1 00:00:01,080 --> 00:00:04,520 Speaker 1: Welcome to stuff you should know from house stuff works 2 00:00:04,559 --> 00:00:13,480 Speaker 1: dot com. Punky chucking, punky chucking, punky chucking. That's right, chuck. 3 00:00:13,520 --> 00:00:16,360 Speaker 1: He and welcome to the podcast. I'm Josh Clark. Clearly, 4 00:00:16,440 --> 00:00:20,920 Speaker 1: Chuck Bryant's here, and uh, let's talk about punkin Chunking. 5 00:00:21,000 --> 00:00:23,680 Speaker 1: I guess you just kind of forced our hand, Chuck. Yes, 6 00:00:23,760 --> 00:00:27,120 Speaker 1: the Road to Punkin Chunking and punkin Chunking. So that's on. 7 00:00:27,440 --> 00:00:30,360 Speaker 1: That's on Science Channel a p m. Eastern Time on 8 00:00:30,480 --> 00:00:34,640 Speaker 1: Thanksgiving night. Yeah, you can see some pumpkins get chunk punkins, 9 00:00:34,640 --> 00:00:38,479 Speaker 1: get chunks punkins. Yeah, okay again Science Channel. The Road 10 00:00:38,520 --> 00:00:41,440 Speaker 1: to punkin Chunkin starts at eight pm Eastern Time. Punkin 11 00:00:41,520 --> 00:00:45,960 Speaker 1: chunk In Itself starts at nine Thanksgiving night. Yes, Science 12 00:00:46,040 --> 00:00:49,919 Speaker 1: Channel on with the show. Yeah, chuck, Um, have you 13 00:00:49,960 --> 00:00:54,120 Speaker 1: ever belonged to a population? No, man, I'm like, I'm 14 00:00:54,160 --> 00:00:57,520 Speaker 1: totally independent. Screw population. You're like that guy who lives 15 00:00:57,560 --> 00:01:00,200 Speaker 1: in the commune, right right, Yeah, Well the joke's on him, 16 00:01:00,280 --> 00:01:05,160 Speaker 1: because a commune constitutes a population. Um, this sounds kind 17 00:01:05,160 --> 00:01:08,880 Speaker 1: of boring, and you would think it is how population works. 18 00:01:08,920 --> 00:01:11,640 Speaker 1: It actually started to pick up. Actually didn't know what 19 00:01:11,680 --> 00:01:13,839 Speaker 1: it was even gonna be. When I saw how population works, 20 00:01:13,880 --> 00:01:16,000 Speaker 1: I was like, what you know, it's awesome. This was 21 00:01:16,080 --> 00:01:19,160 Speaker 1: my idea, this article was. I pinched it. Why didn't 22 00:01:19,160 --> 00:01:22,360 Speaker 1: they let you write it? I don't know, jerks, I know, 23 00:01:22,400 --> 00:01:24,640 Speaker 1: but the grabst did a good job with it. Oh yeah, 24 00:01:24,640 --> 00:01:27,440 Speaker 1: the grabs is always good. Yeah, class, that's ed grab Ainowsky. 25 00:01:27,520 --> 00:01:34,800 Speaker 1: By the way, right, So, UM, human beings marriage, human 26 00:01:34,840 --> 00:01:40,880 Speaker 1: beings tend to um congregate. Yes, we segregate. Interestingly, that 27 00:01:41,080 --> 00:01:46,319 Speaker 1: is an excellent You just blew my mind. Good lord chuck. Um. Well, 28 00:01:46,400 --> 00:01:48,400 Speaker 1: let's get back to what I was saying. Unless you 29 00:01:48,440 --> 00:01:51,400 Speaker 1: want to go on a segregation we'll get on that later. Um, 30 00:01:51,680 --> 00:01:56,000 Speaker 1: humans congregate and segregate. But let's talk about congregation and that. 31 00:01:56,520 --> 00:01:59,760 Speaker 1: UM most of the time, I would say, our early 32 00:02:00,040 --> 00:02:05,200 Speaker 1: early ancestors, uh, and probably even other species congregate because 33 00:02:05,240 --> 00:02:08,640 Speaker 1: there's safety and numbers and it helps like with farming, 34 00:02:08,720 --> 00:02:11,919 Speaker 1: collecting water and food power numbers. But even before farming, 35 00:02:12,120 --> 00:02:16,320 Speaker 1: um h hunter gatherers lived in bands. I think thirty 36 00:02:16,440 --> 00:02:18,720 Speaker 1: was about tops. They figured out somewhere along the way 37 00:02:18,880 --> 00:02:21,720 Speaker 1: that groups of thirty or groups of more than thirty, 38 00:02:21,960 --> 00:02:24,520 Speaker 1: they're tending to be a lot more hostility and UM 39 00:02:24,720 --> 00:02:27,080 Speaker 1: in a group problems. Have you ever tried to kill 40 00:02:27,120 --> 00:02:29,880 Speaker 1: a mass it on by yourself? That's another good point too. 41 00:02:30,120 --> 00:02:37,959 Speaker 1: There's cooperation masted on. Um. There's um. If if let's say, 42 00:02:38,000 --> 00:02:40,680 Speaker 1: if you are farming and your crop fails, well you're 43 00:02:40,680 --> 00:02:46,280 Speaker 1: not standing there like, well I'm in trouble. You can say, hey, neighbor, um, 44 00:02:46,360 --> 00:02:49,920 Speaker 1: I'll totally give you favors of some variety if you 45 00:02:50,000 --> 00:02:52,000 Speaker 1: will let me have some of your grain. Right, I'll 46 00:02:52,000 --> 00:02:55,359 Speaker 1: give you a chicken. Let's say, sure, you can trade. Yeah, 47 00:02:55,720 --> 00:02:58,840 Speaker 1: there's a lot of reasons people live together. So it's 48 00:02:58,919 --> 00:03:03,520 Speaker 1: my theory that pepe will aggregate together naturally. Yes, And 49 00:03:03,560 --> 00:03:05,880 Speaker 1: then there are people out there who get their jollies 50 00:03:06,000 --> 00:03:09,959 Speaker 1: by studying these groups of people. They're called demographer demographers. 51 00:03:10,000 --> 00:03:12,960 Speaker 1: So we have populations natural or otherwise. And let's say 52 00:03:12,960 --> 00:03:17,320 Speaker 1: a natural population, uh today are people who live in 53 00:03:17,560 --> 00:03:21,360 Speaker 1: a certain state Georgians. That's where we are, So that 54 00:03:21,440 --> 00:03:25,639 Speaker 1: you have natural populations, and demographers study um right um, 55 00:03:25,760 --> 00:03:29,960 Speaker 1: and they look at things like say, how many people 56 00:03:30,000 --> 00:03:33,600 Speaker 1: in this natural population are Republicans or Democrat, or how 57 00:03:33,600 --> 00:03:39,240 Speaker 1: many are Caucasian, or how many have um, how many 58 00:03:39,480 --> 00:03:42,120 Speaker 1: live below the poverty line. All kinds of things you 59 00:03:42,160 --> 00:03:45,920 Speaker 1: can study right by looking at a population and are 60 00:03:45,680 --> 00:03:49,600 Speaker 1: are is this? Are these groups segregated like you brought up? 61 00:03:50,080 --> 00:03:53,160 Speaker 1: You know, like if you study, uh, where different races 62 00:03:53,240 --> 00:03:55,839 Speaker 1: are living? Are they living mingling? If so, then that's 63 00:03:55,880 --> 00:03:59,640 Speaker 1: probably a fairly harmonious place hopefully. If not, why are 64 00:03:59,720 --> 00:04:02,640 Speaker 1: they living apart? How do we fix this? Because it's 65 00:04:02,680 --> 00:04:07,520 Speaker 1: probably a problem. Who knows, but yes, so demographers study 66 00:04:07,560 --> 00:04:12,400 Speaker 1: populations natural or otherwise. Right. The problem is is you 67 00:04:13,120 --> 00:04:16,080 Speaker 1: very few people have the ability to hover over the 68 00:04:16,120 --> 00:04:22,320 Speaker 1: earth and use super binocular vision to study populations by side. 69 00:04:22,600 --> 00:04:25,840 Speaker 1: Very few people. Yeah, like three or four I think tops. 70 00:04:25,920 --> 00:04:30,240 Speaker 1: Uh so does that count as a statistic I think so? Okay, Richard, 71 00:04:30,760 --> 00:04:33,680 Speaker 1: um so measuring populations after you can talk about how 72 00:04:33,680 --> 00:04:35,960 Speaker 1: do we actually determine this kind of thing? Yeah, that 73 00:04:36,080 --> 00:04:38,320 Speaker 1: was my sec That was that was a good segue. 74 00:04:38,400 --> 00:04:42,719 Speaker 1: There's a kind of way, josh one is um by 75 00:04:42,960 --> 00:04:47,080 Speaker 1: counting them literally counting them like it's counting every single person, right, 76 00:04:47,120 --> 00:04:50,640 Speaker 1: and that is called complete enumeration. Yeah. Remember we talked 77 00:04:50,640 --> 00:04:53,040 Speaker 1: about that poor guy who was killed or possibly kill 78 00:04:53,120 --> 00:04:55,960 Speaker 1: himself in Kentucky. The census taker. Right, Oh, I didn't 79 00:04:56,000 --> 00:04:58,200 Speaker 1: know that suicide was a possibility. There. I got a 80 00:04:58,320 --> 00:05:01,279 Speaker 1: cryptic email from somebody never followed up on that said 81 00:05:01,360 --> 00:05:05,520 Speaker 1: that he identified himself as a doctor and I think 82 00:05:05,600 --> 00:05:08,840 Speaker 1: said that he was part of the group that was 83 00:05:09,279 --> 00:05:13,640 Speaker 1: the medical examination team and said that they suspected, strongly 84 00:05:13,680 --> 00:05:16,560 Speaker 1: suspected suicide. My problem with it is is how do 85 00:05:16,600 --> 00:05:21,000 Speaker 1: you bind yourself in duct tape? How do you bind 86 00:05:21,000 --> 00:05:27,000 Speaker 1: your own risks and duct tape? Okay, um, So my 87 00:05:27,080 --> 00:05:29,000 Speaker 1: point is, wow, he threw me off of that one. 88 00:05:29,240 --> 00:05:33,160 Speaker 1: My point is that he was called an enumerator, yes, 89 00:05:33,600 --> 00:05:36,479 Speaker 1: literally counter, and that's the people who worked for the 90 00:05:36,520 --> 00:05:39,520 Speaker 1: census whenever they had their their drive and they count 91 00:05:39,960 --> 00:05:42,120 Speaker 1: and that's one way to determine it. Well, let's talk 92 00:05:42,160 --> 00:05:45,640 Speaker 1: about the census. It's gone on every ten years since, right, 93 00:05:45,960 --> 00:05:47,719 Speaker 1: And the reason they do it every ten years because 94 00:05:47,760 --> 00:05:49,640 Speaker 1: it's a real pain in the asked to count every 95 00:05:49,680 --> 00:05:51,880 Speaker 1: person in America. Yeah, the real reason they do it 96 00:05:51,960 --> 00:05:53,919 Speaker 1: so they can Well there's a lot of reasons, but 97 00:05:56,279 --> 00:06:00,480 Speaker 1: that is the reason why anyone's ever conducted a sense. Yeah. Well, 98 00:06:00,520 --> 00:06:03,800 Speaker 1: plus they they determine the number of House representatives for 99 00:06:03,839 --> 00:06:06,039 Speaker 1: your state based on population. Stuff like that. Oh yeah, 100 00:06:06,040 --> 00:06:08,360 Speaker 1: there's that too. But you know, come on, Texas, did 101 00:06:08,400 --> 00:06:13,960 Speaker 1: you know that um that that the census information is 102 00:06:14,040 --> 00:06:17,000 Speaker 1: kept is kept secret for seventy two years? Yeah, aside 103 00:06:17,000 --> 00:06:20,000 Speaker 1: from the numbers, I believe the public cannot see that 104 00:06:20,040 --> 00:06:22,440 Speaker 1: information for seventy two years. Right, what do I seventy two? 105 00:06:22,480 --> 00:06:24,960 Speaker 1: That's odd? It is odd. I wonder if that was 106 00:06:25,000 --> 00:06:28,400 Speaker 1: the average lifespan at the time or something. Dude, that's 107 00:06:28,400 --> 00:06:31,880 Speaker 1: got to be it. I'll bet you're right. Okay. The 108 00:06:31,920 --> 00:06:36,120 Speaker 1: other way, Josh, is to uh do something called sampling, 109 00:06:36,400 --> 00:06:40,440 Speaker 1: and that is when um statticians use a mathematical formula 110 00:06:40,520 --> 00:06:45,479 Speaker 1: to determine the minimum number of people that must be counted, 111 00:06:45,560 --> 00:06:49,880 Speaker 1: and then they multiply that out and basically end up 112 00:06:49,880 --> 00:06:52,880 Speaker 1: getting a full population. And sometimes I did, I don't 113 00:06:52,920 --> 00:06:56,039 Speaker 1: know this. That's even more accurate than an actual head count. 114 00:06:56,920 --> 00:06:58,919 Speaker 1: You see that margin of error, it's like plus or 115 00:06:58,920 --> 00:07:01,279 Speaker 1: minus for sent Yeah, you gotta have a margin of 116 00:07:01,360 --> 00:07:04,040 Speaker 1: error there whenever you're sampling, right, because you're not actually 117 00:07:04,120 --> 00:07:07,160 Speaker 1: going around asking every single person in America are you 118 00:07:07,279 --> 00:07:10,160 Speaker 1: left handed? To determine how many people are left handed? 119 00:07:11,120 --> 00:07:13,080 Speaker 1: But let's say you have a population of a thousand, 120 00:07:13,360 --> 00:07:18,000 Speaker 1: and some statisticians been like, you need a hundred do it, 121 00:07:18,040 --> 00:07:21,880 Speaker 1: but do your egghead voice. Yeah, you need a hundred 122 00:07:21,920 --> 00:07:24,640 Speaker 1: and fifty people. The hundred and fifty people and that 123 00:07:24,680 --> 00:07:26,760 Speaker 1: are left handed, and you can just multiply that out 124 00:07:26,760 --> 00:07:29,920 Speaker 1: to determine that there are, in fact how many people, 125 00:07:30,280 --> 00:07:33,720 Speaker 1: let's say, ten percent of the population of the population. Right, 126 00:07:33,800 --> 00:07:37,840 Speaker 1: but your sample as perfect. Your sample has to be 127 00:07:37,880 --> 00:07:42,440 Speaker 1: a random sample to be an effective sample. Yeah, and 128 00:07:42,480 --> 00:07:44,280 Speaker 1: you know how they used to do that, Uh huh. 129 00:07:44,480 --> 00:07:46,280 Speaker 1: Used to just pick it out of the phone book. Oh, 130 00:07:46,320 --> 00:07:49,679 Speaker 1: I know and call people I know. And that makes 131 00:07:49,720 --> 00:07:52,480 Speaker 1: sense to a certain extent. No, well, back then it 132 00:07:52,520 --> 00:07:54,360 Speaker 1: made a little more sense. I would think it made 133 00:07:54,440 --> 00:07:57,400 Speaker 1: it made less sense, especially if you're talking like nineteen 134 00:07:57,440 --> 00:07:59,640 Speaker 1: fifty years. Well, it depends on what year. I'd say, 135 00:07:59,640 --> 00:08:01,840 Speaker 1: in the night, teen eighties, it was probably a good way. 136 00:08:01,960 --> 00:08:05,119 Speaker 1: But now there are cell phones. People in college probably 137 00:08:05,120 --> 00:08:07,120 Speaker 1: don't have a phone. Poor people who don't have phones 138 00:08:07,160 --> 00:08:08,920 Speaker 1: at all, people who don't have phone. Sure, so that's 139 00:08:08,960 --> 00:08:12,040 Speaker 1: not a very good way. Because what about freight train 140 00:08:12,320 --> 00:08:15,920 Speaker 1: writers of America? What's that they don't have phones? Yeah, 141 00:08:16,000 --> 00:08:18,600 Speaker 1: good point. Yeah, they're not allowed. I don't think they 142 00:08:18,640 --> 00:08:23,000 Speaker 1: want them. So sampling is a little harder than it seems. Yeah, right, 143 00:08:23,120 --> 00:08:25,960 Speaker 1: especially coming up with a random population SANDOM sample of 144 00:08:26,000 --> 00:08:29,080 Speaker 1: the population. Um. But okay, so so far we've talked 145 00:08:29,080 --> 00:08:33,920 Speaker 1: about people and where they live. There's other ways to 146 00:08:33,960 --> 00:08:37,280 Speaker 1: define a population. There's other attributes that people have that 147 00:08:37,360 --> 00:08:39,760 Speaker 1: we use to lump into population. It's not just a 148 00:08:39,800 --> 00:08:43,400 Speaker 1: geography when people think, um, populations, it's not just a 149 00:08:43,440 --> 00:08:48,520 Speaker 1: city population or state. Yes, what an age? You have 150 00:08:48,559 --> 00:08:54,240 Speaker 1: a population of age or continent, a demographic? What else? Location? 151 00:08:54,280 --> 00:08:57,360 Speaker 1: Of course, socio economic Well population, let's talk about age. 152 00:08:57,360 --> 00:08:59,720 Speaker 1: Why would you even want to know age? Who cares? 153 00:09:00,000 --> 00:09:02,280 Speaker 1: People are old, people are young? Whatever? Right, Well, there's 154 00:09:02,280 --> 00:09:04,680 Speaker 1: a lot of factors like, um, take the baby boom 155 00:09:04,679 --> 00:09:07,160 Speaker 1: for instance, after World War Two, all these babies were born, 156 00:09:07,240 --> 00:09:10,520 Speaker 1: so there was a bulge in the population, and I'd 157 00:09:10,559 --> 00:09:12,480 Speaker 1: just like saying the word bulge. You got to do 158 00:09:12,520 --> 00:09:16,240 Speaker 1: the air quote a quote. So, um, what that will 159 00:09:16,240 --> 00:09:17,959 Speaker 1: show them then is wow, we got a bulge here. 160 00:09:18,000 --> 00:09:22,720 Speaker 1: So that means probably in to sixty years, there's gonna 161 00:09:22,720 --> 00:09:26,079 Speaker 1: be some serious buying power. Let's start borrowing as much 162 00:09:26,120 --> 00:09:28,480 Speaker 1: money as we can right now. But it also means 163 00:09:28,520 --> 00:09:32,400 Speaker 1: in seventy plus years that they they may be a 164 00:09:32,440 --> 00:09:35,640 Speaker 1: medical burden and a burden on social security. So let's 165 00:09:35,679 --> 00:09:37,720 Speaker 1: start borrowing as much money as we can right now, 166 00:09:39,000 --> 00:09:42,600 Speaker 1: same same result there. I like that, and we'll get 167 00:09:42,640 --> 00:09:46,679 Speaker 1: to bulges again in a little bit. But let's move on. 168 00:09:46,720 --> 00:09:49,720 Speaker 1: Like you said, socioeconomic data, right, yeah, what what? Why 169 00:09:49,760 --> 00:09:51,520 Speaker 1: would they want to do this job? This one I found. 170 00:09:51,520 --> 00:09:54,760 Speaker 1: I find this the most interesting of all data. You 171 00:09:54,800 --> 00:09:57,920 Speaker 1: can look at a bunch of people who are maybe 172 00:09:58,000 --> 00:10:02,360 Speaker 1: related geographically, right, um, but other than that, aren't related 173 00:10:02,400 --> 00:10:05,840 Speaker 1: in any other way. Uh, and all of them suddenly 174 00:10:06,040 --> 00:10:09,800 Speaker 1: have this horrible cancer and they're just so happens to 175 00:10:09,840 --> 00:10:14,480 Speaker 1: be some manufacturer or thereby what did you say, high 176 00:10:14,480 --> 00:10:18,000 Speaker 1: tension wires, which has been proven I think to not 177 00:10:18,080 --> 00:10:20,840 Speaker 1: actually have any effect on people, not in my buddy. 178 00:10:21,360 --> 00:10:24,240 Speaker 1: Um so uh, Now, all of a sudden, you have 179 00:10:24,280 --> 00:10:27,920 Speaker 1: this information thanks to your demographer friend who went and 180 00:10:27,960 --> 00:10:32,240 Speaker 1: collected it, and um, you can say, okay, paint factory, 181 00:10:32,720 --> 00:10:35,920 Speaker 1: you guys better start giving away some free paint, yeah, 182 00:10:36,080 --> 00:10:40,640 Speaker 1: or we're gonna sue you. Race. That's a little little 183 00:10:40,679 --> 00:10:44,280 Speaker 1: more hinky because technically there is no such thing as 184 00:10:44,320 --> 00:10:48,160 Speaker 1: any difference in different races. I remember watching MTV years 185 00:10:48,200 --> 00:10:51,560 Speaker 1: and years and years ago, and um, the VJ was 186 00:10:51,920 --> 00:10:55,120 Speaker 1: interviewing the Beastie Boys and he was like, Mike D, 187 00:10:55,320 --> 00:10:57,800 Speaker 1: I hear you're dating a black girl. You know, what's 188 00:10:57,840 --> 00:11:00,559 Speaker 1: it like dating somebody from a different race? Which is 189 00:11:00,600 --> 00:11:03,040 Speaker 1: just an asinine question to begin with, But I remember 190 00:11:03,080 --> 00:11:05,760 Speaker 1: Mike D going there's only one race, the human race, 191 00:11:06,240 --> 00:11:10,040 Speaker 1: And I was like, huh so that that was clearly 192 00:11:10,080 --> 00:11:12,880 Speaker 1: before he was down with the Ione or no, that 193 00:11:12,920 --> 00:11:16,240 Speaker 1: was ad Rock. Sorry, yeah, ad Rocks down with the 194 00:11:16,240 --> 00:11:18,440 Speaker 1: IONI Yeah they're divorced though, so he's not down with 195 00:11:18,440 --> 00:11:21,559 Speaker 1: her anymore, poor Ione. Uh So, Yeah, race is a 196 00:11:21,640 --> 00:11:25,240 Speaker 1: little hinky, but you can't actually determine some um useful 197 00:11:25,280 --> 00:11:28,640 Speaker 1: things when you study populations of race because of like, 198 00:11:28,880 --> 00:11:30,719 Speaker 1: you know, it's important for people to be involved in 199 00:11:30,760 --> 00:11:34,120 Speaker 1: their culture. Yeah, and and to hang onto that for sure. 200 00:11:34,200 --> 00:11:37,439 Speaker 1: I guess racial profiling again. I don't know if I 201 00:11:37,480 --> 00:11:39,160 Speaker 1: should say again or not, but it's such a hot 202 00:11:39,160 --> 00:11:44,120 Speaker 1: button issue that yeah, I don't know. We need to 203 00:11:44,160 --> 00:11:46,880 Speaker 1: talk about it collectively. That's my answer for everything. Everybody 204 00:11:46,880 --> 00:11:49,439 Speaker 1: needs to get together and decide what we want to do. Okay, well, 205 00:11:49,480 --> 00:11:50,960 Speaker 1: the other thing with race so is if there's a 206 00:11:51,000 --> 00:11:53,360 Speaker 1: medical problem that's specific to that race, that can help 207 00:11:53,400 --> 00:12:19,640 Speaker 1: out that exactly. Josh alright, So, Chuck, We've got all 208 00:12:19,640 --> 00:12:24,560 Speaker 1: these different factors, attributes, variables. We've used the word demographer 209 00:12:24,640 --> 00:12:30,080 Speaker 1: several times. Um, so we know that people study populations. 210 00:12:30,679 --> 00:12:32,800 Speaker 1: One of the reasons why we study populations is to 211 00:12:33,240 --> 00:12:37,280 Speaker 1: see how big it's getting. And I gotta tell you, buddy, 212 00:12:37,880 --> 00:12:41,679 Speaker 1: the human population is kind of exploded on this planet 213 00:12:41,840 --> 00:12:44,320 Speaker 1: in the last several thousand years. Yeah, but you know 214 00:12:44,320 --> 00:12:46,240 Speaker 1: what they were reading these stats. There were a lot 215 00:12:46,240 --> 00:12:50,559 Speaker 1: more people here way back when than I thought. Yeah. Again, 216 00:12:51,000 --> 00:12:56,160 Speaker 1: favorite book of all time, Um, Charles C. Man. Yes, 217 00:12:56,640 --> 00:13:00,000 Speaker 1: he basically points out that there is probably a hundred 218 00:13:00,120 --> 00:13:03,120 Speaker 1: million people on the North American or on the in 219 00:13:03,200 --> 00:13:07,840 Speaker 1: the America's uh in that's awesome, Yeah, which is a 220 00:13:07,840 --> 00:13:11,000 Speaker 1: fifth of the world population is way more than anyone thought. 221 00:13:11,559 --> 00:13:15,880 Speaker 1: And the reason why is because Columbus shows up. Smallpox 222 00:13:16,000 --> 00:13:20,079 Speaker 1: just ravages both continents, and by the time the European 223 00:13:20,120 --> 00:13:24,320 Speaker 1: settlers start coming for real, Uh, the places decimated. It 224 00:13:24,320 --> 00:13:26,319 Speaker 1: seems like there's nobody there. Right. Well, he had the 225 00:13:26,320 --> 00:13:28,720 Speaker 1: whole genocide. Two things you ever know about that Columbus 226 00:13:29,080 --> 00:13:31,640 Speaker 1: I hear um his men used to like sharpen their 227 00:13:31,760 --> 00:13:37,360 Speaker 1: knives on like the skulls of live um live natives. Well, 228 00:13:37,400 --> 00:13:40,720 Speaker 1: there's the because genocide we talked about later on Um 229 00:13:40,760 --> 00:13:45,400 Speaker 1: in the article. But there's uh speculation that Columbus may 230 00:13:45,440 --> 00:13:48,000 Speaker 1: have been responsible for like the worst mass genocide in 231 00:13:48,080 --> 00:13:53,840 Speaker 1: human history by completely wiping out the Tano Taino Indian people. 232 00:13:54,520 --> 00:13:56,680 Speaker 1: And that was in Hispanielo, which is modern day I 233 00:13:56,720 --> 00:14:00,000 Speaker 1: think Haiti and Dominican Republic. And they some people say 234 00:14:00,040 --> 00:14:01,920 Speaker 1: there were only like five thousand of them, and some 235 00:14:01,920 --> 00:14:04,920 Speaker 1: people say there as many as fifteen million at the 236 00:14:04,960 --> 00:14:09,400 Speaker 1: time that were decimated to about two thousand. Decimated through 237 00:14:09,480 --> 00:14:12,880 Speaker 1: violence or through disease. Yeah, well through violence, because Columbus 238 00:14:12,880 --> 00:14:14,920 Speaker 1: came over, set up a camp in Hispaniola for about 239 00:14:14,960 --> 00:14:19,080 Speaker 1: forty people, and then left, came back on trip number 240 00:14:19,120 --> 00:14:22,680 Speaker 1: two and found that the Indian tribe there had killed 241 00:14:22,720 --> 00:14:25,360 Speaker 1: all those people. So he went on to kill crazy 242 00:14:25,480 --> 00:14:30,360 Speaker 1: rampage basically and completely wiped out the population. And they're 243 00:14:30,360 --> 00:14:31,960 Speaker 1: saying it may have been like double the size of 244 00:14:32,000 --> 00:14:36,840 Speaker 1: the Holocaust. So Happy Columbus Day, everybody, seriously, but we 245 00:14:36,880 --> 00:14:39,040 Speaker 1: do mention that because genocide is is a way that 246 00:14:39,120 --> 00:14:43,960 Speaker 1: a population can change rapidly. Well, let's talk about population growth. Yes, 247 00:14:44,200 --> 00:14:46,840 Speaker 1: all right, so I guess about ten thousand BC, they 248 00:14:46,960 --> 00:14:50,920 Speaker 1: estimate that there's between one and ten million humans. So 249 00:14:51,000 --> 00:14:53,840 Speaker 1: we're starting to slowly grow because by one thousand BC 250 00:14:53,920 --> 00:14:58,000 Speaker 1: there's fifty million, and then by six hundred CE UM 251 00:14:58,440 --> 00:15:00,920 Speaker 1: we're at two mill And see that's a lot more 252 00:15:00,960 --> 00:15:03,760 Speaker 1: than I thought. Yeah there would be at the time. Yeah, 253 00:15:04,080 --> 00:15:06,920 Speaker 1: I think there was about five hundred million in the 254 00:15:06,960 --> 00:15:11,280 Speaker 1: mid fifteenth century, so um, let's go. But let's say 255 00:15:11,280 --> 00:15:14,640 Speaker 1: there's a five million in the mid fifteenth century. The 256 00:15:14,720 --> 00:15:19,320 Speaker 1: twentieth century, the industrial revolutions happened, there's been great leaps 257 00:15:19,040 --> 00:15:23,240 Speaker 1: in science and UM medicine. That's when populations really grow 258 00:15:23,360 --> 00:15:25,800 Speaker 1: is during those big booms. Yeah, because it lends itself 259 00:15:25,840 --> 00:15:31,120 Speaker 1: to fertility, higher and fertility and UM longer lifespans, good 260 00:15:31,160 --> 00:15:34,240 Speaker 1: times breed kids. So the twentieth century hits were at 261 00:15:34,280 --> 00:15:39,680 Speaker 1: one point five billion people and then this century the 262 00:15:39,720 --> 00:15:44,200 Speaker 1: population of the world has quadruple. Yeah, and with like 263 00:15:44,240 --> 00:15:46,640 Speaker 1: six billions. I know that that's it sounded like there 264 00:15:46,640 --> 00:15:48,920 Speaker 1: should have been a drum roll there, but there was 265 00:15:49,040 --> 00:15:51,440 Speaker 1: by that Jerry mnd have put one in there, our producer, Jerry, 266 00:15:51,440 --> 00:15:54,479 Speaker 1: we'll find out later. Uh, And Josh, you are projecting. 267 00:15:54,600 --> 00:15:58,160 Speaker 1: The U. S CSUS Bureau projects that by there will 268 00:15:58,200 --> 00:16:01,440 Speaker 1: be ten billion people. Right. So the reason for this 269 00:16:01,800 --> 00:16:06,320 Speaker 1: is what we call the malthusisant growth model. Mouth. This 270 00:16:06,560 --> 00:16:12,160 Speaker 1: was a eighteenth century clergyman, Thomas huh he uh he actually, 271 00:16:12,840 --> 00:16:16,840 Speaker 1: I guess inadvertently became one of the great economic theorists, 272 00:16:17,520 --> 00:16:22,160 Speaker 1: and he figured out that population grows exponentially. Right, So 273 00:16:22,200 --> 00:16:24,360 Speaker 1: if you have one million people and they have enough kids, 274 00:16:24,400 --> 00:16:27,240 Speaker 1: double the population. But the next generation you have four 275 00:16:27,280 --> 00:16:29,720 Speaker 1: million people. So in one full generation you've gone from 276 00:16:29,760 --> 00:16:34,080 Speaker 1: one million to four million people. Right. Yeah, that's that's big, 277 00:16:35,080 --> 00:16:39,160 Speaker 1: especially when the planet is finite in size and we 278 00:16:39,320 --> 00:16:42,160 Speaker 1: don't have the ability to go colonize other planets yet. Right. 279 00:16:42,360 --> 00:16:46,640 Speaker 1: But it's not necessarily that incremental and steady because of 280 00:16:46,760 --> 00:16:50,440 Speaker 1: what we talked about, which are bulges or spikes and 281 00:16:50,560 --> 00:16:57,200 Speaker 1: bottlenecks like genocide. Right. Yeah, so it doesn't always grow steadily. 282 00:16:57,240 --> 00:17:00,280 Speaker 1: And actually, Chuck, if you heard of the replacement right now, 283 00:17:00,480 --> 00:17:04,879 Speaker 1: the replacement rate is it's how many kids a woman 284 00:17:05,040 --> 00:17:10,080 Speaker 1: has to have to have a high uh statistical probability 285 00:17:10,080 --> 00:17:13,800 Speaker 1: of having a daughter, so that she in essence replaces herself. 286 00:17:14,640 --> 00:17:16,879 Speaker 1: And right now it's two point three three is the 287 00:17:16,920 --> 00:17:20,280 Speaker 1: replacement rate worldwide, And the point of it is to 288 00:17:20,359 --> 00:17:24,520 Speaker 1: trend towards zero population growth. Right, So for every woman 289 00:17:24,560 --> 00:17:28,640 Speaker 1: who dies, she has a daughter that can reproduce and 290 00:17:28,640 --> 00:17:31,080 Speaker 1: and continue on and continue on and continue on. So 291 00:17:31,119 --> 00:17:34,560 Speaker 1: you have overall as many people dying as they're being born. 292 00:17:34,880 --> 00:17:40,280 Speaker 1: So there's no strain, right, and there's also no um dearth. Well, 293 00:17:40,320 --> 00:17:42,880 Speaker 1: it's equilibrium that remot. Reading this reminded me of when 294 00:17:42,880 --> 00:17:45,399 Speaker 1: we did our Big Econ audio book. It's kind of 295 00:17:45,600 --> 00:17:50,440 Speaker 1: population kind of wants to seek equilibrium. I think, like, uh, 296 00:17:50,680 --> 00:17:53,680 Speaker 1: just like economics does and now, and it doesn't always 297 00:17:53,720 --> 00:17:59,199 Speaker 1: happen uh organically, I should say, it probably rarely happens organically. Well, 298 00:17:59,280 --> 00:18:01,639 Speaker 1: let's think about um, like you said, the baby boom, 299 00:18:02,280 --> 00:18:07,360 Speaker 1: post war success in in Europe and um, the US 300 00:18:07,359 --> 00:18:11,320 Speaker 1: and Canada, I guess led to um a huge boom 301 00:18:11,320 --> 00:18:15,320 Speaker 1: in the population. Nobody went to war to grow the population. 302 00:18:15,359 --> 00:18:17,600 Speaker 1: It was just an indirect effect. So all of a 303 00:18:17,600 --> 00:18:21,200 Speaker 1: sudden we had a population spike that created a bulge 304 00:18:21,400 --> 00:18:23,920 Speaker 1: bulge if you will, things can go the other way 305 00:18:23,960 --> 00:18:27,160 Speaker 1: to which is a bottleneck. Right. Yeah, and that's well 306 00:18:27,200 --> 00:18:29,760 Speaker 1: we and I've got If I see genocide one more time, 307 00:18:30,240 --> 00:18:32,000 Speaker 1: we should do a podcast in genocide. I wonder if 308 00:18:32,000 --> 00:18:34,960 Speaker 1: there's a drinking game where every time I say genocide, genocide, 309 00:18:35,080 --> 00:18:40,720 Speaker 1: drink um. Famine, disease. Uh, something called the plague I 310 00:18:40,760 --> 00:18:43,000 Speaker 1: think wiped out like half the world population at one point, 311 00:18:43,160 --> 00:18:45,760 Speaker 1: or half the population of Europe. They suspect that UM 312 00:18:45,880 --> 00:18:50,399 Speaker 1: in uh the fifth century. That would be a c e. 313 00:18:51,640 --> 00:18:54,600 Speaker 1: The plague of Justinian may have killed as many as 314 00:18:54,680 --> 00:18:58,639 Speaker 1: half the world's population, a hundred million people. Unbelievable. Can 315 00:18:58,680 --> 00:19:01,800 Speaker 1: you imagine walking around on at that time, like, holy crap, 316 00:19:01,920 --> 00:19:05,000 Speaker 1: the entire half the world is dead, just died in 317 00:19:05,000 --> 00:19:07,000 Speaker 1: the last couple of years. It's crazy. On the Black 318 00:19:07,040 --> 00:19:11,080 Speaker 1: Death killed twenty to thirty million Europeans. So so plagues 319 00:19:11,160 --> 00:19:14,640 Speaker 1: can happen. There's also UM. I was talking to an 320 00:19:14,640 --> 00:19:19,720 Speaker 1: evolutionary geneticist this is my way UM recently, and he 321 00:19:20,200 --> 00:19:25,280 Speaker 1: was talking about study he authored where they found two 322 00:19:25,440 --> 00:19:30,480 Speaker 1: evolutionary bottlenecks, one coming out of Africa. Uh. They suggested 323 00:19:30,480 --> 00:19:33,240 Speaker 1: a fifty thousand years ago, and another one that happened 324 00:19:33,240 --> 00:19:36,840 Speaker 1: along the bearing Land Bridge, right, And he wasn't saying 325 00:19:36,880 --> 00:19:38,840 Speaker 1: like all of a sudden a bunch of people died, 326 00:19:39,520 --> 00:19:43,600 Speaker 1: but um, these bottlenecks turned up because big groups of 327 00:19:43,600 --> 00:19:46,880 Speaker 1: people separating in smaller groups of people, which is which 328 00:19:46,880 --> 00:19:49,560 Speaker 1: accounts for a loss of genetic diversity. So you have 329 00:19:49,600 --> 00:19:52,720 Speaker 1: the founders effect. Because, as he put it, if you 330 00:19:52,840 --> 00:19:55,359 Speaker 1: take um, if you go into a town and grabbed 331 00:19:55,400 --> 00:19:57,960 Speaker 1: the first fifteen people you meet and say, let's go 332 00:19:58,000 --> 00:20:00,240 Speaker 1: found a new town, that new town isn't to have 333 00:20:00,320 --> 00:20:03,120 Speaker 1: a representative sample of all the surnames in that town. 334 00:20:03,520 --> 00:20:05,800 Speaker 1: If you do that enough times, some surnames are going 335 00:20:05,880 --> 00:20:08,520 Speaker 1: to be lost because people didn't reproduce or whatever. The 336 00:20:08,600 --> 00:20:11,679 Speaker 1: same thing happens with jeans in genetic diversity. Look at you, 337 00:20:12,680 --> 00:20:15,679 Speaker 1: good stuff? Thanks? Uh. Can I mention this place in 338 00:20:15,720 --> 00:20:19,200 Speaker 1: Hong Kong, Yeah, we're talking about well we should mention. 339 00:20:19,280 --> 00:20:23,639 Speaker 1: Population density is the number of humans per unit area 340 00:20:23,720 --> 00:20:26,680 Speaker 1: whatever unit you you know, you choose to call it, 341 00:20:27,240 --> 00:20:30,639 Speaker 1: And the highest ever is believed to have been a 342 00:20:30,680 --> 00:20:36,000 Speaker 1: place called Kowloon Walled City in Hong Kong, and at 343 00:20:36,000 --> 00:20:39,720 Speaker 1: one point evidently there were fifty thousand people in a 344 00:20:39,800 --> 00:20:42,800 Speaker 1: mega block, which is five hundred by six fifty feet. 345 00:20:43,359 --> 00:20:46,520 Speaker 1: Fifty thousand people stuffed in there, and apparently it was 346 00:20:46,560 --> 00:20:51,440 Speaker 1: a lawless district. The grabster. She kidding me. People could 347 00:20:51,440 --> 00:20:54,800 Speaker 1: conceivably get along. Yeah, hands across America style. Did you 348 00:20:54,880 --> 00:20:58,000 Speaker 1: know that um in Athens when Widespread Panic played that 349 00:20:58,040 --> 00:21:00,760 Speaker 1: free show, there was an estimated hundred some people. They're 350 00:21:01,080 --> 00:21:04,399 Speaker 1: not one fight really, Yeah, that's because they were all 351 00:21:04,400 --> 00:21:09,400 Speaker 1: on dope. The dope. I wasn't there. Were you there? Yeah? 352 00:21:09,440 --> 00:21:11,760 Speaker 1: I never got into them, although I did hang out 353 00:21:11,800 --> 00:21:14,399 Speaker 1: with that guy, the bass player day schools. Yeah, I 354 00:21:14,480 --> 00:21:17,840 Speaker 1: hung out with him a couple of times, just through friends. Anyway. Uh, 355 00:21:18,240 --> 00:21:20,239 Speaker 1: that that park is no, I'm sorry. It is now 356 00:21:20,280 --> 00:21:23,280 Speaker 1: a park where the walled city used to be. Yeah, 357 00:21:23,280 --> 00:21:28,480 Speaker 1: which is the opposite of the highest population. Ironically, it's 358 00:21:28,520 --> 00:21:31,639 Speaker 1: just the park maybe the highest population of grass. But 359 00:21:31,720 --> 00:21:57,320 Speaker 1: that's it. So what do we got here, Josh? We 360 00:21:57,400 --> 00:22:01,520 Speaker 1: got um population control. It's something that we've referenced before 361 00:22:01,560 --> 00:22:04,520 Speaker 1: with our China One Child policy. Yeah, and we talked 362 00:22:04,560 --> 00:22:06,919 Speaker 1: about why you would want to control the population. A 363 00:22:07,040 --> 00:22:10,360 Speaker 1: huge group of people put a strain on resources. When 364 00:22:10,359 --> 00:22:13,920 Speaker 1: resources go away, you have resource conflicts like in darfour 365 00:22:14,560 --> 00:22:18,639 Speaker 1: again genocide. Right. Um. There's all sorts of problems that 366 00:22:18,680 --> 00:22:23,000 Speaker 1: come from too many people coming or living in one 367 00:22:23,040 --> 00:22:25,720 Speaker 1: place because of the strain that puts on resources and 368 00:22:25,760 --> 00:22:30,240 Speaker 1: resource allocation. Right. Um. And yet you can control the 369 00:22:30,280 --> 00:22:36,639 Speaker 1: population e g. You know, state mandated reproduction um, China, right, 370 00:22:37,080 --> 00:22:41,400 Speaker 1: and that actually works as as China shows. Um, although 371 00:22:41,560 --> 00:22:44,520 Speaker 1: much to the detriment of some people, thank you Chuck 372 00:22:44,880 --> 00:22:48,480 Speaker 1: for that look and not everyone thinks um, some people 373 00:22:48,520 --> 00:22:51,480 Speaker 1: think we should add more people though, well, yeah, there's Japan. 374 00:22:52,080 --> 00:22:55,159 Speaker 1: In other countries, there's a problem of population declients. So 375 00:22:55,200 --> 00:22:59,160 Speaker 1: we talked about the strain um people put on an 376 00:22:59,200 --> 00:23:02,080 Speaker 1: area that's in capacity, which we've talked about before, and 377 00:23:02,080 --> 00:23:06,639 Speaker 1: that's also from Malthus, right that eventually human population is 378 00:23:06,680 --> 00:23:11,880 Speaker 1: going to outstrip advances in technology or our resources and 379 00:23:11,960 --> 00:23:16,040 Speaker 1: we're screwed. Right Um. On the other side is shrinking, 380 00:23:16,080 --> 00:23:19,359 Speaker 1: population shrinking, And what's the problem with that, Well, you 381 00:23:19,359 --> 00:23:22,560 Speaker 1: don't want the population to shrink too much because you, uh, 382 00:23:22,880 --> 00:23:25,639 Speaker 1: you need those hands to go to work and to 383 00:23:25,640 --> 00:23:28,840 Speaker 1: to contribute to the economy and to grow the grain 384 00:23:29,000 --> 00:23:32,199 Speaker 1: and sow the flower and all that good stuff. And 385 00:23:32,280 --> 00:23:34,760 Speaker 1: apparently in Russia, Japan and Australia they all have like 386 00:23:34,800 --> 00:23:38,600 Speaker 1: little incentive programs to make little babies. Sure, how about that? 387 00:23:38,680 --> 00:23:42,200 Speaker 1: Which is the way to go? Remember John Fuller's famous quote, um, 388 00:23:42,280 --> 00:23:45,639 Speaker 1: when he was pitching an article about that program in Russia, 389 00:23:45,680 --> 00:23:48,520 Speaker 1: and he's talking about poutin giving away a TV. Oh, yeah, 390 00:23:48,560 --> 00:23:51,800 Speaker 1: that's right, that's really funny. Yeah, um and the baby 391 00:23:51,840 --> 00:23:53,560 Speaker 1: get a TV. I think you had to be there 392 00:23:53,840 --> 00:23:56,960 Speaker 1: and check the reason why. Uh, some of these places 393 00:23:57,000 --> 00:24:00,879 Speaker 1: are seeing a population shrink and or have to, I 394 00:24:00,920 --> 00:24:05,400 Speaker 1: guess give incentives to reproduce. Uh. Started in about nineteen 395 00:24:05,520 --> 00:24:09,600 Speaker 1: sixty birth control. That's so crazy that it had an 396 00:24:09,600 --> 00:24:13,000 Speaker 1: effect that much of an effect that pronounced of an effect. Yeah, 397 00:24:13,000 --> 00:24:15,040 Speaker 1: well it would seem like it would though, I guess 398 00:24:15,040 --> 00:24:19,080 Speaker 1: so because it's called birth control. Sure you know before 399 00:24:19,080 --> 00:24:20,960 Speaker 1: that it was called have as many babies as you 400 00:24:20,960 --> 00:24:25,920 Speaker 1: possibly can. It was called no control alright, So clearly 401 00:24:26,040 --> 00:24:28,800 Speaker 1: there's a lot of reasons to study people. Yeah, it's 402 00:24:29,800 --> 00:24:31,119 Speaker 1: I thought it would be. There's a lot of stuff 403 00:24:31,160 --> 00:24:34,000 Speaker 1: to study to h you can find out whether or 404 00:24:34,040 --> 00:24:37,920 Speaker 1: not we're going to kill the planet or whether um, 405 00:24:38,040 --> 00:24:42,000 Speaker 1: people need to stop using contraceptives, or whether you know 406 00:24:42,040 --> 00:24:45,880 Speaker 1: what your chances are of putin giving you a free TV. Uh. 407 00:24:45,920 --> 00:24:48,959 Speaker 1: It's all in there. It's all demographers know everything, all 408 00:24:48,960 --> 00:24:52,040 Speaker 1: there for the taking. So when you're a frenzy friendly 409 00:24:52,440 --> 00:24:55,960 Speaker 1: enumerator comes knocking on your door, don't chase them off 410 00:24:55,960 --> 00:24:59,320 Speaker 1: your land with your dog or a gun. Let him in, 411 00:24:59,480 --> 00:25:01,880 Speaker 1: give him some lemonade, maybe some cookies. Yeah, we'll check 412 00:25:01,880 --> 00:25:04,119 Speaker 1: their lambing at first, but oh yeah, before you let 413 00:25:04,200 --> 00:25:06,840 Speaker 1: them in. Ceo Ah, Chuck could go in. And if 414 00:25:06,840 --> 00:25:08,840 Speaker 1: you want to know more about population, you can read 415 00:25:08,880 --> 00:25:12,800 Speaker 1: Grabanowski's great article on the site. Just type in population 416 00:25:12,840 --> 00:25:15,720 Speaker 1: in the handy search bart how stuff works dot com, 417 00:25:15,760 --> 00:25:21,360 Speaker 1: which of course leads us to listener mail. Josh, I'm 418 00:25:21,400 --> 00:25:26,240 Speaker 1: just gonna call this your turn at listener mail because 419 00:25:26,240 --> 00:25:28,920 Speaker 1: I think you have to know we'll talk about Yeah. 420 00:25:28,960 --> 00:25:31,880 Speaker 1: I just I don't necessarily have too much listener mail 421 00:25:31,960 --> 00:25:34,639 Speaker 1: per se um. But I just wanted to give a 422 00:25:34,680 --> 00:25:38,400 Speaker 1: shout out to a couple of fellow Toledo wins. One 423 00:25:38,440 --> 00:25:42,360 Speaker 1: who's a longtime resident, one who's a recent transplant. Christopher 424 00:25:42,760 --> 00:25:45,120 Speaker 1: is holding the fort down in Toledo for me, keeping 425 00:25:45,119 --> 00:25:50,480 Speaker 1: it real, he has uh officially lobbied, um, the congresswoman 426 00:25:50,600 --> 00:25:53,159 Speaker 1: from Toledo to get me the key to the city. 427 00:25:54,280 --> 00:25:57,840 Speaker 1: How awesome with it? Yeah, so Marcy kept her. If 428 00:25:57,880 --> 00:26:00,600 Speaker 1: you're listening, I would like, if you get to the city, 429 00:26:00,800 --> 00:26:02,600 Speaker 1: we gotta go for a ceremony and I at least 430 00:26:02,760 --> 00:26:05,080 Speaker 1: want to get like a key chain to the city. Okay, 431 00:26:05,200 --> 00:26:07,679 Speaker 1: and you can have the key. We'll see what we 432 00:26:07,680 --> 00:26:11,800 Speaker 1: can do. Um. So yeah, Christopher has officially petitioner. He's 433 00:26:11,880 --> 00:26:14,159 Speaker 1: he suggested that I'm the third most famous Toledo in 434 00:26:14,200 --> 00:26:18,199 Speaker 1: of all time after Jamie Farr, Jamie Farr, Danny Thomas, 435 00:26:18,200 --> 00:26:20,719 Speaker 1: the Great Entertainer, and then me. And I was like, 436 00:26:21,119 --> 00:26:23,800 Speaker 1: I think you're for getting Katie Holmes because she's from Toledo. 437 00:26:24,480 --> 00:26:28,720 Speaker 1: And he's like, no, you got her bek Kate Cruz. 438 00:26:28,760 --> 00:26:31,760 Speaker 1: You know, is it Kate Cruise now? So anyway, thanks 439 00:26:31,760 --> 00:26:34,280 Speaker 1: a lot for the effort, Christopher, even if it doesn't 440 00:26:34,280 --> 00:26:36,959 Speaker 1: come to fruition. If it does, you will get a 441 00:26:37,000 --> 00:26:40,920 Speaker 1: firm handshake and a free Friendlies Sunday of your choosing 442 00:26:40,960 --> 00:26:43,760 Speaker 1: for me. Friend. Yeah, we'll be going to Friendlies if 443 00:26:43,760 --> 00:26:46,040 Speaker 1: we go to Toledo. Sure. Uh. And then I also 444 00:26:46,119 --> 00:26:50,480 Speaker 1: want to say hi to Colin, who is a recent transplant, 445 00:26:50,480 --> 00:26:54,240 Speaker 1: as I said, Um from Colorado, I believe, who moves 446 00:26:54,280 --> 00:26:58,080 Speaker 1: from Colorado to Toledo. He moved to Toledo to attend 447 00:26:58,080 --> 00:27:01,960 Speaker 1: Bowling Green State University. Joe falcons my brother went there, 448 00:27:02,720 --> 00:27:06,679 Speaker 1: and uh, Colin did so in an eight eight Dodge Colt. Right, 449 00:27:07,160 --> 00:27:09,960 Speaker 1: that's having a couple of problems. One of the rear 450 00:27:10,000 --> 00:27:13,639 Speaker 1: struts are completely detached and the axle is holding on 451 00:27:13,760 --> 00:27:18,119 Speaker 1: by a tread, he says, Um. And the mechanics didn't 452 00:27:18,160 --> 00:27:20,720 Speaker 1: want him to leave when he took it in for service, 453 00:27:21,200 --> 00:27:24,119 Speaker 1: so they're like, you're going to die in this thing, um. 454 00:27:24,200 --> 00:27:27,560 Speaker 1: And the other problem is it has ants, he says. 455 00:27:27,840 --> 00:27:29,959 Speaker 1: I've never heard of a car having ants. I had 456 00:27:29,960 --> 00:27:32,320 Speaker 1: an incident car once. Really, you can't get rid of 457 00:27:32,400 --> 00:27:33,960 Speaker 1: them when they well, that's probably when you were living 458 00:27:34,000 --> 00:27:35,960 Speaker 1: in the car, which was probably always parked on the 459 00:27:36,000 --> 00:27:38,280 Speaker 1: ant hill. This is actually prior to that, when I 460 00:27:38,320 --> 00:27:41,399 Speaker 1: lived in the car. Um. But yeah, no, it's a 461 00:27:41,680 --> 00:27:44,199 Speaker 1: it's a it's a real problem. And Collin's basically just 462 00:27:44,240 --> 00:27:46,159 Speaker 1: put the bullet and said, well, I have ants in 463 00:27:46,160 --> 00:27:48,520 Speaker 1: my car now. He loves his ad eight Dutch Colt, 464 00:27:48,640 --> 00:27:50,919 Speaker 1: he said he loves Salito. He's enjoying. He went to 465 00:27:50,920 --> 00:27:53,680 Speaker 1: Tony Paco's as I suggested. I gotta try that one day. 466 00:27:53,760 --> 00:27:56,040 Speaker 1: I also told him to go to Rusty's Jazz Cafe. 467 00:27:56,440 --> 00:28:00,520 Speaker 1: So it's as authentic as it comes. Awesome. Um So, 468 00:28:01,119 --> 00:28:04,320 Speaker 1: hey Christopher, Hey Colling, you guys enjoy yourselves, Be safe 469 00:28:04,320 --> 00:28:08,240 Speaker 1: and toledown for the winner, Go mud Hens and h 470 00:28:08,359 --> 00:28:11,240 Speaker 1: Thanks for writing in. And if you want to say 471 00:28:11,280 --> 00:28:13,800 Speaker 1: hi to me or Chuck or both of us Chuckers 472 00:28:13,840 --> 00:28:17,119 Speaker 1: or Jerry, you're right, Chucker's Jerry chuck Er, I I 473 00:28:17,160 --> 00:28:21,400 Speaker 1: mean Chucker me chuck in me. Um. You can put 474 00:28:21,400 --> 00:28:25,240 Speaker 1: that in an email to stuff podcast at how stuff 475 00:28:25,240 --> 00:28:32,040 Speaker 1: works dot com for more on this and thousands of 476 00:28:32,080 --> 00:28:35,400 Speaker 1: other topics, does it how stuff works dot com. Want 477 00:28:35,480 --> 00:28:38,120 Speaker 1: more how stuff works, check out our blogs on the 478 00:28:38,120 --> 00:28:39,800 Speaker 1: house stuff works dot com home page