candy corn? I love candy corn. Do you like

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Speaker 1: candy corn? No? What it's like? Wax? What are you

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Speaker 1: talking about? Candy corn has such a great flavor. I

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Speaker 1: also saw a pole on Twitter where somebody was saying

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Speaker 1: what's the most trash candy? And everybody was saying, you

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Speaker 1: gotta get Milky Way out of there. I like Milky Way.

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Speaker 1: Something's wrong with everybody. Milky Way is a very good candy.

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Speaker 1: My favorite candy Three Musketeers. That's good too. I like

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Speaker 1: a good nugut? Is that? What's in there? Nowgat you

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Speaker 1: throw those things in a freezer for just a little bit. Ooh,

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Speaker 1: I've never tried that. Hey, stuff you gave up? I'm

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Speaker 1: going to I'm t T and I'm Zakiah and from

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Speaker 1: Spotify Studios. This is dope laps. This happens every year

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Speaker 1: around Halloween. As soon as October hits in that Halloween

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Speaker 1: candy hits the market, everybody becomes the great debater about

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Speaker 1: the best Halloween candy. Full size, family size, funny size,

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Speaker 1: all these things like that? Are y'all giving out full

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Speaker 1: size candy bars? On Halloween. We're not not in my house.

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Speaker 1: I saw a thing on Twitter where somebody has given

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Speaker 1: out rotessery chickens. Ha ha ha. First of all, you're bawling.

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Speaker 1: I don't care what grocery store you're going to to

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Speaker 1: give out rotessity chickens. That's crazy. I'm having three guests

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Speaker 1: and that's it. Three tricker treaders and good night. Right,

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Speaker 1: But it's so interesting to me that people will do

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Speaker 1: these polls and you know, your little ten followers will answer.

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Speaker 1: I'm only saying that because I got about seventeen, but

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Speaker 1: your ten followers will answer. And then people are like,

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Speaker 1: it's official, Milkie Way is out of here, and I'm like,

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Speaker 1: based off of ten people, your ten friends is that.

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Speaker 1: That's not how polls work. That's not how it works.

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Speaker 1: You need more people. We don't believe. We don't believe you.

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Speaker 1: You need more people. And that leads us right into

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Speaker 1: today's topic, polling. There's a lot of polls out there,

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Speaker 1: and we see them all the time. Sometimes you're not

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Speaker 1: even aware that you are looking at poll data and

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Speaker 1: it's right in front of your face. A lot of

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Speaker 1: y'all are sharing memes. You got bar charts where you

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Speaker 1: should have pie charts and scatterplots when you need a

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Speaker 1: line plot. And I want to say, hey, that's not right.

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Speaker 1: But today we want to bring you the facts. We're

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Speaker 1: going to tell you everything we know about polling. We're

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Speaker 1: going to ask all of our questions about polling, because

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Speaker 1: I mean, the election season is upon us. Bernie Sanders

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Speaker 1: got like twenty five million in donations, Elizabeth Warren got

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Speaker 1: like twenty one million in donations. They're taking that money

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Speaker 1: and they're gearing up. And what they're gonna start doing

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Speaker 1: is they're gonna start using that money to not only

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Speaker 1: put out like campaign ads and things like that, but

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Speaker 1: they're also buying results from survey firms. And they're going

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Speaker 1: to start to look at this data about all of

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Speaker 1: us and put it out there and they're going to

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Speaker 1: say this, many percent of Americans do this, and this

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Speaker 1: is why you should vote for me. So I just

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Speaker 1: saw the latest Cricket Media poll. Have you seen this

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Speaker 1: when it has Bernie Sanders and Elizabeth Warren as the

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Speaker 1: top candidates and it says here are the top five

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Speaker 1: candidates after the first debate. But if you zoom in,

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Speaker 1: pinch to zoom, that is so tiny. Yeah, it says

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Speaker 1: a poll of Democratic voters in Iowa, New Hampshire, and

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Speaker 1: South Carolina. And so sometimes people share these polls and

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Speaker 1: it's like, this is what Americans think, and I'm like,

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Speaker 1: this is from three states, and it's the subgroup of

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Speaker 1: a subgroup. You even ask folks from the biggest states

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Speaker 1: or the most populated states. Nobody in my family lives

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Speaker 1: in any of these states. And so what we're trying

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Speaker 1: to do with this episode is give you all the

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Speaker 1: necessary information that you need to be able to look

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Speaker 1: at some of this stuff and say, oh, that's not

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Speaker 1: what that pole meant and find that the real dat

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Speaker 1: of yourself. Yeah, And if you're like me, you're wondering

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Speaker 1: who are those people getting surveyed? We also have a

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Speaker 1: tip about how you can make sure your voice is hurt.

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Speaker 1: So let's get into the rescitation. I have questions about

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Speaker 1: that because I see a lot of poles being shared

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Speaker 1: on Instagram and on Twitter without any of that qualifying information.

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Speaker 1: Where did you get these people from? How many people

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Speaker 1: did you survey? When did you survey them? And even

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Speaker 1: if the information is there, you see how tiny it is,

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Speaker 1: and when you just scroll past it and you're like, Okay,

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Speaker 1: Sanders A. Warren, I thought it was a shadow box, right,

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Speaker 1: I thot just a line. I thought it was a

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Speaker 1: line that separate this from the next paragraph divider. So

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Speaker 1: I think, like, I have so many questions, like can

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Speaker 1: just anybody issue a poll and put this information out?

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Speaker 1: Are there certain places we should go when we're looking

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Speaker 1: for polling down or like when I want to know

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Speaker 1: what's the latest trend? You know, where do you go

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Speaker 1: for reliable information? Yeah? And then if I'm seeing information,

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Speaker 1: like if I'm seeing a bar graph and it says like, Okay,

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Speaker 1: these are the top candidates, blah blah blah, how do

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Speaker 1: I make sense of that? Like? What else should I

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Speaker 1: look for to say that this is reliable information? What

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Speaker 1: am my cue? Right? Which polls should I take with

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Speaker 1: a grain of salt? And which polls should I drink

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Speaker 1: with salt water? Okay, So now that we know what

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Speaker 1: we want to know, let's get into the dissection. This week,

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Speaker 1: we are jumping into polls, as you know, and we

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Speaker 1: ask doctor Rachel Bidekoffer to help us out.

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Speaker 2: I'm the assistant director of the Watson Center for Public

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Speaker 2: Policy at Christopher Newport University and a senior research fellow

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Speaker 2: at then a Scannon Center in DC.

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Speaker 1: Whenever we bring up polls, everybody jumps right to thinking

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Speaker 1: about elections. But we know that polls can be about anything.

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Speaker 2: Not all polling is about elections. For sure. You get

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Speaker 2: polling about public policy issues. You can get polling that

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Speaker 2: about market research issues. I mean, companies will use polling

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Speaker 2: to determine product placement or you know, the types of

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Speaker 2: advertising they want to use for their products. Polling is

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Speaker 2: used in cross every sector.

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Speaker 1: We're constantly polling each other every day. Like anytime somebody

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Speaker 1: puts out anything, people are I don't like that, like

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Speaker 1: I love it, you know. So it's like when you

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Speaker 1: look in the comments section of like a post on

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Speaker 1: the Shade Room or TMZ or anything like that. I

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Speaker 1: sometimes I'll just scroll through them and like, Ooh, this

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Speaker 1: person don't like it. That's one. This person like it,

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Speaker 1: that's one. Then going back and forth and seeing like

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Speaker 1: a running pole in my mind to see like where

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Speaker 1: people are falling on it, you know, another pole that

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Speaker 1: just popped into my head. It's always in these toothpaste

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Speaker 1: commercials where it's like nine out of ten dentists choose

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Speaker 1: Colgate and I'm like, compared to what else? Right, there's

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Speaker 1: always an asterisk and then it says like standard toothpaste.

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Speaker 1: I'm like, I don't know what standard toothpaste is? And

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Speaker 1: did y'all really survey all the dentists? How many dentists?

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Speaker 1: Is that there's a lot of different types of polls.

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Speaker 1: I think the one that we are most used to

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Speaker 1: seeing as an election poll, and that's what doctor Bittercofford

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Speaker 1: calls a horse race.

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Speaker 2: Horse race is just an election poll. So a poll

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Speaker 2: in which you're trying to determine who's up and who's down,

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Speaker 2: or whether or not an election is going to be competitive.

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Speaker 2: I wouldn't say there's necessary categories, but different types of

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Speaker 2: questions and approaches that election polling might cover, depending on

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Speaker 2: the goal of the survey.

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Speaker 1: I always see polls that show like people think this,

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Speaker 1: or candidates are trending this way, they're doing well with mothers,

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Speaker 1: they're doing well with veterans, Like huh, how do you

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Speaker 1: get down to the actual sample or people that you

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Speaker 1: want to pull?

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Speaker 2: So the way that sampling works in a population is

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Speaker 2: that you're taking a random sample of a population and

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Speaker 2: through a process called waiting, it does tell you within

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Speaker 2: a certain window what the preferences of that broader population is.

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Speaker 1: So, if I want to say everybody that eats candy

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Speaker 1: in America, Okay, we know that that's two million people.

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Speaker 1: I know that's not right. But like, we know that's

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Speaker 1: two million people, So how many people do I need

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Speaker 1: to survey? Right? That becomes your survey sample size in

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Speaker 1: order for it to be an accurate representation of that

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Speaker 1: entire population of candy eaters, right, Because you're not going

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Speaker 1: to get all two million people to participate, right. And

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Speaker 1: even if you say, okay, I need to ask a

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Speaker 1: thousand people in order to understand what two million people think,

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Speaker 1: you're not going to get all of those one thousand

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Speaker 1: people to participate. So what she's talking about with waiting

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Speaker 1: is creating a poll that has the same demographic of

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Speaker 1: the folks that you are trying to represent.

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Speaker 2: There are some survey firms that are still using what

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Speaker 2: we call random digit dialing for voter surveys, and I

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Speaker 2: would argue that that's not an ideal way because voters,

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Speaker 2: as it turns out, when you call people and ask

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Speaker 2: them are you registered to vote? They will lie to

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Speaker 2: you and tell you yes. Because it's socially desirable to

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Speaker 2: be registered to vote, and they don't want to admit

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Speaker 2: that they're not registered to vote.

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Speaker 1: And so when I think about that, you know, you

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Speaker 1: say candy corn is great, And when we think about

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Speaker 1: the population of people eating candy, we might say, there

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Speaker 1: are people who eat candy who don't like chocolate, people

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Speaker 1: who eat candy who love chocolate, people who eat candy

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Speaker 1: that are allergic to nuts, right, and all those things.

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Speaker 1: We will want to capture that in our We wouldn't

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Speaker 1: want to only get people that are allergic to nuts,

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Speaker 1: because then that would make that kind of that group

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Speaker 1: of people. It would keewe the results because then you

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Speaker 1: would only get from their perspective, and so sneakers will

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Speaker 1: be out of here then exactly because they can't them exactly.

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Speaker 1: So basically, what we're trying to get you to see

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Speaker 1: is you have to do your research on your population

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Speaker 1: to make sure you're finding ways to ensure the credibility

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Speaker 1: of your sample. So you'll see the phrase likely voters

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Speaker 1: in a lot of polls, and more people claim to

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Speaker 1: vote than actually go out and vote when the time comes,

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Speaker 1: And so part of the job of the pollsters is

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Speaker 1: to try and find those people who will actually go

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Speaker 1: out and vote, as opposed to the people who will

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Speaker 1: just say they're voting but stay at home. Oh. So

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Speaker 1: this also kind of ties into the last episode of

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Speaker 1: social cognition, like how do people perceive you? Exactly? So

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Speaker 1: if somebody's polling you, you're like, what are they going

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Speaker 1: to think of me? And it may change the response

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Speaker 1: that you get. Yeah, so you'll say, oh, I'll definitely

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Speaker 1: vote with which is a con MM. And that's why

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Speaker 1: I don't like exit polls. Oh, because I'm like, everybody's

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Speaker 1: out here line they're like, oh, well, the exit poll

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Speaker 1: because even on the day of the election, they're basing

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Speaker 1: a lot of stuff off of exit polls. So people

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Speaker 1: will go in and they'll cast their ballot, and they'll

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Speaker 1: come out and they say, who did you vote for

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Speaker 1: A or B? And somebody might say A because they

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Speaker 1: feel like, oh, well, in my county, everybody's voting for A,

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Speaker 1: and I don't want to come out and say I

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Speaker 1: voted for B. That might be embarrassing. Somebody may hear me,

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Speaker 1: they may judge me, and so then they'll change the answer.

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Speaker 1: Another con So, now that kind of captures the essence

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Speaker 1: of deciding who you will pull and getting your sample,

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Speaker 1: But what about what you'll ask them? That's very important.

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Speaker 1: The first thing you can do when designing a survey

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Speaker 1: is to make sure that your questions aren't leading.

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Speaker 2: You really want to have questions that are worded in

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Speaker 2: a way in which you're getting responses that are a

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Speaker 2: product of the questions.

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Speaker 1: So, for example, if you say, do y'all love my outfit?

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Speaker 1: Yes or no? That's leading. You're leading it with we

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Speaker 1: should love the outfit. Are y'all going to vote for

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Speaker 1: this idiot? Yes or no? Right, that's not a good

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Speaker 1: survey question. So when you create questions like this, you're

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Speaker 1: basically generating what's called a push pull. This is something

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Speaker 1: that pushes the respondent to answer in one way as

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Speaker 1: opposed to the other. So her next suggestion is to

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Speaker 1: not provide an easy out answer. So that's something that's

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Speaker 1: like if you have a bunch of different you have

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Speaker 1: a question, you have a bunch of different answers, and

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Speaker 1: it ranges on a scale from agreed to disagree. Having

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Speaker 1: something in the very center that says neither agreed nor disagree.

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Speaker 1: People are more likely to choose that because they don't

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Speaker 1: want to do anything that's, like I guess, controversial in

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Speaker 1: their mind. So they just want to ride that middle line.

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Speaker 1: That's crazy to me. I love to be at the extremes.

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Speaker 1: We know my answers always strongly agree or strongly disagree.

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Speaker 1: I hate it, loved it. This is the best thing

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Speaker 1: I've ever seen. I was just having this conversation with

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Speaker 1: my friend and I was like, I thought you said

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Speaker 1: you hated that. She said, when I say I hate something,

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Speaker 1: I don't really hate it or I don't really love it.

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Speaker 1: And I said, huh, that's you. You do that too,

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Speaker 1: I don't really hate or really love. So you'll say

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Speaker 1: I hate this and then be like, oh, I guess

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Speaker 1: it's not that bad. That's just uninformed. I don't know

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Speaker 1: any I can't think of a single example where I

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Speaker 1: said I hated something and then I said, I guess

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Speaker 1: it's not that bad. I can think of situations where

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Speaker 1: you've loved stuff and then hated it. Wait, are these

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Speaker 1: people people? I just did not love? That a point?

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Speaker 1: Now love to hate? Yes, that's an easy progression. I

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Speaker 1: start everyone to get from love to you are a ghost.

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Speaker 1: Sorry to that man, Sorry to this man who who

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Speaker 1: a third tip that doctor Bitterkoffer gave us when it

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Speaker 1: comes to designing good surveys, to rotate your answers so

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Speaker 1: each person taking the survey will see the answers in

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Speaker 1: a different order.

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Speaker 2: Because, believe it or not, people will go with the

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Speaker 2: first response disproportionately. So if you have, say you have

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Speaker 2: a horse race question, and you have the one candidate

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Speaker 2: offered always first, you might not get an accurate estimation

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Speaker 2: on the race because people will choose the first candidate

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Speaker 2: more often just because it's mentioned first.

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Speaker 1: That reminds me of like in the SAT when they

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Speaker 1: were like, oh, always choose C. Can remember scantrons and stuff,

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Speaker 1: Gantron's everything, and people with default to C because they

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Speaker 1: were like, oh, when they're making these exams, you're more

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Speaker 1: likely to get the right answer if you choose C

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Speaker 1: because of whatever reason. I don't know if that's how

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Speaker 1: the computer would do it or whatever. And so people

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Speaker 1: got hip and then they started rotating the answers. Another

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Speaker 1: reason to get these standardized tests out of here. Yeah.

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Speaker 1: The last tip we got is to make sure your

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Speaker 1: survey is short and to the point. I don't want

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Speaker 1: to read this paragraph right, So if it's long, I'm

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Speaker 1: probably won't even participate, and so the shorter the survey

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Speaker 1: the better. So those are our top pointers for designing

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Speaker 1: a survey. You may be asking why is designing good

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Speaker 1: questions so important? All of these factors contribute to a

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Speaker 1: higher response rate. Response rate refers to the percentage of

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Speaker 1: people that you are trying to survey that actually complete

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Speaker 1: the survey.

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Speaker 2: And over the last few decades, especially as people have

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Speaker 2: become more technologically diverse, response rates have been declined.

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Speaker 1: That's a good point TT because if you don't have

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Speaker 1: if you have long questions, if people don't want to participate,

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Speaker 1: that affects your response rate. And so all that work

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Speaker 1: you did trying to figure out what's the right sample

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Speaker 1: size and number of people, you won't get enough respondents.

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Speaker 1: And that's why you know now when you call your

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Speaker 1: cell phone provider and they want to do a quick survey,

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Speaker 1: at the end of the call, they're like, it's just

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Speaker 1: one question, like hanging there. They're starting to catch on.

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Speaker 1: It's like, I'm not going to talk on the phone

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Speaker 1: to a computer for you know, thirty minutes answering all

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Speaker 1: these questions, like on a scale of one to ten,

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Speaker 1: what was your experience? No ask me one question was

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Speaker 1: the experience good or not? So now we know how

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Speaker 1: to get the right population for a survey and how

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Speaker 1: to design good questions to ask them. When we come back,

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Speaker 1: we're going to talk about making sense of the results.

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Speaker 1: We're back, and we're going to get into how to

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Speaker 1: we through all of these polls and find the right information.

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Speaker 1: Right there's so many polls out there. Every week, I

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Speaker 1: feel like there's a new article telling me about the

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Speaker 1: latest updates or who's ahead, who's dropping out, who's face.

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Speaker 1: I don't know how to make sense of it. And

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Speaker 1: as someone who watches academic polls, Twitter polls, political polls,

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Speaker 1: and all of y'all's ig polls, how do I know

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Speaker 1: whether or not they're any good?

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Speaker 2: When a poll comes across the TV and you know,

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Speaker 2: the journalist is like, oh, forty five percent of Americans

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Speaker 2: love lamas, you know, not to just take that as

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Speaker 2: a face value, but to go and look at the

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Speaker 2: survey and say, okay, how is the sample taken?

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Speaker 1: I love lamas? You do? You should not go of Americans.

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Speaker 1: That seems a little low. That seems a little low

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Speaker 1: to me. But I'm not gonna dwell on that too much.

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Speaker 1: Let's let's just keep going.

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Speaker 2: So the very first thing that I look at when

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Speaker 2: a survey comes out is that end size really need

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Speaker 2: a baseline of at least five hundred completes for a

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Speaker 2: survey to be reliable statistically.

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Speaker 1: So in size is the number of people that complete

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Speaker 1: the survey. Right. In size is important for all surveys.

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Speaker 1: The larger en size, the more reliable the outcome of

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Speaker 1: the survey is.

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Speaker 2: And there are many surveys that have gotten you know,

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Speaker 2: primo attention on MSNBC and CNN this cycle that are

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Speaker 2: nowhere close to five hundred. Technically speaking, of course, you

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Speaker 2: can get a statistical response off of a lower end size,

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Speaker 2: but in terms of horse race pulling, there's this statistical

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Speaker 2: problem called the margin of era that's exasperated a lot

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Speaker 2: by a small in size study.

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Speaker 1: You really have issues with margin of error when you

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Speaker 1: have a small sample size. In the field that I

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Speaker 1: work in, we call that uncertainty. And so it's basically

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Speaker 1: saying that when you take a measurement, so let's say

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Speaker 1: it's something you're trying to weigh ten pounds, and if

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Speaker 1: you have uncertainty that's plus or minus five pounds. That's

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Speaker 1: a large range. That means that when you're weighing this,

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Speaker 1: it can either be five pounds or or anywhere between

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Speaker 1: five pounds and fifteen pounds, so you'reself subtracting five or

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Speaker 1: adding five on both exactly. So that means that you

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Speaker 1: have don't have a high confidence in the measurement that

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Speaker 1: you're that you're taking, but you can also have a

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Speaker 1: smaller margin of era though, right, Yeah, So if the

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Speaker 1: measurement that you're taking, you're really you have a high

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Speaker 1: confidence in it to a higher confidence level, your margin

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Speaker 1: of error could be really really small, like a pound.

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Speaker 1: So you can say this thing is ten pounds plus

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Speaker 1: minus one pounds, so that means it could be nine

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Speaker 1: pounds per eleven pounds. And that's really cool to think

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Speaker 1: about it, like the way it works in your field,

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Speaker 1: because that's really similar to how it kind of works

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Speaker 1: for polling. Right, you can say thirty percent of moms

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Speaker 1: choose gift, and you can say, with a five percent error,

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Speaker 1: twenty five anywhere from twenty five to thirty five percent

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Speaker 1: of moms choose gift, and you can get that, right.

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Speaker 1: You can get to that type of resolution with a

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Speaker 1: small sample size. But if you want to be able

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Speaker 1: to say thirty percent of moms choose Gift, and you

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Speaker 1: mean twenty nine to thirty one percent of moms choose

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Speaker 1: You're gonna have to sample way more, way more moms, yeah,

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Speaker 1: to get that type of accuracy. And so you're saying that,

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Speaker 1: so margin of RAA is just how uncertain are you

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Speaker 1: about this statistic? And then the other part of that

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Speaker 1: is your confidence level. And so I think confidence level

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Speaker 1: is really cool because that also has to do with

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Speaker 1: the number of people that you survey. But I don't

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Speaker 1: know what they might call this in your field, but

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Speaker 1: confidence level is like thirty percent of moms choose JIFF,

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Speaker 1: And if I'm ninety five percent confident, then that means

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Speaker 1: that ninety five out of one hundred times that I

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Speaker 1: do this survey, thirty percent of moms will choose JIFT.

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Speaker 1: Or if I have a ninety nine percent confidence level,

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Speaker 1: then that means that ninety nine times if I do

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Speaker 1: this survey one hundred times, ninety nine of those times,

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Speaker 1: thirty percent of moms are gonna choose JIFT. Jeff, is

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Speaker 1: really you? I thought you were allergic allergic to peede us.

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Speaker 1: I told you it's a mild allergy. I still eat it.

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Speaker 1: It's not safe. I don't recommend that for anybody that

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Speaker 1: has mild or extreme peanut allergy. Go to your doctor. So,

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Speaker 1: as consumers of poll data, we have to go and

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Speaker 1: ask some real questions about how the data was collected.

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Speaker 1: One of the things is did the survey cast a

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Speaker 1: wide net like that survey I showed you earlier. Yeah,

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Speaker 1: like the survey we were talking that we were talking about.

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Speaker 1: They only surveyed people in Indiana, South Carolina. Yeah, three states. Yeah,

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Speaker 1: and they're making very like sweeping claims, yeah, that we're

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Speaker 1: all consuming. It's like, oh my gosh, Okay, so Bernie

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Speaker 1: Sanders and Lizbond are tied at nineteen percent and that

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Speaker 1: might not necessarily be true everywhere. And then the other

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Speaker 1: question is how did they even get the information? Are

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Speaker 1: people taking these surveys on the phone, you know those

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Speaker 1: people that are standing outside with clipboards at different places.

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Speaker 1: Are they sprint past them? Are they doing the surveys

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Speaker 1: like that like sorry, I have diarrhea, like I have

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Speaker 1: to go? Or are they doing them online?

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Speaker 2: If it's online, have they done due diligence to try

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Speaker 2: to make their panels randomized as much as possible. Are

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Speaker 2: they being transparent about their methodology and the design effect.

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Speaker 1: Anytime you see a poll, there should be something at

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Speaker 1: the bottom of it that tells you where you can

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Speaker 1: go to find more information about that poll, or tell

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Speaker 1: you a little bit more about it. Yeah, show me

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Speaker 1: the link and click the link like this is important.

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Speaker 1: If you're reading a poll and you're feeling swayed by it,

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Speaker 1: you should want to know more information about it.

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Speaker 2: If you're a smart connoisseur, you can look at the

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Speaker 2: data and understand. Okay, you know, I might not be

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Speaker 2: able to take this to the bank. I probably can't

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Speaker 2: say for certain candidate A has forty five percent of

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Speaker 2: the vote. But I can say that candidate A is

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Speaker 2: somewhere near forty five percent of the vote. And if

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Speaker 2: candidate B is at forty and candidate A is at

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Speaker 2: forty five and the margin of era is three points,

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Speaker 2: I can say with some statistical confidence that candidate A

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Speaker 2: is indeed leading candidate B.

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Speaker 1: So now that we know all that we should consider

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Speaker 1: when looking at poll data, let's take this information and

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Speaker 1: apply it retroactively. Right, what happened in twenty sixteen? Yes,

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Speaker 1: because all of those poles are wrong, I looked at

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Speaker 1: a lot of polls, and all those polls told me

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Speaker 1: one thing, and it didn't turn out that way. So

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Speaker 1: what happened.

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Speaker 2: What people usually are talking about when they talk about

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Speaker 2: the polling being off in twenty sixteen is they're talking

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Speaker 2: about the aggregated forecasting models, like the stuff that came

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Speaker 2: out from five point thirty eight that said Clinton was

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Speaker 2: going to win you know, seventy thirty, right, And those

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Speaker 2: are distinct from polls because forecasting models produce probabilities of

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Speaker 2: you know, one candidate winning over the other.

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Speaker 1: Right. So forecast is basically like, feed me all your

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Speaker 1: polling data, and I'm going to predict what might happen

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00:22:58,359 --> 00:23:01,920
Speaker 1: based on multiple different poles. But if the polling data

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00:23:01,920 --> 00:23:04,080
Speaker 1: is no good, then the forecast is no good because

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Speaker 1: that's what it relies on.

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Speaker 2: So one of the reasons that the forecasting models was

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00:23:08,880 --> 00:23:12,960
Speaker 2: so off is that there was a clear signal coming

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00:23:13,040 --> 00:23:17,240
Speaker 2: from the polling data that everybody missed, and that was

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00:23:17,880 --> 00:23:21,840
Speaker 2: that we had a very high number of voters double

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00:23:22,119 --> 00:23:26,120
Speaker 2: sometimes triple the amount that we're saying they were undecided

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00:23:26,200 --> 00:23:29,919
Speaker 2: or don't know in their vote choice running up to

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00:23:29,960 --> 00:23:33,840
Speaker 2: the election. Even a week before the election, that is

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00:23:33,920 --> 00:23:37,320
Speaker 2: double the amount of normal voters for a presidential election.

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00:23:37,840 --> 00:23:41,159
Speaker 2: And what the narrative should have been in terms of

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00:23:41,200 --> 00:23:45,000
Speaker 2: the polling was given this amount of uncertainty and this

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00:23:45,520 --> 00:23:49,359
Speaker 2: unsettleness in the polling data, we really have no idea

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00:23:49,440 --> 00:23:51,080
Speaker 2: what's going to happen on election day.

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00:23:51,200 --> 00:23:54,639
Speaker 1: All right, But see now that everybody's listened to the episode,

448
00:23:55,320 --> 00:24:00,280
Speaker 1: you already know the threat to the soundness of your

449
00:24:00,320 --> 00:24:04,199
Speaker 1: poll when you have that non committal option there. So

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00:24:04,240 --> 00:24:07,800
Speaker 1: when you have a large number of respondents saying neither

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00:24:07,800 --> 00:24:11,600
Speaker 1: here nor there, unsure, you probably can't trust that information.

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00:24:12,160 --> 00:24:16,200
Speaker 2: In twenty twenty, Democratic voters, people who want to see

453
00:24:16,200 --> 00:24:19,000
Speaker 2: Trump lose, are never going to believe any of the polling.

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00:24:19,440 --> 00:24:23,240
Speaker 2: So you know, if twenty sixteen was about comfort, twenty

455
00:24:23,320 --> 00:24:26,240
Speaker 2: twenty is going to be about distrust of that polling data.

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00:24:26,280 --> 00:24:30,080
Speaker 2: And that's actually to the country's advantage because it's going

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00:24:30,119 --> 00:24:32,520
Speaker 2: to make people show up no matter what the data says.

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00:24:32,960 --> 00:24:34,920
Speaker 2: But yeah, I mean, we're just living at a time

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00:24:34,960 --> 00:24:38,720
Speaker 2: period where people are deeply distrustful of everything, and you

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00:24:38,760 --> 00:24:42,439
Speaker 2: know that distrust is starting to hamper our ability to

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00:24:42,480 --> 00:24:43,480
Speaker 2: function as a nation.

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00:24:43,800 --> 00:24:45,960
Speaker 1: I think I'm ready. I was just about to say

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00:24:46,000 --> 00:24:50,280
Speaker 1: that girl, I was like, I am red d Okay.

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00:24:50,359 --> 00:24:53,159
Speaker 1: I'm not going to just read these headlines and then

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00:24:53,280 --> 00:24:56,320
Speaker 1: run with it. I'm going to be doing a lot

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00:24:56,400 --> 00:24:59,600
Speaker 1: deeper dives into all of these surveys because they are

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00:24:59,640 --> 00:25:03,720
Speaker 1: about to be coming at us like rapid fire. Yes,

468
00:25:03,880 --> 00:25:07,000
Speaker 1: And I'm already telling you. If I follow you and

469
00:25:07,119 --> 00:25:09,200
Speaker 1: you post the results of a pole, I'm gonna ask

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00:25:09,240 --> 00:25:12,280
Speaker 1: some questions. I'm gonna say, send a link. How many

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00:25:12,440 --> 00:25:15,440
Speaker 1: people did you survey? I'm saying, even if you're posting

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00:25:15,680 --> 00:25:17,879
Speaker 1: thirty percent of people said I should where does outfit?

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00:25:18,359 --> 00:25:20,440
Speaker 1: Is that three out of ten or is that thirty

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00:25:20,480 --> 00:25:23,159
Speaker 1: out of one hundred? Even on your Instagram surveys. So

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00:25:23,200 --> 00:25:26,440
Speaker 1: if you're posting a pole on Instagram and you're like, oh,

476
00:25:26,760 --> 00:25:29,280
Speaker 1: what shoes should I buy? This red shoe or this

477
00:25:29,320 --> 00:25:33,200
Speaker 1: black shoe? And you buy the red shoe because seventy

478
00:25:33,240 --> 00:25:35,800
Speaker 1: five percent of people said that you should buy the

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00:25:35,800 --> 00:25:38,680
Speaker 1: red shoe. But then we find out that only four

480
00:25:38,680 --> 00:25:42,480
Speaker 1: people participated in your Instagram pole, you probably shouldn't have

481
00:25:42,480 --> 00:25:45,400
Speaker 1: bought either shoe. Go return it. I hope you kept

482
00:25:45,480 --> 00:25:49,840
Speaker 1: your received all right. So this week we are finally

483
00:25:49,840 --> 00:25:51,440
Speaker 1: going to get to the bottom of it. Dope Labs

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00:25:51,480 --> 00:25:55,720
Speaker 1: Podcast wants to know candy Corn in or out? Candy

485
00:25:55,720 --> 00:26:00,600
Speaker 1: Corn is so in? Nope, you are? I know there. Hey, hey, y'all,

486
00:26:00,680 --> 00:26:02,600
Speaker 1: I know my friends out that it's trying to carry

487
00:26:02,600 --> 00:26:09,000
Speaker 1: it on. Hey, I got three bags already. Oh. I

488
00:26:09,080 --> 00:26:12,359
Speaker 1: feel sorry for the kids in your neighborhood. These kids,

489
00:26:12,440 --> 00:26:16,480
Speaker 1: if they're smart, love candy corn. Smart people love candy corn.

490
00:26:16,480 --> 00:26:19,480
Speaker 1: I'm starting a campaign. Oh my god, We're gonna post

491
00:26:19,480 --> 00:26:21,840
Speaker 1: a poll on Instagram. So y'all go and vote candy

492
00:26:21,880 --> 00:26:40,000
Speaker 1: corn yes or yes no, don't forget to check out

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00:26:40,000 --> 00:26:42,879
Speaker 1: our website for the cheat sheet on today's episode. You

494
00:26:42,880 --> 00:26:44,919
Speaker 1: can find it and sign up for our newsletter at

495
00:26:44,920 --> 00:26:48,800
Speaker 1: Dope Labs podcast dot com. Also, we love hearing from you.

496
00:26:49,240 --> 00:26:51,480
Speaker 1: What do you think about today's lab? What are your

497
00:26:51,480 --> 00:26:54,720
Speaker 1: ideas for future labs? Our number is two zero two

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00:26:54,880 --> 00:26:57,800
Speaker 1: five six seven seven zero two eight. You can also

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00:26:57,880 --> 00:27:00,720
Speaker 1: find us on Twitter and Instagram at Dope Labs Podcast.

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00:27:01,320 --> 00:27:03,840
Speaker 1: T T is on Twitter at d R Underscore t

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00:27:04,080 --> 00:27:06,840
Speaker 1: s h O, and you can find Zakia at z

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00:27:07,200 --> 00:27:10,520
Speaker 1: Said So follow us on Spotify or wherever you listen

503
00:27:10,560 --> 00:27:14,440
Speaker 1: to podcasts. Special thanks to today's guest doctor Rachel Bittercoffer.

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00:27:14,960 --> 00:27:17,480
Speaker 1: You can find her on Twitter at Rachel Bittercoffer. That's

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00:27:17,600 --> 00:27:20,640
Speaker 1: b I t E c O f e R. Doctor

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00:27:20,640 --> 00:27:23,320
Speaker 1: Bittercoffer also suggested some links for anyone who's interested in

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00:27:23,400 --> 00:27:26,000
Speaker 1: learning more about survey methodology and polling, and you can

508
00:27:26,000 --> 00:27:28,760
Speaker 1: find those in our show notes. Our producer is Jenny

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00:27:28,800 --> 00:27:32,399
Speaker 1: Radlet Mass of Wave Runner Studios. Mixing and sound design

510
00:27:32,480 --> 00:27:36,120
Speaker 1: by Hannis Brown and special thanks to Tyler Adams. Original

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00:27:36,160 --> 00:27:39,840
Speaker 1: theme music is by Taka Yasuzawa and Alex Sugi Eurra,

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00:27:40,359 --> 00:27:44,359
Speaker 1: with additional music by Elijah Alex Harvey. Dope Labs is

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00:27:44,359 --> 00:27:47,200
Speaker 1: a production of Spotify Studios and Mega Own Media Group,

514
00:27:47,400 --> 00:27:50,480
Speaker 1: and its executive produced by us T. T. Shadia and

515
00:27:50,560 --> 00:27:57,280
Speaker 1: Zakiah Wattley. Doctor Bittercoffer said that a lot of these

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00:27:57,280 --> 00:28:02,040
Speaker 1: surveys they're calling landline because you're not allowed legally to

517
00:28:02,400 --> 00:28:06,840
Speaker 1: robocall cell phones. First of all, who has a landline? Well,

518
00:28:06,880 --> 00:28:10,760
Speaker 1: in twenty nineteen, no one. Do your parents have a landline? Yeah,

519
00:28:10,760 --> 00:28:13,199
Speaker 1: but they're not answering it mine either, And when they do,

520
00:28:13,280 --> 00:28:17,480
Speaker 1: it sounds like they're answering it from the depths of

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00:28:17,520 --> 00:28:19,960
Speaker 1: the earth, beyond the beyond yeah, I'm like hello,