1 00:00:00,160 --> 00:00:03,440 Speaker 1: Mid terms are right around the corner, just ahead in November, 2 00:00:03,800 --> 00:00:07,080 Speaker 1: consequential mid term election, probably one of the most important 3 00:00:07,080 --> 00:00:09,960 Speaker 1: of her lifetimes, I think, given the state of the country, 4 00:00:10,000 --> 00:00:12,039 Speaker 1: everything that's going on in the country. So we're going 5 00:00:12,080 --> 00:00:14,920 Speaker 1: to turn to a pollster who has actually gotten things right, 6 00:00:15,040 --> 00:00:17,560 Speaker 1: who doesn't inject bias. He just tries to get to 7 00:00:17,600 --> 00:00:19,639 Speaker 1: the truth. He tries to get to the answers. He 8 00:00:19,680 --> 00:00:22,440 Speaker 1: tries to reflect reality at a time when a lot 9 00:00:22,480 --> 00:00:25,040 Speaker 1: of pollsters are showing their bias. Right, they inject their 10 00:00:25,040 --> 00:00:27,720 Speaker 1: own bias into the outcomes and what they think the 11 00:00:27,760 --> 00:00:30,639 Speaker 1: electorate is going to look like. That pollster, he's the 12 00:00:30,720 --> 00:00:33,720 Speaker 1: founder of the Trafalgar Group. His name is Robert Colly. 13 00:00:33,760 --> 00:00:36,479 Speaker 1: You've seen him on Fox News quite a bit. But 14 00:00:36,760 --> 00:00:38,559 Speaker 1: he has a history of getting things right. I mean, 15 00:00:38,560 --> 00:00:40,400 Speaker 1: you look at over the last six years, their average 16 00:00:40,479 --> 00:00:45,000 Speaker 1: error rate is two point four percent. Even in the election, 17 00:00:45,040 --> 00:00:47,839 Speaker 1: they made a huge name for themselves because you know, 18 00:00:47,920 --> 00:00:51,000 Speaker 1: he stood by his polling results which showed a clear 19 00:00:51,080 --> 00:00:54,720 Speaker 1: three D plus Trump victory. You know, he was an outlier. 20 00:00:54,800 --> 00:00:58,440 Speaker 1: Basically consensus was saying Hillary Clinton would win, and Robert 21 00:00:58,440 --> 00:01:01,360 Speaker 1: didn't back down. He didn't act down. He stood by 22 00:01:01,440 --> 00:01:04,200 Speaker 1: his results. You've got the real clear politics. Tom Bevan 23 00:01:04,280 --> 00:01:07,240 Speaker 1: has said that the Trafalgar Group are widely recognized as 24 00:01:07,280 --> 00:01:11,200 Speaker 1: one of America's most consistently accurate and trusted. The Weekly 25 00:01:11,280 --> 00:01:14,479 Speaker 1: Standards said a single firm had the most accurate polls 26 00:01:14,480 --> 00:01:20,000 Speaker 1: in Florida, Pennsylvania, Michigan, North Carolina, Ohio, Colorado, in Georgia. 27 00:01:20,200 --> 00:01:23,440 Speaker 1: Talking about the Trafalgar Group, So they've gotten a lot, right, 28 00:01:23,920 --> 00:01:27,039 Speaker 1: I think, largely because they don't inject bias into the polls, 29 00:01:27,080 --> 00:01:29,480 Speaker 1: and they do things like likely voters, particularly at a 30 00:01:29,480 --> 00:01:31,760 Speaker 1: time right now when we're head of the election, and 31 00:01:31,800 --> 00:01:35,440 Speaker 1: that's really important. So we are going to talk to 32 00:01:35,560 --> 00:01:38,560 Speaker 1: Robert about this election cycle, what it all means, talk 33 00:01:38,600 --> 00:01:41,400 Speaker 1: about that hidden Trump vote in two thousand sixteen, two 34 00:01:41,400 --> 00:01:43,760 Speaker 1: thousand twenty. Are we gonna see a little bit of 35 00:01:43,760 --> 00:01:46,440 Speaker 1: that again? Is there a hidden Republican vote? So we're 36 00:01:46,440 --> 00:01:48,840 Speaker 1: going to talk to Robert about all of this, a 37 00:01:48,840 --> 00:01:50,960 Speaker 1: guy who is a history of getting things right, what 38 00:01:51,120 --> 00:01:54,280 Speaker 1: he thinks about today's political landscape, where things are going, 39 00:01:54,400 --> 00:01:57,160 Speaker 1: and what voters care about. So stay tuned for Robert 40 00:01:57,240 --> 00:02:10,799 Speaker 1: Cally Trafalgar Group. That's next. We've got Robert ka Haley 41 00:02:10,880 --> 00:02:13,760 Speaker 1: from the Trafalgar Group. You know, Robert, I'm really looking 42 00:02:13,800 --> 00:02:15,880 Speaker 1: forward to this conversation because you're one of the few 43 00:02:15,960 --> 00:02:20,720 Speaker 1: posters people still trust in this day and age. Well, 44 00:02:20,840 --> 00:02:25,960 Speaker 1: you know, we got into poeing because not because this 45 00:02:26,080 --> 00:02:30,040 Speaker 1: was anybody's big ambition. This isn't what I took in school. Uh. 46 00:02:30,080 --> 00:02:33,720 Speaker 1: You know, I was a political of doing campaigns, and 47 00:02:33,800 --> 00:02:36,480 Speaker 1: we were I was a general consultant. We were running 48 00:02:37,000 --> 00:02:41,040 Speaker 1: kind of legislative congressional level races around the country. And 49 00:02:41,040 --> 00:02:45,120 Speaker 1: what we found is the poem was just terrible. And 50 00:02:46,120 --> 00:02:48,480 Speaker 1: everybody kind of knew it was terrible, but you know 51 00:02:48,480 --> 00:02:54,720 Speaker 1: it was like nobody had anything better. And I've studied 52 00:02:54,760 --> 00:02:58,640 Speaker 1: a little bit um kind of watching. Uh, some you know, 53 00:02:58,880 --> 00:03:00,920 Speaker 1: kind of digging into the pool. Weren't asking, you know, 54 00:03:01,480 --> 00:03:03,880 Speaker 1: volunteer into see if I can go to the call 55 00:03:03,960 --> 00:03:06,000 Speaker 1: center and see what it looks like down there and 56 00:03:06,280 --> 00:03:08,880 Speaker 1: stuff like that and just get a sense for how 57 00:03:08,919 --> 00:03:11,160 Speaker 1: they were doing it. And so I found some what 58 00:03:11,280 --> 00:03:17,360 Speaker 1: I found figured to be serious falls and UH decided, hey, 59 00:03:17,440 --> 00:03:20,400 Speaker 1: we should we should try this, uh, and we should 60 00:03:20,440 --> 00:03:23,160 Speaker 1: try to do it with at a different angle. Uh. 61 00:03:23,160 --> 00:03:25,000 Speaker 1: And now there was some bomps along the way we that. 62 00:03:25,120 --> 00:03:29,360 Speaker 1: There's obviously there's some questions when the new technology of 63 00:03:29,360 --> 00:03:32,959 Speaker 1: the automated polls came out, Uh, there was some questions 64 00:03:33,000 --> 00:03:35,520 Speaker 1: about where you can you can't do that same as 65 00:03:35,520 --> 00:03:38,160 Speaker 1: automated calls, and we had a little buff of the 66 00:03:38,200 --> 00:03:40,880 Speaker 1: road figuring all that out. But in the end, what 67 00:03:40,920 --> 00:03:45,320 Speaker 1: we determined was there's some basic mistakes that the police 68 00:03:45,400 --> 00:03:48,760 Speaker 1: is making first and foremost, I mean, and this is 69 00:03:48,840 --> 00:03:51,240 Speaker 1: this is the key thing. If you ever want to 70 00:03:51,280 --> 00:03:55,040 Speaker 1: know how reliable pol is, look how many questions I asked. 71 00:03:56,960 --> 00:03:59,000 Speaker 1: We do not live in a day and age where 72 00:03:59,040 --> 00:04:04,160 Speaker 1: average people have time to answer long poles. So what 73 00:04:04,360 --> 00:04:12,800 Speaker 1: happens is poles disproportionately represent the opinion of those who 74 00:04:13,160 --> 00:04:16,120 Speaker 1: have the time, and those people tend to care about 75 00:04:16,120 --> 00:04:18,919 Speaker 1: politics a little bit too much. They tend to be 76 00:04:19,040 --> 00:04:23,440 Speaker 1: higher educated, and they tend to be very firm uh 77 00:04:23,520 --> 00:04:27,400 Speaker 1: in the in their corner. They're either firmly liberal or 78 00:04:27,480 --> 00:04:31,000 Speaker 1: firmly conservative. And the worst is the people who are 79 00:04:31,080 --> 00:04:34,000 Speaker 1: just bored and need someone to talk to. And you'd 80 00:04:34,040 --> 00:04:37,680 Speaker 1: be surprised how people and our society fit that. So 81 00:04:39,120 --> 00:04:41,880 Speaker 1: we just think they miss average people. And that's the 82 00:04:41,920 --> 00:04:44,600 Speaker 1: first thing I mean candidates all the time tell me, oh, 83 00:04:44,640 --> 00:04:46,680 Speaker 1: well you know this, this this is the pulse when 84 00:04:46,680 --> 00:04:48,760 Speaker 1: I gave me and I'll look at it thirty or 85 00:04:48,760 --> 00:04:53,760 Speaker 1: five questions, It's worthless. The manything, there's so much bias 86 00:04:53,880 --> 00:04:56,039 Speaker 1: that exists in today's world. And when we see it 87 00:04:56,040 --> 00:04:57,359 Speaker 1: in the media, I mean we see it when we 88 00:04:57,400 --> 00:05:00,040 Speaker 1: turn on the TV. We see it constantly. The bias 89 00:05:00,040 --> 00:05:03,960 Speaker 1: that exists and our society. Is there bias in pulling 90 00:05:04,720 --> 00:05:08,479 Speaker 1: and if so, how does that bias present itself? Well, 91 00:05:08,520 --> 00:05:12,920 Speaker 1: first of all, the self selection that that many questions 92 00:05:12,920 --> 00:05:16,320 Speaker 1: it gives you creates a level of bias. I looked 93 00:05:16,360 --> 00:05:20,000 Speaker 1: at one the other day. It doesn't matter what state um, 94 00:05:20,040 --> 00:05:25,039 Speaker 1: but the college graduates, post college and I mean post 95 00:05:25,080 --> 00:05:30,080 Speaker 1: college and post degree. That so like everything from a 96 00:05:30,160 --> 00:05:33,080 Speaker 1: from a master's, a doctorate, in a college graduate. They 97 00:05:33,160 --> 00:05:37,279 Speaker 1: represented fifty percent of the people in the pole. There's 98 00:05:37,320 --> 00:05:40,680 Speaker 1: not a state in America that that looks like a 99 00:05:40,760 --> 00:05:45,840 Speaker 1: general election turnout nowhere, it looks like the kind of 100 00:05:45,839 --> 00:05:49,000 Speaker 1: people who were participate. And in that case of forty 101 00:05:49,000 --> 00:05:51,840 Speaker 1: two questions pol And. So what it does is it 102 00:05:52,120 --> 00:05:55,040 Speaker 1: creates a bias to begin with a people who are 103 00:05:55,080 --> 00:05:59,359 Speaker 1: more involved in politics and right now, people who have 104 00:05:59,400 --> 00:06:04,159 Speaker 1: a greater epicacy tend to lean left. So that's the 105 00:06:04,200 --> 00:06:08,440 Speaker 1: first not intentional by us. And then of course, I 106 00:06:08,480 --> 00:06:11,880 Speaker 1: mean you have to look at it every company, no 107 00:06:11,920 --> 00:06:14,240 Speaker 1: matter what you do. I don't care if you make tomatoes, 108 00:06:14,640 --> 00:06:17,560 Speaker 1: I don't care. You know, grotovators, I don't kindn't care. 109 00:06:17,560 --> 00:06:21,880 Speaker 1: If if you build cars, you are built. You are 110 00:06:21,960 --> 00:06:24,599 Speaker 1: based on what the people who are paying you want. 111 00:06:25,720 --> 00:06:30,760 Speaker 1: You're you know you your answer to them. So the 112 00:06:30,880 --> 00:06:34,920 Speaker 1: question is do the media outlets seek polls that are perfect? 113 00:06:35,360 --> 00:06:39,800 Speaker 1: Are seek polls that fit the agenda of their news department? 114 00:06:41,520 --> 00:06:45,960 Speaker 1: And do the universities want poles that are accurate our 115 00:06:46,080 --> 00:06:51,159 Speaker 1: polls that seek to reflect the position of their university. 116 00:06:51,640 --> 00:06:54,200 Speaker 1: I mean, in the end, what we found is a 117 00:06:54,279 --> 00:06:56,039 Speaker 1: lot of the polls and and this is this is 118 00:06:56,040 --> 00:06:58,200 Speaker 1: true for the ones put out by the partisan groups 119 00:06:58,200 --> 00:07:00,760 Speaker 1: that that are out there. You know they paid for 120 00:07:00,839 --> 00:07:04,160 Speaker 1: by the Republican Party, Democrat Party, paid for candidate, paid 121 00:07:04,160 --> 00:07:06,640 Speaker 1: for by a pack. But in the end you have 122 00:07:06,720 --> 00:07:09,720 Speaker 1: to answer this question, what was this pollar designed for? 123 00:07:09,880 --> 00:07:13,239 Speaker 1: Is it to reflect the electorate or is it designed 124 00:07:13,240 --> 00:07:16,760 Speaker 1: to affect the electorate? And I will tell you effect 125 00:07:16,920 --> 00:07:20,000 Speaker 1: is much more than reflect right now, and that's one 126 00:07:20,040 --> 00:07:22,640 Speaker 1: of the problems. And so what they're doing is they're 127 00:07:22,680 --> 00:07:26,800 Speaker 1: flooding these these averages and giving people a thought since 128 00:07:26,800 --> 00:07:29,840 Speaker 1: where races are because there's four or five junk poles 129 00:07:29,880 --> 00:07:33,000 Speaker 1: in there that have an agenda and then only two 130 00:07:33,080 --> 00:07:35,360 Speaker 1: or three that are honest, and of course it leans 131 00:07:35,360 --> 00:07:37,480 Speaker 1: towards the agenda of the junk polls well, and to 132 00:07:37,600 --> 00:07:39,880 Speaker 1: that in that vein, I see. You know, if you 133 00:07:39,920 --> 00:07:42,280 Speaker 1: go to the real clear politics, particularly if you're just 134 00:07:42,280 --> 00:07:47,320 Speaker 1: saying at the congressional ballot, you have Republicans up five 135 00:07:47,440 --> 00:07:50,120 Speaker 1: right now, but you're also doing likely voters. A lot 136 00:07:50,200 --> 00:07:52,760 Speaker 1: of people are doing registered voters. Why do you do 137 00:07:52,880 --> 00:07:56,679 Speaker 1: likely voters? And why do other people do registered voters? Well, 138 00:07:57,840 --> 00:08:00,880 Speaker 1: first of all, because they're most these guys are really 139 00:08:00,880 --> 00:08:04,080 Speaker 1: bad at predicting news gonna vote. That's one of the 140 00:08:04,080 --> 00:08:07,920 Speaker 1: things we learned and the Trump campaign last time UH 141 00:08:09,120 --> 00:08:14,240 Speaker 1: is they were whole groups they weren't pulling, so likely 142 00:08:14,320 --> 00:08:18,800 Speaker 1: voters tends to give UH an advantage to the Democrats. 143 00:08:19,600 --> 00:08:22,160 Speaker 1: It just does. I don't know why they switched to it, 144 00:08:22,240 --> 00:08:25,680 Speaker 1: other it seems very obvious to me because when you're 145 00:08:25,680 --> 00:08:29,600 Speaker 1: calling registered voters or you mean registered voters gives an 146 00:08:29,640 --> 00:08:33,000 Speaker 1: advantage to Democrats? Absolutely? Yeah, okay, I think you actidently 147 00:08:33,040 --> 00:08:35,920 Speaker 1: said likely voters, but you mean registered voters gives the 148 00:08:35,960 --> 00:08:38,880 Speaker 1: advantage to Democrats. Yeah, no worries, stories, just wanted to 149 00:08:38,920 --> 00:08:41,720 Speaker 1: make sure. But when you're calling registed voters, you're calling 150 00:08:41,880 --> 00:08:46,480 Speaker 1: everyone from Johnny and Susie who have voted in every 151 00:08:46,520 --> 00:08:51,880 Speaker 1: single election every two years to build the bomb. Who 152 00:08:51,960 --> 00:08:56,040 Speaker 1: hasn't voted in ten years but still registered? How is 153 00:08:56,080 --> 00:08:59,880 Speaker 1: that more accurate poll? How do you at least not 154 00:09:00,160 --> 00:09:03,520 Speaker 1: filter out people who haven't voted at all? Now, I'm 155 00:09:03,559 --> 00:09:06,520 Speaker 1: a big fan of identifying low propensitive voters that I 156 00:09:06,520 --> 00:09:11,240 Speaker 1: think will participate in particular election. But we but just imagine, 157 00:09:11,280 --> 00:09:13,680 Speaker 1: we had one of the biggest turnouts in America in 158 00:09:14,559 --> 00:09:18,160 Speaker 1: no one will debate back. Do you think somebody who 159 00:09:18,200 --> 00:09:21,480 Speaker 1: did not get off their couch and vote in should 160 00:09:21,480 --> 00:09:28,640 Speaker 1: be getting pulled about two? It's insane. It does not. 161 00:09:29,440 --> 00:09:33,719 Speaker 1: It is a fundamental problem, and we're never gonna do that. 162 00:09:34,200 --> 00:09:37,760 Speaker 1: Talk a little bit about you know, the hidden Trump 163 00:09:37,800 --> 00:09:44,319 Speaker 1: support that existed in and well, it manifested itself two 164 00:09:44,320 --> 00:09:48,400 Speaker 1: different ways in we called you they were the kind 165 00:09:48,440 --> 00:09:51,199 Speaker 1: of hidden the shy Trump voter. And these were people 166 00:09:51,240 --> 00:09:56,400 Speaker 1: who were definitely voting for trauma um but because of 167 00:09:57,120 --> 00:09:59,439 Speaker 1: you know, Hilly calling him the basket of the plorables, 168 00:09:59,520 --> 00:10:02,200 Speaker 1: and and just kind of the stigma that was out there, 169 00:10:03,320 --> 00:10:06,880 Speaker 1: they were hesitant to say so on the phone. They 170 00:10:06,960 --> 00:10:11,000 Speaker 1: just were. And the thing is, we all know, I mean, 171 00:10:11,120 --> 00:10:14,000 Speaker 1: just forget what I tell you. We all know this 172 00:10:14,080 --> 00:10:18,040 Speaker 1: is real. Every single person who's listening to this knows 173 00:10:18,080 --> 00:10:20,400 Speaker 1: somebody who was for Trump who didn't want all their 174 00:10:20,400 --> 00:10:23,360 Speaker 1: friends and family to know it and didn't talk about it, 175 00:10:23,360 --> 00:10:24,880 Speaker 1: didn't have a sign in, they y ared, but could 176 00:10:24,920 --> 00:10:27,000 Speaker 1: not wait to vote for it. We all know people 177 00:10:27,080 --> 00:10:30,840 Speaker 1: like that. So why would why would you think that 178 00:10:30,920 --> 00:10:35,320 Speaker 1: everyone is just this forthcoming is forthcoming in a pole? 179 00:10:35,400 --> 00:10:38,520 Speaker 1: Of course they weren't. So what they were doing is 180 00:10:38,520 --> 00:10:40,679 Speaker 1: that they weren't saying who there for were or they 181 00:10:40,679 --> 00:10:42,800 Speaker 1: were staying in deciety and they when we weren't, or 182 00:10:42,840 --> 00:10:44,720 Speaker 1: they were staying there for Hillary. So what we did 183 00:10:44,760 --> 00:10:47,640 Speaker 1: is we we used a little vehicle. Uh. And I 184 00:10:47,679 --> 00:10:49,360 Speaker 1: would love to take credit for this, but it is 185 00:10:49,440 --> 00:10:52,160 Speaker 1: not my idea. There's it was a great guy. He 186 00:10:52,400 --> 00:10:55,080 Speaker 1: was one of Lee Atwater's Contemporaries in South Carolina, where 187 00:10:55,080 --> 00:10:57,600 Speaker 1: I grew up and did politics. His name was um 188 00:10:57,840 --> 00:11:01,400 Speaker 1: Rod Sheeley, and Roy Chiley told me, he said, you 189 00:11:01,440 --> 00:11:03,720 Speaker 1: need to give people a polite way to tell you 190 00:11:03,800 --> 00:11:07,440 Speaker 1: something that they think is impolite. And he said you 191 00:11:07,480 --> 00:11:09,800 Speaker 1: need to ask him, well, how do you think? What 192 00:11:09,840 --> 00:11:13,120 Speaker 1: do you think most of your neighbors think? And so 193 00:11:13,480 --> 00:11:15,640 Speaker 1: we came back through and I said, let's just use 194 00:11:15,679 --> 00:11:18,320 Speaker 1: this little sleeves to the neighbor question. So he said, 195 00:11:18,320 --> 00:11:20,760 Speaker 1: all right, well, okay, you're for Hillary. What do you 196 00:11:20,800 --> 00:11:22,679 Speaker 1: think who do you think most of your neighbors for? 197 00:11:24,480 --> 00:11:28,040 Speaker 1: And they would say Trump. And what that is is 198 00:11:28,080 --> 00:11:30,960 Speaker 1: it's it's a projection vehicle and lets people tell you 199 00:11:31,000 --> 00:11:33,440 Speaker 1: what they wanted to tell you about you, thinking that's 200 00:11:33,480 --> 00:11:37,719 Speaker 1: their opinion. So we started looking at the difference and 201 00:11:37,760 --> 00:11:41,640 Speaker 1: in and in states we would measure that how what 202 00:11:41,720 --> 00:11:45,960 Speaker 1: the margin was between those who said Trump and those 203 00:11:46,440 --> 00:11:50,240 Speaker 1: who said Hillary and then those who neighbor question was 204 00:11:50,240 --> 00:11:55,520 Speaker 1: was different. And the common thing was Hillary uh never 205 00:11:55,960 --> 00:12:01,160 Speaker 1: went with up. Hilly support never went up, It always 206 00:12:01,200 --> 00:12:05,760 Speaker 1: went down, and Trump support always went up. And so 207 00:12:05,840 --> 00:12:08,160 Speaker 1: in every single state it was like, all right, hey, 208 00:12:08,160 --> 00:12:10,559 Speaker 1: this state's an eight. You know the states the three 209 00:12:11,640 --> 00:12:14,600 Speaker 1: and we can figure out pretty quickly consistently what what 210 00:12:14,640 --> 00:12:18,000 Speaker 1: the margin was between the people who said Trumph on 211 00:12:18,160 --> 00:12:21,920 Speaker 1: question one, I mean Hillary on question question one and 212 00:12:21,920 --> 00:12:25,320 Speaker 1: and said Trump on question two. So we were able 213 00:12:25,400 --> 00:12:29,560 Speaker 1: to get to a better estivation of where the hidden 214 00:12:29,559 --> 00:12:34,600 Speaker 1: Trump vote was in these states and better identify them 215 00:12:34,679 --> 00:12:38,040 Speaker 1: and then ask them some other specific questions. There are 216 00:12:38,080 --> 00:12:41,600 Speaker 1: a lot of things that we don't explain, uh to 217 00:12:41,720 --> 00:12:44,520 Speaker 1: help us get there because they're they're very proprietary ways 218 00:12:44,559 --> 00:12:48,080 Speaker 1: we question people. There's ways you can do all kinds 219 00:12:48,080 --> 00:12:51,000 Speaker 1: of stuff in pulling that you can get you a 220 00:12:51,000 --> 00:12:53,480 Speaker 1: more honest answer. I mean, if you want to be 221 00:12:53,520 --> 00:12:55,559 Speaker 1: one of our callers, you don't. It's not just like 222 00:12:55,640 --> 00:12:58,280 Speaker 1: being a regular caller who do the polling calls. You 223 00:12:58,360 --> 00:13:01,480 Speaker 1: have to sign a document. You will never suppose the 224 00:13:01,480 --> 00:13:03,720 Speaker 1: way we teach you to ask questions. Take a quick 225 00:13:03,720 --> 00:13:07,040 Speaker 1: commercial break back with Robert Colly of the Trafalgar Group. 226 00:13:07,320 --> 00:13:13,600 Speaker 1: That's next. Is there a hidden Geope vote right now? 227 00:13:13,640 --> 00:13:16,719 Speaker 1: Because I think because the left has gone so insane 228 00:13:16,760 --> 00:13:18,959 Speaker 1: on certain issues, I would imagine there are a lot 229 00:13:18,960 --> 00:13:22,480 Speaker 1: of independence or maybe even some soft Dems who may 230 00:13:22,480 --> 00:13:26,280 Speaker 1: be inclined to vote a different way given today's environment. 231 00:13:26,520 --> 00:13:29,760 Speaker 1: Is there is there a hidden Geope vote? Perhaps? Yeah, 232 00:13:29,840 --> 00:13:33,680 Speaker 1: that's often tell you was totally different. It wasn't just 233 00:13:33,760 --> 00:13:36,720 Speaker 1: they were hidden. They didn't want to participate. The pole 234 00:13:39,040 --> 00:13:43,600 Speaker 1: with a whole new ballgame in you had conservatives Trump 235 00:13:43,600 --> 00:13:48,079 Speaker 1: supporters who literally just didn't want to take polls. What 236 00:13:48,160 --> 00:13:51,520 Speaker 1: we found is to get we would have to try 237 00:13:51,960 --> 00:13:57,320 Speaker 1: five times. It's hard to get Republicans as Democrats. So 238 00:13:58,080 --> 00:14:00,400 Speaker 1: if you're doing a poll and you need he get 239 00:14:00,960 --> 00:14:04,120 Speaker 1: you know, thirty eight percent Republican for that state and 240 00:14:04,200 --> 00:14:08,520 Speaker 1: you need to get you know, forty Democrat for that state, 241 00:14:08,920 --> 00:14:12,160 Speaker 1: we would have to work way harder. We'd have to 242 00:14:12,200 --> 00:14:15,240 Speaker 1: call five times the number of people, reach out to 243 00:14:15,320 --> 00:14:19,400 Speaker 1: five times a number of people to get that percent Republican. 244 00:14:19,440 --> 00:14:22,440 Speaker 1: Then who would to get to the Democrats because they 245 00:14:22,440 --> 00:14:25,840 Speaker 1: were hesitant answer. Now here's the other problem. The ones 246 00:14:25,880 --> 00:14:29,120 Speaker 1: that were not hesitant ansch on the Republican side where 247 00:14:29,120 --> 00:14:31,680 Speaker 1: they never Trump Republicans who couldn't wait to tell you 248 00:14:31,720 --> 00:14:37,560 Speaker 1: they hated it. So if the poem company wasn't really 249 00:14:37,600 --> 00:14:41,960 Speaker 1: trying to get this right, they would end up artificially 250 00:14:42,480 --> 00:14:45,720 Speaker 1: boosting the size of never trump Republicans since they were 251 00:14:45,760 --> 00:14:49,680 Speaker 1: more likely to participate in polls, if you didn't filter 252 00:14:49,840 --> 00:14:52,640 Speaker 1: that out and put that as a percentage. Wait, that's 253 00:14:52,640 --> 00:14:55,720 Speaker 1: saying ay, all right, hey, among the Republicans we get 254 00:14:56,360 --> 00:14:59,600 Speaker 1: there is you know, eleven percent that are never Trumpers. Well, 255 00:14:59,640 --> 00:15:03,800 Speaker 1: if you didn't make sure that was the case, you'd 256 00:15:03,880 --> 00:15:06,680 Speaker 1: end up, you know, having of your Republicans have be 257 00:15:06,720 --> 00:15:09,680 Speaker 1: never Trumpers or more and the pole would be off. 258 00:15:10,920 --> 00:15:13,200 Speaker 1: And that's why you saw some of the nonsense you 259 00:15:13,320 --> 00:15:17,960 Speaker 1: saw in these states. I mean, you know, Wisconsin the 260 00:15:18,000 --> 00:15:20,520 Speaker 1: great a great example. We had the best pole in 261 00:15:20,520 --> 00:15:23,840 Speaker 1: Wisconsin in America, and Washington, I think it was Washington 262 00:15:23,880 --> 00:15:26,760 Speaker 1: Post ABC had it off by like twelve And we 263 00:15:26,800 --> 00:15:28,880 Speaker 1: had the best one in Florida, and the best one 264 00:15:28,880 --> 00:15:30,960 Speaker 1: in North Carolina, and the best one in Duxas and 265 00:15:31,000 --> 00:15:33,720 Speaker 1: the best one I know why Yeah, And there was 266 00:15:33,880 --> 00:15:35,560 Speaker 1: and there were a few states that you know, we 267 00:15:35,560 --> 00:15:38,560 Speaker 1: were a couple of points off, and that sometimes like 268 00:15:38,560 --> 00:15:40,560 Speaker 1: a couple of points of difference between winning and losing, 269 00:15:40,600 --> 00:15:43,240 Speaker 1: and we can all just we could spend the rest 270 00:15:43,280 --> 00:15:46,520 Speaker 1: of our lives discussing how that couple of points got done, 271 00:15:46,640 --> 00:15:49,200 Speaker 1: whether it was right, but it doesn't matter, it's the past. 272 00:15:50,040 --> 00:15:53,760 Speaker 1: But the fact is, these these things, these things matter. 273 00:15:53,840 --> 00:15:57,240 Speaker 1: And that's why the polling was so off compared to 274 00:15:57,240 --> 00:15:59,320 Speaker 1: what happened. They said this was gonna be a Joe 275 00:15:59,320 --> 00:16:01,360 Speaker 1: Biden blower. It was by no means that Joe Biden 276 00:16:01,400 --> 00:16:03,880 Speaker 1: blowout a few votes in a few different states. And 277 00:16:03,880 --> 00:16:06,000 Speaker 1: it's different game. So what does that mean for the 278 00:16:06,000 --> 00:16:10,240 Speaker 1: mid terms. The mid terms are very much just like 279 00:16:10,680 --> 00:16:17,280 Speaker 1: what Virginia was. So so if Virginia was, hey, we 280 00:16:17,360 --> 00:16:19,920 Speaker 1: have this oude of your election, there are lots of 281 00:16:20,120 --> 00:16:24,000 Speaker 1: Republicans and Democrats in Virginia who vote every two years 282 00:16:24,000 --> 00:16:28,000 Speaker 1: for federal elections, but don't vote in oude your elections. 283 00:16:28,040 --> 00:16:31,000 Speaker 1: So it was a battle between the two parties see 284 00:16:31,040 --> 00:16:37,760 Speaker 1: which one could build up closer to their turnout. So 285 00:16:37,840 --> 00:16:41,200 Speaker 1: it's going to grab those people who don't usually vote, 286 00:16:41,360 --> 00:16:43,280 Speaker 1: who did vote in twenty and trying to get as 287 00:16:43,280 --> 00:16:45,560 Speaker 1: many of them to vote as possible. It's the same 288 00:16:45,600 --> 00:16:49,880 Speaker 1: thing here. It's it is a it is a motivation game, 289 00:16:50,760 --> 00:16:54,400 Speaker 1: and so that's and so that's what's really key. So 290 00:16:54,440 --> 00:16:56,400 Speaker 1: that's why I really don't understand these people who are 291 00:16:56,400 --> 00:17:02,000 Speaker 1: polling beyond registered voters who hadn't gotten off their sixteen 292 00:17:02,000 --> 00:17:04,560 Speaker 1: I don't know if those didn't those elections didn't wake up, 293 00:17:04,560 --> 00:17:09,639 Speaker 1: I don't know what will um. But so that's so 294 00:17:09,760 --> 00:17:12,760 Speaker 1: you have to you have to pull anticipating your turnout 295 00:17:12,880 --> 00:17:19,320 Speaker 1: is gonna be somewhere between, and a lot of that 296 00:17:19,440 --> 00:17:24,199 Speaker 1: is just where you think that's gonna be. Are uh 297 00:17:24,440 --> 00:17:28,600 Speaker 1: a young family, you know, people with school age children. 298 00:17:29,359 --> 00:17:31,399 Speaker 1: Are they going to turn out in the rate they 299 00:17:31,440 --> 00:17:35,320 Speaker 1: did in ten or are they going to turn out 300 00:17:35,359 --> 00:17:37,800 Speaker 1: like they did in Virginia because of all the school stuff? 301 00:17:38,280 --> 00:17:40,560 Speaker 1: Are they motivated on that? Will they come in a 302 00:17:40,640 --> 00:17:43,399 Speaker 1: higher margin? What what proportion of the thing You're just 303 00:17:43,400 --> 00:17:46,800 Speaker 1: gonna turn out? And a usual midterm election, the seniors 304 00:17:46,800 --> 00:17:50,680 Speaker 1: would represent a much bigger point, much bigger part than 305 00:17:50,720 --> 00:17:53,840 Speaker 1: they do in a in a presidential election, So a 306 00:17:53,840 --> 00:17:56,080 Speaker 1: lot of that The difference is first of how you 307 00:17:56,160 --> 00:18:01,320 Speaker 1: model your turnout, because again how your collection and stuff 308 00:18:01,400 --> 00:18:03,840 Speaker 1: is based on the models for your turnout and the 309 00:18:03,880 --> 00:18:06,800 Speaker 1: way you collect the information. So yeah, it's gonna it's 310 00:18:06,880 --> 00:18:09,960 Speaker 1: it's gonna affect it a lot. And everybody's you know, 311 00:18:10,600 --> 00:18:14,080 Speaker 1: having different scenarios. I mean, I guess take of me 312 00:18:14,080 --> 00:18:17,120 Speaker 1: an attach form being Republicans. So frankly, most of our 313 00:18:17,119 --> 00:18:23,320 Speaker 1: scenarios are are the better snare for the Democrats and 314 00:18:23,320 --> 00:18:26,880 Speaker 1: the Republicans, just so we can avoid that accusation. From 315 00:18:26,920 --> 00:18:29,359 Speaker 1: what you've seen so far in the pooling you've done 316 00:18:29,440 --> 00:18:33,760 Speaker 1: for this cycle, what does the political landscape look like 317 00:18:34,040 --> 00:18:39,880 Speaker 1: right now? People are frustrated in general. Um, they they 318 00:18:39,880 --> 00:18:43,119 Speaker 1: don't really look at a party to solve their problems. 319 00:18:43,720 --> 00:18:45,560 Speaker 1: And this is what I was trying to say a 320 00:18:45,600 --> 00:18:48,840 Speaker 1: long time ago when all these generic ballots came out. Now, like, oh, 321 00:18:49,000 --> 00:18:56,040 Speaker 1: Republicans are sixty years Listen these people Republicans. When when 322 00:18:56,040 --> 00:18:59,120 Speaker 1: you see that generic ballot jumping around, what that is 323 00:18:59,880 --> 00:19:02,080 Speaker 1: is like it's like a guy who's been in a 324 00:19:02,080 --> 00:19:05,440 Speaker 1: casino all night bet and black and he says, well, 325 00:19:05,600 --> 00:19:08,000 Speaker 1: all I'm doing is losing. I'm gonna bet read for 326 00:19:08,040 --> 00:19:10,840 Speaker 1: a while. That ain't the car I usually bet, but 327 00:19:10,880 --> 00:19:13,280 Speaker 1: I'm gonna because I'm we'll see if I get something different. 328 00:19:14,240 --> 00:19:16,959 Speaker 1: And that's what this is is I don't like what 329 00:19:17,040 --> 00:19:19,480 Speaker 1: I got and we'll see if I get something different. 330 00:19:19,800 --> 00:19:21,920 Speaker 1: So if the Republicans do pick up a bunch of seach, 331 00:19:22,000 --> 00:19:24,040 Speaker 1: don't get in your heads that all these people suddenly 332 00:19:24,359 --> 00:19:27,439 Speaker 1: became Republicans, and they're gonna be walking around, you know, 333 00:19:27,480 --> 00:19:29,639 Speaker 1: wearing red T shirts for the next ten years. No, 334 00:19:30,200 --> 00:19:33,680 Speaker 1: they've given you a chance to earn their votes. Well 335 00:19:33,760 --> 00:19:38,760 Speaker 1: you're gonna do with it. The American public is shopping 336 00:19:39,680 --> 00:19:42,879 Speaker 1: and they don't know when they don't want a party 337 00:19:42,920 --> 00:19:46,000 Speaker 1: at all, but they've got to find one. And so 338 00:19:46,080 --> 00:19:50,840 Speaker 1: what I see as voters that are hesitantly moving towards 339 00:19:50,840 --> 00:19:53,720 Speaker 1: the Republicans with because they think they might be better 340 00:19:54,440 --> 00:19:58,320 Speaker 1: than what they've got. And there has become a separation 341 00:19:58,400 --> 00:20:00,959 Speaker 1: that be it that it's started, it are official or not, 342 00:20:01,320 --> 00:20:04,040 Speaker 1: between Joe Biden and the Democrats. What the Democrats are 343 00:20:04,080 --> 00:20:08,000 Speaker 1: doing is they're letting Joe Biden take all the negative 344 00:20:08,040 --> 00:20:12,080 Speaker 1: and letting me get blamed on him and trying to 345 00:20:12,119 --> 00:20:16,560 Speaker 1: separate themselves from him. But in the end, I think 346 00:20:16,600 --> 00:20:20,160 Speaker 1: this election is a reference among Joe Biden. I mean, 347 00:20:20,240 --> 00:20:22,680 Speaker 1: this is the Joe Biden work or not? Is the 348 00:20:22,760 --> 00:20:28,119 Speaker 1: sum administration making the difference that you wanted or is 349 00:20:28,160 --> 00:20:30,920 Speaker 1: it going the opposite way? I mean, what's Saul said 350 00:20:30,920 --> 00:20:34,880 Speaker 1: and done? I think what you'll see And I said 351 00:20:34,920 --> 00:20:37,520 Speaker 1: this the other iron handy, I said, I think running 352 00:20:37,600 --> 00:20:40,399 Speaker 1: as a Democrat with Joe Biden as president is like 353 00:20:40,520 --> 00:20:44,600 Speaker 1: having a household of termites. Everything looks fine on the outside, 354 00:20:45,359 --> 00:20:49,040 Speaker 1: nobody's talking about it, nobody has any clue. But before 355 00:20:49,080 --> 00:20:51,439 Speaker 1: it's over, it's going to be the only thing that matters. 356 00:20:51,600 --> 00:20:55,720 Speaker 1: How much did the FBI raid on Maral logo. How 357 00:20:55,800 --> 00:20:58,360 Speaker 1: much has that impacted things or or has it? Well 358 00:20:58,400 --> 00:21:01,560 Speaker 1: remember that part about how it's switch party can get 359 00:21:01,560 --> 00:21:08,240 Speaker 1: closer to the turnout. That's that helped the Republicans get closer. 360 00:21:09,320 --> 00:21:13,159 Speaker 1: There was a level. I mean there was and I 361 00:21:13,200 --> 00:21:19,560 Speaker 1: saw this in early the Republicans. We're so angry at 362 00:21:19,640 --> 00:21:23,159 Speaker 1: all the nonsense in the summer of and all the 363 00:21:23,200 --> 00:21:25,760 Speaker 1: masks mandate and all I just all the COVID nonsense, 364 00:21:25,840 --> 00:21:28,520 Speaker 1: all the statue being torn down, all the approaches. They 365 00:21:28,520 --> 00:21:32,520 Speaker 1: were just so angry, and it was like the only 366 00:21:32,520 --> 00:21:35,960 Speaker 1: way to scratch that itch was to vote. The only 367 00:21:36,040 --> 00:21:39,000 Speaker 1: way to get some release from that pressure was to vote. 368 00:21:39,840 --> 00:21:44,160 Speaker 1: And I'm seeing them feel that way again, and I'm 369 00:21:44,200 --> 00:21:49,399 Speaker 1: seeing the Democrats certainly motivated, but not like that. And 370 00:21:49,440 --> 00:21:51,199 Speaker 1: then there are people in the middle that are just 371 00:21:52,359 --> 00:21:56,560 Speaker 1: I just almost like in Trump selection where I said 372 00:21:56,600 --> 00:21:58,439 Speaker 1: that people a lot of people voted for Trump. They 373 00:21:58,440 --> 00:22:01,800 Speaker 1: didn't like Trump, that's necessarily. They just saw Trump as 374 00:22:01,800 --> 00:22:03,760 Speaker 1: a big old monkey wrench that they could throw in 375 00:22:03,800 --> 00:22:07,479 Speaker 1: the middle of a machine that was hurting them. They said, 376 00:22:07,320 --> 00:22:09,640 Speaker 1: how to hate this system? How do I break it? Oh, 377 00:22:09,760 --> 00:22:11,840 Speaker 1: that guy looks like a monkey rinch will break it. 378 00:22:12,200 --> 00:22:15,560 Speaker 1: Let me throw it. That's what they wanted. And people 379 00:22:15,640 --> 00:22:18,800 Speaker 1: just think this thing is dysfunctional. I mean, what we 380 00:22:18,920 --> 00:22:21,879 Speaker 1: get what people are telling us is the government cares 381 00:22:21,920 --> 00:22:27,560 Speaker 1: about all kinds of stuff I don't, and that would include, 382 00:22:28,400 --> 00:22:31,760 Speaker 1: you know, constant pressure on Green New Deal stuff which 383 00:22:31,880 --> 00:22:38,200 Speaker 1: pronounced people use, its establishing all of you know, the 384 00:22:38,200 --> 00:22:42,480 Speaker 1: white supremacists as a priority for the military, and all 385 00:22:42,560 --> 00:22:45,520 Speaker 1: the other things they see as nonsense and the things 386 00:22:45,600 --> 00:22:49,200 Speaker 1: that do matter to people. Am I safe in my home? 387 00:22:49,240 --> 00:22:53,119 Speaker 1: Are my children safe from fitting all? Um? Are people 388 00:22:53,160 --> 00:22:57,159 Speaker 1: coming across our our border and making our communities less safe? 389 00:22:58,320 --> 00:23:00,919 Speaker 1: Is the economy given me the opportunity to make a 390 00:23:00,960 --> 00:23:05,240 Speaker 1: good living? Yeah? And somehow the government doesn't seem to 391 00:23:05,240 --> 00:23:07,439 Speaker 1: be doing anything about that. In fact, seems to be 392 00:23:07,480 --> 00:23:11,920 Speaker 1: working to the opposite effect. And it's it's like, why 393 00:23:12,000 --> 00:23:14,680 Speaker 1: is the government working against me? I mean, I feel 394 00:23:14,720 --> 00:23:16,280 Speaker 1: that way. You know, I think a lot of the 395 00:23:16,280 --> 00:23:19,159 Speaker 1: people listening to this podcast feel that way. You know, 396 00:23:19,560 --> 00:23:20,960 Speaker 1: I know a lot of people on the left. They're 397 00:23:20,960 --> 00:23:23,560 Speaker 1: they're saying that the overturning of reverse Wade, that that 398 00:23:23,760 --> 00:23:27,480 Speaker 1: is going to motivate you know, Democrats, particularly Democratic woman. 399 00:23:27,840 --> 00:23:30,360 Speaker 1: Uh what are you saying in that? You know, how 400 00:23:30,480 --> 00:23:32,320 Speaker 1: much of a motivating factor do you think that's going 401 00:23:32,359 --> 00:23:35,040 Speaker 1: to be for the left? Well, first thing, I'll tell you, 402 00:23:35,240 --> 00:23:39,000 Speaker 1: I think the luckiest thing that ever happened to the 403 00:23:39,000 --> 00:23:44,400 Speaker 1: conservative movement was that Leak Because this decision came out 404 00:23:45,160 --> 00:23:48,040 Speaker 1: now as a decision, but almost like when you know, 405 00:23:48,560 --> 00:23:51,600 Speaker 1: a government floats an idea to see what the reaction 406 00:23:51,760 --> 00:23:54,840 Speaker 1: is and so then when happens, it doesn't seem so shocking. 407 00:23:55,760 --> 00:24:00,760 Speaker 1: The shock value of versus Wade being overturned was cut 408 00:24:01,400 --> 00:24:05,200 Speaker 1: in half by that Leak decision, and that was helpful 409 00:24:05,200 --> 00:24:09,879 Speaker 1: for the conservative movement, uh in general. Now, once the 410 00:24:09,920 --> 00:24:15,240 Speaker 1: decision came out, it upset them, but it wasn't It 411 00:24:15,280 --> 00:24:19,119 Speaker 1: wasn't as bad as it could have been. It what 412 00:24:19,400 --> 00:24:24,960 Speaker 1: it wasn't overwhelming. What I see moving people was what 413 00:24:25,080 --> 00:24:29,120 Speaker 1: happened in the days that followed, and that is some 414 00:24:29,200 --> 00:24:33,800 Speaker 1: of these states moving quickly to make further restrictions on 415 00:24:33,840 --> 00:24:37,639 Speaker 1: abortions and and and just kind of jump on this 416 00:24:37,760 --> 00:24:41,320 Speaker 1: opportunity because maybe they hadn't done anything on abortion before 417 00:24:41,400 --> 00:24:44,919 Speaker 1: that would now be legal. What we have found in 418 00:24:45,000 --> 00:24:49,520 Speaker 1: our national surveys is that when you look at first 419 00:24:49,560 --> 00:24:52,960 Speaker 1: trying you know abortion being legal. So p we have 420 00:24:53,160 --> 00:24:55,600 Speaker 1: where do you draw the line? And what we find 421 00:24:55,720 --> 00:24:59,000 Speaker 1: is an abortion for rape, incess, in life the mother, 422 00:24:59,800 --> 00:25:04,960 Speaker 1: for trimester before a heartbeat. You have fifty percent of 423 00:25:04,960 --> 00:25:10,520 Speaker 1: the population who says that's it, that's all you should have. 424 00:25:11,440 --> 00:25:15,960 Speaker 1: You start getting into second trimester, third trimester partial birth, 425 00:25:17,240 --> 00:25:20,960 Speaker 1: it keeps going down. Third trimester partial birth is only 426 00:25:21,000 --> 00:25:28,480 Speaker 1: eleven percent of the population that's against. And so when 427 00:25:28,760 --> 00:25:34,560 Speaker 1: the conservative movement right now is on heartbeat and not before, 428 00:25:35,240 --> 00:25:40,480 Speaker 1: I mean not after rape, incess life the mother, then 429 00:25:41,160 --> 00:25:46,720 Speaker 1: they have a winning majority. But when states start talking 430 00:25:46,720 --> 00:25:50,960 Speaker 1: about no restrictions, just life of the mother. None of 431 00:25:51,000 --> 00:25:54,440 Speaker 1: these bills have passed yet, mind you, But when they 432 00:25:54,480 --> 00:25:58,600 Speaker 1: talk about having special sessions and doing things like that, 433 00:25:58,600 --> 00:26:01,520 Speaker 1: that's the stuff that is freaking in a lot of 434 00:26:01,520 --> 00:26:05,840 Speaker 1: these people out And one of the lessons you would 435 00:26:05,840 --> 00:26:08,960 Speaker 1: think that the right has learned from the left is 436 00:26:09,680 --> 00:26:12,800 Speaker 1: when you get something you know, don't go for it 437 00:26:12,880 --> 00:26:17,240 Speaker 1: all immediately. If you really want to restrict abortion, don't 438 00:26:17,240 --> 00:26:19,359 Speaker 1: push it so hard that you lose a bunch of 439 00:26:19,359 --> 00:26:24,639 Speaker 1: elections and then actually lose the ability to restrict abortion. Um. 440 00:26:24,680 --> 00:26:27,160 Speaker 1: But I see them making the some of the concerns 441 00:26:27,200 --> 00:26:30,800 Speaker 1: making the same mistake pushing this right now. And you 442 00:26:30,840 --> 00:26:34,479 Speaker 1: can see the narratives of the insane stories about you know, 443 00:26:34,560 --> 00:26:37,800 Speaker 1: people are gonna have me arrested for miscarriages and all 444 00:26:37,840 --> 00:26:41,960 Speaker 1: this nonsense, and all this could be avoided if people 445 00:26:41,960 --> 00:26:44,159 Speaker 1: would just say, well, we've got a great bill, you know, 446 00:26:44,200 --> 00:26:47,359 Speaker 1: in are in any particular state. I know, for example, 447 00:26:47,400 --> 00:26:51,760 Speaker 1: Georgia the heartbeat bill howl even passed. A lot of 448 00:26:51,800 --> 00:26:55,040 Speaker 1: states have a heartbeat bill, and uh, you know, and 449 00:26:56,000 --> 00:26:57,880 Speaker 1: you gotta work in the yard in every single state 450 00:26:57,920 --> 00:27:03,440 Speaker 1: that has one. But um, moving further on it. And 451 00:27:03,440 --> 00:27:06,639 Speaker 1: and of course the characterization by the other side that 452 00:27:06,720 --> 00:27:11,159 Speaker 1: you are I mean, for example, the Kansas referendum, it 453 00:27:11,320 --> 00:27:14,040 Speaker 1: was not about stritching abortion. It was about taking the 454 00:27:14,200 --> 00:27:18,080 Speaker 1: power to make abortion law away from the Kansas Supreme 455 00:27:18,119 --> 00:27:22,040 Speaker 1: Court and handing it to the Kansas legislature. That's all 456 00:27:22,080 --> 00:27:26,720 Speaker 1: it was. But the other side came in. The thing 457 00:27:26,800 --> 00:27:31,480 Speaker 1: was that was the most convoluted word referendum you've ever seen. Uh. 458 00:27:31,520 --> 00:27:33,040 Speaker 1: And then the other side came in and said, this 459 00:27:33,080 --> 00:27:35,640 Speaker 1: is a vote to an abortion and people on election. 460 00:27:35,680 --> 00:27:39,359 Speaker 1: They believe that, and that's why I got shot down it. 461 00:27:39,520 --> 00:27:43,000 Speaker 1: They thought it was literally voting and making all abortions illegal. 462 00:27:44,000 --> 00:27:47,120 Speaker 1: So the other side is going to grandstand that they 463 00:27:47,119 --> 00:27:51,160 Speaker 1: are going to um do all of these things. So 464 00:27:51,240 --> 00:27:53,639 Speaker 1: now it's not the time to fight those battles. Now's 465 00:27:53,640 --> 00:27:57,320 Speaker 1: the time to win your elections. And if you really 466 00:27:57,320 --> 00:27:59,680 Speaker 1: want to and abortion it's a business. You get a 467 00:27:59,720 --> 00:28:02,520 Speaker 1: heart of bill. Every abortion clinic in your state's going 468 00:28:02,560 --> 00:28:05,800 Speaker 1: out of business because they just not have enough customers. 469 00:28:05,800 --> 00:28:07,520 Speaker 1: And it's so much better when they go into business 470 00:28:07,920 --> 00:28:10,920 Speaker 1: then when they're perceived to be shut down. Quick commercial break, 471 00:28:11,080 --> 00:28:17,760 Speaker 1: Stay tuned. What are the top issues that you're saying 472 00:28:17,880 --> 00:28:20,000 Speaker 1: right now? Right? I know you've kind of hit on 473 00:28:20,080 --> 00:28:21,720 Speaker 1: some of it, but if you know, if you're just 474 00:28:21,800 --> 00:28:23,720 Speaker 1: kind of looking at sort of like the top five 475 00:28:24,280 --> 00:28:26,760 Speaker 1: and what you're saying, you know, what are they? Well, 476 00:28:26,920 --> 00:28:35,240 Speaker 1: certainly inflation, Uh, the sense that the government is a 477 00:28:35,280 --> 00:28:39,080 Speaker 1: little bit out of control. Um. It was funny because 478 00:28:39,160 --> 00:28:42,240 Speaker 1: I was I was watching the UH Sunday Meet the 479 00:28:42,280 --> 00:28:45,280 Speaker 1: Press numbers and they threw up on the screen. Now 480 00:28:45,360 --> 00:28:48,840 Speaker 1: the average people more worried about the state of the democracy, 481 00:28:50,120 --> 00:28:53,000 Speaker 1: and and they were painting it like these are all 482 00:28:53,080 --> 00:28:57,320 Speaker 1: January six people. And I'm like, I know enough about 483 00:28:57,360 --> 00:29:00,280 Speaker 1: Poe And if that question reads like it looks like 484 00:29:00,320 --> 00:29:03,960 Speaker 1: it reads after the FBI rate to former president, they're 485 00:29:04,000 --> 00:29:06,960 Speaker 1: a Republicans saying they word about democracy too, and you 486 00:29:07,120 --> 00:29:10,760 Speaker 1: just ignored that that a piece of that poll. People 487 00:29:10,800 --> 00:29:13,480 Speaker 1: are concerned about this big government's out of control. They're 488 00:29:13,560 --> 00:29:17,320 Speaker 1: absolutely concerned. So there are people on the Republican side. 489 00:29:17,360 --> 00:29:21,200 Speaker 1: I mean, here's the thing. If you say that talking 490 00:29:21,280 --> 00:29:24,560 Speaker 1: about the election being stolen and questioning where the election 491 00:29:24,680 --> 00:29:28,760 Speaker 1: was honest is a threat to democracy, then can you 492 00:29:28,800 --> 00:29:32,240 Speaker 1: not also concede that if those people who say the 493 00:29:32,280 --> 00:29:34,960 Speaker 1: election might have been stolen it were right, a stolen 494 00:29:35,000 --> 00:29:38,560 Speaker 1: election is also a threat to democracy. I think you 495 00:29:38,600 --> 00:29:41,920 Speaker 1: can so take us through for those who are sort of, 496 00:29:42,560 --> 00:29:44,880 Speaker 1: you know, unfamiliar with how polling works, just kind of 497 00:29:44,920 --> 00:29:47,960 Speaker 1: take us through a real quick like thirty foot this 498 00:29:48,000 --> 00:29:49,760 Speaker 1: is how it's done. You don't have to you know, 499 00:29:49,600 --> 00:29:51,800 Speaker 1: and I know that you guys have your proprietary stuff, 500 00:29:51,840 --> 00:29:53,120 Speaker 1: so you know, you don't have to get into anything 501 00:29:53,120 --> 00:29:54,840 Speaker 1: like that. But just but just like the bear, the 502 00:29:54,880 --> 00:29:56,840 Speaker 1: bear basics, you know, for people to kind of understand 503 00:29:56,840 --> 00:29:59,880 Speaker 1: how it all that's right now. Um, Well, with poles, 504 00:30:00,040 --> 00:30:03,520 Speaker 1: you have to polls about what it's supposed to be. 505 00:30:03,680 --> 00:30:05,920 Speaker 1: Is it get a snapshot of where the electorate is 506 00:30:05,960 --> 00:30:11,080 Speaker 1: the day the days you're checking. Um, everybody uses different 507 00:30:11,200 --> 00:30:15,080 Speaker 1: methods to collect that data. I believe that you need 508 00:30:15,080 --> 00:30:18,720 Speaker 1: to use multiple methods because I think people live differently. 509 00:30:19,280 --> 00:30:21,160 Speaker 1: I see Pauls all the time. They just do live 510 00:30:21,240 --> 00:30:25,520 Speaker 1: calls and I I want to know what normal millennials 511 00:30:25,520 --> 00:30:28,240 Speaker 1: and gen Z's answered the number they didn't recognize and 512 00:30:28,280 --> 00:30:32,720 Speaker 1: talk to you for thirty five minutes none zero, because 513 00:30:32,720 --> 00:30:36,400 Speaker 1: they don't do that. Uh we uh we make sure 514 00:30:36,440 --> 00:30:41,000 Speaker 1: we we do calls home and cell phone. We we 515 00:30:41,000 --> 00:30:44,720 Speaker 1: we we send texts and our text are text back 516 00:30:44,760 --> 00:30:48,720 Speaker 1: and forth, not text link, so people more plantering. We 517 00:30:48,760 --> 00:30:52,720 Speaker 1: send emails, We we provide them other callbacks like I 518 00:30:52,760 --> 00:30:55,120 Speaker 1: can call back, I can take a poll. Lots of 519 00:30:55,360 --> 00:30:58,400 Speaker 1: different methods. So we we have a lot of methods 520 00:30:58,440 --> 00:31:01,920 Speaker 1: that we can ellect all our by data. Uh. We 521 00:31:01,960 --> 00:31:07,280 Speaker 1: believe in short questionnaires, uh three four or five, six 522 00:31:07,320 --> 00:31:11,160 Speaker 1: seven most we want to do because we think those 523 00:31:11,160 --> 00:31:13,400 Speaker 1: are the kind of polls that people take. The first 524 00:31:13,480 --> 00:31:17,280 Speaker 1: question every pole gets when they call a hous is 525 00:31:17,360 --> 00:31:19,760 Speaker 1: how long is this gonna take? And if your answer 526 00:31:19,920 --> 00:31:22,560 Speaker 1: something other than oh, just three minutes, oh, it's just 527 00:31:22,680 --> 00:31:26,920 Speaker 1: five questions hanging up normally far because they got lives, 528 00:31:26,920 --> 00:31:30,280 Speaker 1: they got stuff to do. So we believe short questionnaires, 529 00:31:30,360 --> 00:31:33,440 Speaker 1: but we believe in large sample sizes. We don't want 530 00:31:33,440 --> 00:31:36,680 Speaker 1: to come in your state and sample uh less than 531 00:31:37,200 --> 00:31:40,000 Speaker 1: a thousand people every state poll we put out, and 532 00:31:40,000 --> 00:31:42,320 Speaker 1: we're doing a lot of state polls this year. There 533 00:31:42,320 --> 00:31:45,360 Speaker 1: are all a thousand people getting that marginary as low 534 00:31:45,400 --> 00:31:48,160 Speaker 1: as we can get it. And so those are some 535 00:31:48,240 --> 00:31:51,479 Speaker 1: of our key factors. And and we tried to do 536 00:31:51,680 --> 00:31:55,959 Speaker 1: as little weighting as possible, which means we focus on 537 00:31:56,000 --> 00:31:59,800 Speaker 1: trying to hit those markers. If I need to find 538 00:32:00,720 --> 00:32:03,200 Speaker 1: you know, if if I'm doing a state and that's 539 00:32:03,280 --> 00:32:06,120 Speaker 1: thirty four percent African American, I need to get thirty 540 00:32:07,480 --> 00:32:11,640 Speaker 1: of my samples from African Americans. It's it's very important. 541 00:32:11,680 --> 00:32:15,040 Speaker 1: I think that a lot of minority groups are often 542 00:32:15,160 --> 00:32:18,640 Speaker 1: misrepresented because people get a small sample of them and 543 00:32:18,760 --> 00:32:21,520 Speaker 1: wait that into what's called weighting it up, which give 544 00:32:21,560 --> 00:32:24,840 Speaker 1: it a higher impact than it has. And you know 545 00:32:25,080 --> 00:32:28,240 Speaker 1: you're not getting uh, you're not treating the people that 546 00:32:28,280 --> 00:32:31,600 Speaker 1: you're polling that are small groups with respect when when 547 00:32:31,640 --> 00:32:37,560 Speaker 1: you don't actually give them this give them the representation 548 00:32:38,000 --> 00:32:40,520 Speaker 1: of the diverse opinions that would happen had you gotten 549 00:32:40,520 --> 00:32:42,880 Speaker 1: the proper am out of samples. So we're big on 550 00:32:42,920 --> 00:32:45,560 Speaker 1: getting our samples just right. We usually do the first 551 00:32:45,600 --> 00:32:47,640 Speaker 1: couple of nights and then after what we're looking see 552 00:32:47,640 --> 00:32:50,480 Speaker 1: where our holes are. Hey, you know what are we missing? Well, 553 00:32:50,520 --> 00:32:55,160 Speaker 1: you know we're missing parents with school age children who 554 00:32:55,240 --> 00:32:56,760 Speaker 1: live in this part of the state. And I say, 555 00:32:56,800 --> 00:32:59,360 Speaker 1: all right, what method there was was finding the best too? Well, 556 00:32:59,360 --> 00:33:01,120 Speaker 1: those guys say email on this part will be responding 557 00:33:01,160 --> 00:33:04,400 Speaker 1: to email best. Then let's send a ton of emails 558 00:33:04,440 --> 00:33:06,480 Speaker 1: to that part of the state for that group, and 559 00:33:06,560 --> 00:33:10,200 Speaker 1: let's make sure we get them all. And that's differently 560 00:33:10,240 --> 00:33:12,160 Speaker 1: what we don't have the static. We have to have 561 00:33:12,400 --> 00:33:15,680 Speaker 1: x many emails in this many text Now we have 562 00:33:15,840 --> 00:33:20,720 Speaker 1: to get the sample right. That's our priority. And you 563 00:33:20,760 --> 00:33:24,200 Speaker 1: know we try to just be honest and transparent because 564 00:33:24,320 --> 00:33:28,680 Speaker 1: unlike these media groups and unlike the universities, we you know, 565 00:33:29,480 --> 00:33:32,000 Speaker 1: our business is you know, we put a lot of 566 00:33:32,040 --> 00:33:34,920 Speaker 1: stuff out there for business development. And there are a 567 00:33:34,960 --> 00:33:40,960 Speaker 1: lot of industry groups, um companies, funds, you know, people 568 00:33:41,040 --> 00:33:45,440 Speaker 1: that run hedge funds, high net worth individuals who literally 569 00:33:45,480 --> 00:33:49,200 Speaker 1: pay us for the services of providing them honest polls 570 00:33:49,240 --> 00:33:51,920 Speaker 1: that are not skewed by parties or campaigns. Well, we 571 00:33:51,960 --> 00:33:55,200 Speaker 1: appreciate the work that you're doing, and everyone continue to 572 00:33:55,240 --> 00:33:58,040 Speaker 1: look for the trafalgar groups work. I know you came 573 00:33:58,040 --> 00:34:01,040 Speaker 1: out with the poll h the other day with IO Pennsylvania, 574 00:34:01,120 --> 00:34:05,440 Speaker 1: So everybody keep your eyes on the work that Robert's doing. Robert, 575 00:34:05,480 --> 00:34:07,560 Speaker 1: thanks so much for joining The Truth with Lisa Booth. 576 00:34:07,600 --> 00:34:10,160 Speaker 1: I really appreciate your time. It's great to be here. 577 00:34:10,200 --> 00:34:13,880 Speaker 1: And I like truth el that's catchy. I like it. 578 00:34:14,840 --> 00:34:17,239 Speaker 1: You know, it kind of rhymes, it goes well. So well, 579 00:34:17,280 --> 00:34:19,640 Speaker 1: I appreciate the work you're doing, and uh, we'll we'll 580 00:34:19,640 --> 00:34:34,759 Speaker 1: continue to pay attention. So thanks so much. I appreciate it. Okay, 581 00:34:23,520 --> 00:34:38,279 Speaker 1: good bye. How even polls now are skewed? How even 582 00:34:38,320 --> 00:34:40,920 Speaker 1: polls now our bias? It seems that bias is steeped 583 00:34:40,960 --> 00:34:43,920 Speaker 1: into you know, pretty much every aspect of her life 584 00:34:44,120 --> 00:34:46,640 Speaker 1: and uh so'm sorry. So it was an interesting conversation 585 00:34:46,640 --> 00:34:49,440 Speaker 1: with Robert always looked to their polls. You know, I 586 00:34:49,440 --> 00:34:51,920 Speaker 1: trust the guy. I think it's a really trustworthy guy. 587 00:34:51,960 --> 00:34:54,040 Speaker 1: And he's a history of getting fixed right, which is 588 00:34:54,080 --> 00:34:56,520 Speaker 1: hard to do, particularly when we learned the pooling business. 589 00:34:57,000 --> 00:34:59,840 Speaker 1: So appreciate you for listening to the show. One of 590 00:34:59,840 --> 00:35:02,080 Speaker 1: the thank John Cassio and my producer for putting the 591 00:35:02,080 --> 00:35:05,160 Speaker 1: show together. Tune in every Monday every Thursday, but you 592 00:35:05,160 --> 00:35:07,480 Speaker 1: can also listen throughout the week On The Truth with 593 00:35:07,600 --> 00:35:08,200 Speaker 1: Lisa Booth