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Speaker 1: Welcome to Stuff you Should Know from House Stuff Works

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Speaker 1: dot com. Hey, and welcome to the podcast. I'm Josh Clark,

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Speaker 1: There's Charles w Chuck Bryant, There's Jerry Stuffy. Should know

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Speaker 1: why grinning. It's been a while, man. Know it's funny,

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Speaker 1: like those words come pouring out of my mouth and

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Speaker 1: then it's cool. You wake up in the middle of

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Speaker 1: night saying that, and you mean like slugs you in

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Speaker 1: the face. She's sleep She has to dry my brow. Yes,

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Speaker 1: we prerecorded some for December, as we like to do

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Speaker 1: to take a little time off at the end of

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Speaker 1: the year and not explain things for a few weeks

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Speaker 1: in our real lives. Like people ask me things like

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Speaker 1: what happened to that stick of butter? Yeah, I don't know,

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Speaker 1: don't ask don't even ask me. I could tell you,

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Speaker 1: but I'm not gonna exactly. That's how it goes in

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Speaker 1: my house. Find your own butter. December was find your

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Speaker 1: own butter money. That's a good that's that's a good one.

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Speaker 1: That should be a T shirt. Stuff you should know

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Speaker 1: find your own butter or December is find your own butter.

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Speaker 1: M Yeah, that's right. Maybe a stick of butter and

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Speaker 1: some Garland on it. Yeah, I like that. So it's

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Speaker 1: good to see you again. Man's gonna be back in here. Yeah,

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Speaker 1: it is nice to be back. As much as the

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Speaker 1: break was great, I'm happy to be explaining things again.

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Speaker 1: Well that's good because if we got in here and

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Speaker 1: you're like, I can't do this, I can't do it again,

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Speaker 1: we'd be in trouble. Yeah. So I'm glad we're all

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Speaker 1: feeling good. Jerry, you're feeling good. Jerry's got two thumbs

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Speaker 1: up in a big goofy smile, two of her three thumbs.

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Speaker 1: She looks like Bob from that male enhancement pill ad.

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Speaker 1: Oh see the guy, the old man that's like super Buff,

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Speaker 1: I would call him old. He was middle aged. He

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Speaker 1: looked like kind of a Bob Dobbs type dude. I

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Speaker 1: think that's kind of who he was modeled. You see

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Speaker 1: the guy that's super muscling. Now I'm thinking of someone different.

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Speaker 1: I think, are you thinking of Jack lo Lane? No? No, no, no,

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Speaker 1: just there's some ad. There's some old man that looks

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Speaker 1: like really creepy because from the next super Buff looks

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Speaker 1: like it's twenty five year old. No, remember that was

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Speaker 1: like a male enhancement pill and I'm making air quotes

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Speaker 1: here for erectile this unsion. Well, there go the air quotes.

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Speaker 1: But yes, And it was like in the early two thousands.

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Speaker 1: I think maybe late nineties, but I think early two thousands,

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Speaker 1: and these ads were everywhere, and there was Bob and

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Speaker 1: like all these great things happened to him because he

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Speaker 1: started taking this pill. I can't remember the name of

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Speaker 1: the pill. But the company like got into a lot

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Speaker 1: of trouble because it was basically like a subscription service

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Speaker 1: and like you gave him your credit card and you

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Speaker 1: got this free trial, but then they started sending it

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Speaker 1: to you and it was like next to impossible to

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Speaker 1: cut off service. Interesting, They're like, no, we want your

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Speaker 1: mailness to be enhanced. So you you've seen these ads.

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Speaker 1: I was gonna start asking questions, but why bother? I

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Speaker 1: will I will YouTube. I will find it on YouTube.

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Speaker 1: I'll be like, oh, Bob, yeah you will. You'll go, Oh,

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Speaker 1: I want to come back in and record an insert

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Speaker 1: the guy that's on the back of all these pill

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Speaker 1: bottles in my bathroom. Oh, chuck, I don't even remember

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Speaker 1: how we got Oh yeah, Jerry did that. That was

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Speaker 1: Jerry's fault. But um, you remember we did the Enlightenment episode. Okay,

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Speaker 1: we talked a lot about how there's this kind of

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Speaker 1: um tug of war over the human psyche between rationalism

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Speaker 1: and mysticism. I guess you could you could put it well,

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Speaker 1: I feel like we're talking today about the scientific method.

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Speaker 1: Great idea, by the way, Thank you very much. It's

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Speaker 1: been a long time coming. Um, because I realized, like

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Speaker 1: I don't understand it as fully as I don't understand science.

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Speaker 1: I understand the scientific method because it's pretty cut and

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Speaker 1: dry and it's beautiful and elegant and simple. But then

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Speaker 1: you just take this thing and it came out of

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Speaker 1: the birth of rationalism, and when you place it into

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Speaker 1: the world and make it function, there's a lot of implications.

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Speaker 1: Is it being used properly? Is it being used responsibly?

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Speaker 1: Like are we putting what constitutes f E than to that?

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Speaker 1: You know, like it just raises all this other stuff.

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Speaker 1: And it made me realize, like I don't understand science

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Speaker 1: as much as I want to, So researching this it

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Speaker 1: was awesome. Yeah, and this is a cool episode, I

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Speaker 1: think because not only are we going to talk about

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Speaker 1: the scientific method, but we're going to talk about just science,

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Speaker 1: like what is science in general? And some of the

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Speaker 1: rock stars along the way, who really you know, laid

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Speaker 1: out the path remarkably in like many many years ago,

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Speaker 1: like coming up with these amazing discoveries that still like

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Speaker 1: hold you know, you can like hold their feet to

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Speaker 1: the fire for a lot of this stuff. Yeah, because

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Speaker 1: if you come upon a universal truth, you know it

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Speaker 1: is what it is, like you got to be the

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Speaker 1: person who discovered it because you know, you saw it,

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Speaker 1: you realized it a certain way, but ultimately it was

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Speaker 1: there already. Yeah, like Newton, I mean, we'll talk about

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Speaker 1: all this stuff, but it's not like now we're like,

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Speaker 1: oh Newton, most of what he said was wrong, But

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Speaker 1: that's understandable because it was a long time ago. Like

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Speaker 1: his stuff holds up really well. I was wondering if

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Speaker 1: he on his deathbed was just like, oh, man, I

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Speaker 1: contributed so much to humanity it's mind boggling, but I

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Speaker 1: couldn't enhance my malehood. Well, Bob hadn't come along yet,

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Speaker 1: So Chuck, let's let's just quit stalling and talk about science,

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Speaker 1: like what is science? Well, I hate the old elementary

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Speaker 1: school uh defined as, but it's a pretty good place

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Speaker 1: to start here to get a base definition of science. Yeah,

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Speaker 1: Old William Harris did a great job with this. Yes,

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Speaker 1: William Harris did a great job. Uh. Science the intellectual

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Speaker 1: and practical activity encompassing the structure and behavior of the

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Speaker 1: physical and natural world through observation and experimentation. Uh So

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Speaker 1: the first part of that is science is practical and

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Speaker 1: it is you know, they make a good he makes.

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Speaker 1: Bill Harris makes a great point in here. It's not

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Speaker 1: just stuff you do in a lab, and it's not

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Speaker 1: just for scientists. It is all about being hands on

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Speaker 1: and active, and it's about discovery and asking questions about

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Speaker 1: I mean, that's how everything is ultimately solved, is by

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Speaker 1: someone looking at something and having a question about it exactly.

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Speaker 1: And then the scientific method comes in when you say,

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Speaker 1: and this is how you properly get to that answer exactly. Um.

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Speaker 1: And he makes another good point to that. The idea

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Speaker 1: that there's a method, a scientific method, makes it seem

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Speaker 1: like it's it's secreted away among the the fraternity of scientists.

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Speaker 1: And like you said, any anybody can use it. It's

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Speaker 1: just kind of part of being a curious human is

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Speaker 1: it's not even anyone can use it everyone does use it,

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Speaker 1: you just might not even know that you're using it,

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Speaker 1: like if you I mean one of the examples that

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Speaker 1: use later is if like your car overheats, when you

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Speaker 1: figure out why and fix it, that's the scientific method, right,

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Speaker 1: playing out exactly based on reasoning. Yeah, okay, and deduction

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Speaker 1: and induction. Man, there's so much to talk about. Okay,

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Speaker 1: So let's let's talk about that definition that you had.

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Speaker 1: So the first part is that science is it's a

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Speaker 1: practical activity. So science is practical, right, It's it's this, Um.

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Speaker 1: The basis of the whole thing is discovery. Right, you

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Speaker 1: see something, You see birds in flight, and you say,

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Speaker 1: where are those birds going? And if you just went

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Speaker 1: and laid down on the ground and went to sleep

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Speaker 1: after that, then you're not you're not carrying out science.

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Speaker 1: But if you went, I want to find out where

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Speaker 1: those birds are going, and you follow them and you

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Speaker 1: start taking notes, that's that is the basis of science

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Speaker 1: is discovery. Yeah, and that's the observational part as well. Um,

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Speaker 1: sometimes you're using a microscope or a telescope, sometimes you're

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Speaker 1: using your eyeballs. But no matter what your tool is, uh,

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Speaker 1: you're gonna be watching something and recording. What's called data

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Speaker 1: or data depending on I don't know what kind of

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Speaker 1: person you are. What do you say I think? I

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Speaker 1: say both. I think data. Yeah, I don't think I've

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Speaker 1: don't think. I say data data, I say data data. Yeah,

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Speaker 1: all right, we'll go with data. You say both. I

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Speaker 1: feel like it just comes out of my mouth one

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Speaker 1: way or the other, and I don't really think about it.

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Speaker 1: I think that's like being ambidextrous. Yeah. Yeah, I'm a

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Speaker 1: data datas Yeah. Uh so once you are observing this data, well,

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Speaker 1: there are a couple of kinds. There's quantitative data, which

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Speaker 1: are numbers, like you know, your body temperature is point six,

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Speaker 1: although I think that's changed slightly now, didn't it. Yeah? Yeah, there.

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Speaker 1: You used to be like, if you were a human being,

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Speaker 1: your body temperature is point six, and they it's like, no,

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Speaker 1: there's a little more variation than that. But any kind

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Speaker 1: of just numerical representation is quantitative, whereas qualitative is behavioral,

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Speaker 1: like I'm gonna watch that bird um eat in poop

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Speaker 1: for the next week, right, or what happens if I

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Speaker 1: what will the slug do if I put a bunch

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Speaker 1: of salt on it? Do you know I don't do that. No,

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Speaker 1: you really should not do that. No, it's awful. But

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Speaker 1: the reaction of the slug is gathering qualitative data. And

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Speaker 1: depending on who you talk to, there isn't qualitative data

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Speaker 1: and science that it should all just be quantitative because yeah,

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Speaker 1: because quantitative data is reproducible. Qualitative data is. It's not

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Speaker 1: necessarily reproducible. You can observe the same phenomenon, but you're

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Speaker 1: not necessarily controlling it. Okay, well, I guess I get that.

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Speaker 1: But I agree with Bill here and that they are both.

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Speaker 1: They go hand in hand, and neither one is more

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Speaker 1: important than the other. You need to have both. Well,

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Speaker 1: a lot of people do, and we'll talk more about

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Speaker 1: it later, because without the idea that qualitative data is

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Speaker 1: acceptable and scientific, you don't have the social social sciences,

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Speaker 1: like they don't exist. Yeah, that's a good point, but yes,

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Speaker 1: we have quantitative data and qualitative data. I agree with you.

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Speaker 1: They're both useful. Uh. It is an intellectual pursuit um,

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Speaker 1: So you can make observations on data all day long.

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Speaker 1: But until you bring reason, in this case inductive reasoning,

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Speaker 1: which is driving a generalization based on your observations, then

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Speaker 1: it's just data sitting there on a piece of paper

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Speaker 1: like it's supposed to lead you somewhere right exactly, And

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Speaker 1: so we should talk about inductive and deductive reasoning and

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Speaker 1: it depending. Again, it's really weird, But one of the

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Speaker 1: things that came across is that there's not a universal

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Speaker 1: agreement on how science is carried out. Like I saw

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Speaker 1: some places where there's like there's no place for inductive

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Speaker 1: reasoning in science. Then other places they're saying, well, you

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Speaker 1: have to have science using inductive reasoning. Everybody seems to

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Speaker 1: agree that deductive reasoning is the basis of science, but

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Speaker 1: that you also have to have inductive So deductive is

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Speaker 1: basically taking a big, broad generalization and saying that it

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Speaker 1: applies to something specific more specific. Yes, Uh, inductive is

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Speaker 1: the opposite where you say, I've noticed these different data points,

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Speaker 1: and uh, that means that this broad generalization is true.

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Speaker 1: So you go from specific, small observations to a broad generalization.

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Speaker 1: And the reason that a lot of people say, well,

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Speaker 1: inductive reasoning doesn't have any place in science is because

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Speaker 1: you're saying those birds over there are all brown, Therefore

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Speaker 1: all birds of that type of brown, even though I

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Speaker 1: haven't seen every single bird of that type in the world.

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Speaker 1: I'm saying that all those birds are brown. And a

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Speaker 1: lot of people say there's no place for that in science. Well,

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Speaker 1: if you want to go out and prove that, then

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Speaker 1: that's your business. You know. You can't just say that

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Speaker 1: and be like and I'm done, right exactly. I guess

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Speaker 1: you could. But much of scientists, right, but the the

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Speaker 1: you can use it to formulate hey, hypotheses. Right, so

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Speaker 1: you can say I've generated all these data points, I'm

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Speaker 1: gonna put them together and see if this broad generalization

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Speaker 1: is right. Okay, so there is a place for inductive

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Speaker 1: seeing science. But everybody says deductive reasoning is the basis

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Speaker 1: of science. Well, Bill Harris does. Uh. He offers a

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Speaker 1: great example for inductive reasoning with Edwin Hubble of the

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Speaker 1: Hubble Telescope. Uh. He was looking through the Hooker telescope,

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Speaker 1: which at the time at California's Mount Wilson. Is that

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Speaker 1: the one from Rebel Now that's um Griffith Park Observatory,

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Speaker 1: which has been redesigned, and uh, it is really cool now, yeah,

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Speaker 1: I mean it was kind of cool before, but it

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Speaker 1: was definitely like, uh, sort of the bass music in

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Speaker 1: the time forgot. Oh really, so they've updated it. I'll

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Speaker 1: bet that was cool though in its own way. Yeah,

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Speaker 1: it was neat. I used to live near there, so

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Speaker 1: it was kind of But that's like the famous one,

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Speaker 1: at least in the movies. Yeah, that's where they have

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Speaker 1: the big knife fight. Yeah, and there's this James Dean

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Speaker 1: statue there too. I didn't like a bust um. So yes.

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Speaker 1: Edwin Hubble he's at Mountain Wilson and he's looking through

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Speaker 1: the Hooker telescope, which was the biggest one. And at

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Speaker 1: the time, everyone said, the Milky Way galaxy is it?

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Speaker 1: That's what we've got going on. Did you know this? Uh? Yeah,

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Speaker 1: I knew that because we're talking. Yeah, not that long ago.

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Speaker 1: Did not realize this. And he started looking through this

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Speaker 1: telescope and said, you know what, these nebula that everyone

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Speaker 1: says are part of our galaxy look to me like

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Speaker 1: they're beyond our galaxy. Not only that, they look like

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Speaker 1: they're moving away from us. So he made this, uh

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Speaker 1: with through inductive reasoning, made this observation that you know what,

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Speaker 1: I think there are many many galaxies out there, and

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Speaker 1: not only that, I think they are expanding, and uh,

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Speaker 1: through technological advancement with telescopes over the years, scientists, you know,

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Speaker 1: it proved to be true. Yeah, pretty cool. So this

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Speaker 1: is a really good example of him saying, like, I've

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Speaker 1: made some observations, and now I'm going to say this

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Speaker 1: broad generalization. Right, So these these galaxies appear to be

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Speaker 1: moving away from me, not another. So the whole universe

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Speaker 1: is expanding. Right, that's inductive reasoning. It's a pretty brave thing, uh,

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Speaker 1: especially back then, because you're really putting your reputation at stake,

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Speaker 1: you know. So what Hubble was, what Hubble did was

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Speaker 1: what we've come to see as science. He made some observations,

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Speaker 1: he came up with a hypothesis, um, and then it

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Speaker 1: was tested later on. It's not you don't necessarily as

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Speaker 1: a scientist. You're a part of a larger collective of scientists, right,

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Speaker 1: And every scientist needs one another. It's why there's journals

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Speaker 1: and and um conferences and things like that to share information, right,

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Speaker 1: And the party and the party, and and Hubble came

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Speaker 1: up with his own observations. And rather than just experimenting, experimenting,

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Speaker 1: experimenting himself, which I'm sure he continued to do, he

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Speaker 1: created this basis of work that he probably realized is

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Speaker 1: going to survive him. Right, And then later on scientists

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Speaker 1: came down the road and they tested his hypothesis and

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Speaker 1: they found it was correct, and so his hypothesis became

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Speaker 1: a theory. It eventually became part of the basis of

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Speaker 1: the Big Bang theory that the universe started as a

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Speaker 1: huge explosion and it's expanding still because we're because it

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Speaker 1: exploded at one point, right, And they did that by

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Speaker 1: carrying out other tests or experiments exactly. So this is

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Speaker 1: how science works. Like some guy back in in nineteen

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Speaker 1: nine makes some observations in California, he proposes this big,

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Speaker 1: broad generalist generalization, and over the next like ensuing half

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Speaker 1: a century, more and more scientists all around the world

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Speaker 1: start testing his hypothesis and find it to be true,

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Speaker 1: so it becomes a theory. Yeah. Well, well let's finish

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Speaker 1: up here with science. The last part of the definition

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Speaker 1: is that it's systematic and it's methodical, and it requires

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Speaker 1: testing and experiments, and it requires those experiments and tests

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Speaker 1: uh to be repeated and verified, and it is is

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Speaker 1: it's a system. It's a way of working things out.

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Speaker 1: It's a way of working That is the scientific method basically, Yeah,

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Speaker 1: you have your idea, you pose a question, you theorize

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Speaker 1: hyper you put a hypothesis out there, and then you

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Speaker 1: go about trying to either prove it or disprove it. Yeah, exactly.

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Speaker 1: And then the way that you go about proving or

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Speaker 1: disproving it, that's the scientific method. Everything else is just

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Speaker 1: scientific inquiry. The way you go about, the standardized way

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Speaker 1: of going about scientific inquiry is the scientific method. And

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Speaker 1: we friend, we'll talk about the scientific method right after this.

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Speaker 1: All right, you brought up a point. I think we

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Speaker 1: should go ahead and just get right to my friend.

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Speaker 1: Let's do hypotheses and theories. One thing to say together, No,

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Speaker 1: One thing that really chase my hide is, uh, when

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Speaker 1: you hear poopo ors of whatever scientific theory say, well,

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Speaker 1: it's just a theory. And you where was this thing

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Speaker 1: that you found that poo pooed that? Do you remember

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Speaker 1: what website that was? No? No, although I do want

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Speaker 1: to give a shout out now that you mentioned it.

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Speaker 1: Two explorables. It's like an online university basically of free courses,

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Speaker 1: and uh, there is one on scientific reasoning that is

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Speaker 1: just amazing. It's like a huge rabbit hole. You go

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Speaker 1: down and you start clicking on the embedded links and

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Speaker 1: you end up like understanding all sorts of stuff. So

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Speaker 1: go check that one out if you like understanding stuff.

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Speaker 1: So that's one of the things that bug me if

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Speaker 1: someone says it's just a theory, and this does a

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Speaker 1: great job of kind of throwing that out the window. Um,

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Speaker 1: because it's basically mixing up the two definitions of theory. Yeah,

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Speaker 1: there's like a colloquial definition that people use every day

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Speaker 1: that it doesn't really have much to do with the

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Speaker 1: scientific use. Like, I got a theory that Jerry entered

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Speaker 1: one hour bathroom breaks every day is really playing uh

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Speaker 1: words with friends in the lobby. I think your theory

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Speaker 1: is correct. So that's a theory in the colloquial meaning.

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Speaker 1: As far as science goes, the theory is not just

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Speaker 1: something you postulate, say if this may or may not

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Speaker 1: be true. The theory is beyond the hypothesis, and it's

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Speaker 1: something that is strongly supported in many different ways and

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Speaker 1: all there's all kinds of evidence to support something that

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Speaker 1: eventually becomes a theory. Right, So, um, what you your

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Speaker 1: theory about Jerry's bathroom breaks in the scientific world would

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Speaker 1: be a hypothesis. In fact, yeah, it would be a

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Speaker 1: scientific law, but ultimately it would begin as a hypothesis,

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Speaker 1: a hunch based on intuition, based on the data you've collected, observeyations,

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Speaker 1: that kind of stuff. Where like, you know, you've seen

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Speaker 1: that Jerry goes to the bathroom for like an hour

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Speaker 1: to stretch frequently. When she comes back, she's um finishing

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Speaker 1: up a game of words with friends. You've heard that

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Speaker 1: she's been spotted in the lobby during these times. So

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Speaker 1: your hypothesis is that while she is gone for these

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Speaker 1: hour long bathroom breaks, she's actually down the lobby playing

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Speaker 1: words with friends. Right, based on knowledge, observation, and logic. Right,

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Speaker 1: So let's say that you decided to set up an experiment,

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Speaker 1: and you experimented, and you went and you found Jerry

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Speaker 1: playing words with friends five different times, and you told

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Speaker 1: me about it, and I was like, I'm going to

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Speaker 1: run that same experiment exactly the way you did, Right,

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Speaker 1: I would test that same hypothesis. If I found the

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Speaker 1: same results to be true, then what you would have

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Speaker 1: come up with, your hypothesis would move to basically a

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Speaker 1: theory that is, this widely accepted thing, this explanation that

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Speaker 1: Jerry is not actually in the bad room, she's downstairs

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Speaker 1: playing with friends. Would be the Jerry bathroom break theory,

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Speaker 1: that's right. And then if it turns out that you

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Speaker 1: find that Jerry spending an hour a day pretending to

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Speaker 1: be in the bathroom but actually being downstairs playing words

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Speaker 1: with friends, if the universe couldn't exist without her doing

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Speaker 1: that every day, you would have a scientific law. That's right. Yeah,

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Speaker 1: I think that was a good example you came up

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Speaker 1: with as a great example. As it turns out. Uh,

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Speaker 1: I guess the point here is when you hear someone

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Speaker 1: say in an argument, well, that's just a theory, just

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Speaker 1: punch him in the head and then tell them what

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Speaker 1: we just said about the bathroom breaks and they'll say,

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Speaker 1: who's Jerry? Or just just queue up that whole bit

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Speaker 1: and stand outside of their window wearing a trench coat

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Speaker 1: and holding a boom box over your head with the

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Speaker 1: smug look on your face. Uh all right, So should

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Speaker 1: we go back in the old way back machine a

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Speaker 1: little bit and just talk a little bit about how

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Speaker 1: the scientific method came to be? Yes, man, this this thing,

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Speaker 1: what are you running this on these days? Straight? Kerosene?

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Speaker 1: The fumes in here killing me. Sorry about that? Trying

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Speaker 1: to go green? You know, kerosene is not green diesel.

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Speaker 1: Maybe I'm choking bio diesel. How about that? Okay, the

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Speaker 1: way Back machine will run French freed grease. That would

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Speaker 1: be fine. I'll get to work on that. I could

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Speaker 1: handle this. So you you tease us with the Renaissance,

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Speaker 1: and the reason the Renaissance was so awesome and necessary

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Speaker 1: it was because of something else we talked about, which

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Speaker 1: was the Dark Ages, Uh when, which you remember, that's

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Speaker 1: the rationalists disparaging term for this era. That's right, but

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Speaker 1: I think sort of rightfully so because right before the

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Speaker 1: Dark Ages until about a century after, there was not

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Speaker 1: much advancement at all in the realm of scientific advancement.

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Speaker 1: Uh No, it's it's true. That's hard to argue with that,

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Speaker 1: and and the reason why is again, science wasn't really

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Speaker 1: born yet, and there is a huge struggle between rationalism

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Speaker 1: and mysticism, and ultimately we're living in the age of

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Speaker 1: rationalism now. Yeah, and we should point out too that

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Speaker 1: this was mainly in Europe over the Islamic world. As

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Speaker 1: I think we had a listener mail point out, there

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Speaker 1: are a lot of advancements being made uh just sort

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Speaker 1: of flying under the European radar at the time, because

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Speaker 1: some say the Catholic Church kind of kept science under

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Speaker 1: its thumb for a while, and it's a pretty big threat.

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Speaker 1: He said, you know, you can't do this stuff, you

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Speaker 1: can't experiment like this, and don't ask these questions because

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Speaker 1: here are your answers. But eventually the Renaissance came about

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Speaker 1: in the twelfth century and people woke up and I

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Speaker 1: saw some of the work in the Islamic world and said,

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Speaker 1: you know what, maybe let's start reading up on Aristotle

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Speaker 1: and told me and euclid it once again. Yeah, they're like,

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Speaker 1: we forgot about these guys. I mean, it literally kind

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Speaker 1: of vanished for a while. It did from the West. Yes,

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Speaker 1: fortunately it was still around, you know, and in its

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Speaker 1: home places. But yes, in the West they were lost.

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Speaker 1: The Roman stuff was almost entirely lost because it was

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Speaker 1: being suppressed by the locals, and I think the Greek

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Speaker 1: knowledge was completely banished. Yes, somehow somehow they got there

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Speaker 1: was some um. We got another listener mail after the Enlightenment.

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Speaker 1: One they said that it was an Islamic scholar who

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Speaker 1: was the one who translated Aristotle into Latin. Or something

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Speaker 1: like that, and that without this guy, like the West

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Speaker 1: wouldn't have had much to start with, because that's where

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Speaker 1: that birth of rationalism came from from, was this rediscovery

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Speaker 1: of Greek and Roman classical thought, and this was the

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Speaker 1: basis of scientific inquiry of rationalism, of saying, like, okay,

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Speaker 1: there's there's set rules to things, and we need to

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Speaker 1: discover these rules and how the principles of how the

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Speaker 1: universe works, like there has to be principles, and we

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Speaker 1: need to find this in a rational, methodical way. And

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Speaker 1: right out of the gate Europe said, oh, okay, well

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Speaker 1: whatever you say is right then, Aristotle. We're used to

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Speaker 1: just believing everything without questioning it. And luckily Albert Magnus,

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Speaker 1: I think is who it was. Um Albertus was Albertus Magnus.

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Speaker 1: Roger Bacon, who said, no, it is Bacon, Roger Bacon,

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Speaker 1: who just has this great name, Roger Bacon, the Bacon brothers. Yeah,

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Speaker 1: he's they weren't brothers though, but they were they related

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Speaker 1: at all? You know. I looked that up and I

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Speaker 1: don't think people know either way. I don't think there's

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Speaker 1: any proof, but a lot of people think because of

424
00:24:44,480 --> 00:24:46,760
Speaker 1: their names and the way things went back then, that

425
00:24:46,880 --> 00:24:49,280
Speaker 1: they may very well have been. And I mean they

426
00:24:49,280 --> 00:24:52,919
Speaker 1: were separated by three or so years. Although Roger was

427
00:24:52,960 --> 00:24:55,960
Speaker 1: a was a monk, so he would not have had children.

428
00:24:56,520 --> 00:24:59,080
Speaker 1: So if they were, it's an excellent point. It wasn't

429
00:24:59,119 --> 00:25:02,719
Speaker 1: necessarily through line, you know. Yeah, it could have been

430
00:25:02,800 --> 00:25:06,040
Speaker 1: a nephew or something, or his brother Kevin might have

431
00:25:06,119 --> 00:25:09,880
Speaker 1: had the line that matched. So Roger was the one

432
00:25:09,880 --> 00:25:13,800
Speaker 1: who said, everybody stopped. Just because Aristotle wrote something doesn't

433
00:25:13,800 --> 00:25:18,400
Speaker 1: mean it's fact, especially when we find contradictions to it.

434
00:25:18,920 --> 00:25:21,440
Speaker 1: It doesn't oursells not automatically, right, And this is a

435
00:25:21,520 --> 00:25:25,639
Speaker 1: huge advancement. Yeah, And Albertus Magnus was the one, I

436
00:25:25,680 --> 00:25:30,000
Speaker 1: believe who said, you know, this thing called revealed truth,

437
00:25:30,160 --> 00:25:34,639
Speaker 1: which is basically God says this instead of a truth

438
00:25:34,680 --> 00:25:39,359
Speaker 1: found by experimenting, is maybe we should experiment instead and

439
00:25:39,440 --> 00:25:42,199
Speaker 1: not take this revealed truth as the truth. Right. And

440
00:25:42,280 --> 00:25:46,320
Speaker 1: we mentioned in that Enlightenment episode as well about scholasticism

441
00:25:46,320 --> 00:25:52,399
Speaker 1: about using scientific inquiry to explain theology, which was, you know,

442
00:25:52,640 --> 00:25:55,840
Speaker 1: you're still working from a theological standpoint, right, but you're

443
00:25:55,840 --> 00:26:00,560
Speaker 1: starting to use scientific inquiry and the the idea that

444
00:26:00,640 --> 00:26:03,399
Speaker 1: you shouldn't just accept things as truth. That was again

445
00:26:03,440 --> 00:26:07,840
Speaker 1: a huge, huge breakthrough. Yeah. Uh. Francis Bacon, the other

446
00:26:07,880 --> 00:26:10,760
Speaker 1: Bacon brother, he's one of the heroes of this story. Yeah.

447
00:26:10,800 --> 00:26:16,080
Speaker 1: He was an attorney and philosopher and possibly Shakespeare. Oh really,

448
00:26:16,400 --> 00:26:21,640
Speaker 1: I never heard that? Interesting, So what do you mean

449
00:26:21,680 --> 00:26:25,600
Speaker 1: like wrote those under the pseudonym? Huh? And there the

450
00:26:25,640 --> 00:26:27,800
Speaker 1: Shakespeare sister was the other theory too, right, it was

451
00:26:27,840 --> 00:26:30,840
Speaker 1: a woman. I've heard that, and she couldn't like, you know,

452
00:26:30,920 --> 00:26:34,760
Speaker 1: women couldn't be the play right, so was her dumb

453
00:26:34,760 --> 00:26:38,280
Speaker 1: brother William? That's good? Was it a brother? I think

454
00:26:38,320 --> 00:26:40,680
Speaker 1: that was one of the theories. This is a good

455
00:26:41,119 --> 00:26:44,960
Speaker 1: Smith song too. Uh, Shakespeare sister was that the name

456
00:26:44,960 --> 00:26:48,360
Speaker 1: of it? Wouldn't it a band to you? I think

457
00:26:48,400 --> 00:26:52,280
Speaker 1: it was? What was it? Maybe? Uh? So anyway, he

458
00:26:52,400 --> 00:26:56,679
Speaker 1: was a philosopher and a lawyer, and he said, you

459
00:26:56,720 --> 00:27:01,879
Speaker 1: know what, the Baconian method basically became the scientific method.

460
00:27:02,280 --> 00:27:05,040
Speaker 1: He was the first dude who really said, this is

461
00:27:05,320 --> 00:27:09,480
Speaker 1: how the steps that you should take to uh investigate science.

462
00:27:10,359 --> 00:27:12,080
Speaker 1: There has to be a framework. And the whole point

463
00:27:12,119 --> 00:27:15,200
Speaker 1: of this that we take this so for granted now

464
00:27:15,280 --> 00:27:22,120
Speaker 1: because it's so intuitively and on its face right as

465
00:27:22,119 --> 00:27:24,560
Speaker 1: far as science inquiry, he goes, but this is an

466
00:27:24,720 --> 00:27:30,000
Speaker 1: enormous breakthrough to say, follow this step, these steps, this framework,

467
00:27:30,920 --> 00:27:35,160
Speaker 1: and if everybody who carries out science follows the same framework,

468
00:27:35,480 --> 00:27:39,600
Speaker 1: then science will be universal and interchangeable and anyone in

469
00:27:39,600 --> 00:27:43,119
Speaker 1: the world, and not just now, but anytime, we'll be

470
00:27:43,200 --> 00:27:47,399
Speaker 1: able to carry out the same experiment and we'll be

471
00:27:47,480 --> 00:27:52,600
Speaker 1: able to verify or disprove it. And that is amazing

472
00:27:52,800 --> 00:27:55,440
Speaker 1: that that happened. That's why Francis Bacon is one of

473
00:27:55,480 --> 00:27:57,159
Speaker 1: the heroes of the story. And he didn't come up

474
00:27:57,200 --> 00:27:59,639
Speaker 1: with this entirely on his own, but he was the

475
00:27:59,640 --> 00:28:02,399
Speaker 1: one who said, this is what we're gonna do. I'm

476
00:28:02,440 --> 00:28:04,760
Speaker 1: gonna give it a name. I'm going to spell it out,

477
00:28:04,880 --> 00:28:08,159
Speaker 1: and from now on you can call me the dad

478
00:28:08,680 --> 00:28:11,639
Speaker 1: of the scientific method. Yeah, and that's why Newton was

479
00:28:11,680 --> 00:28:14,800
Speaker 1: such a rock star, because he so rigorously stuck to

480
00:28:14,920 --> 00:28:19,359
Speaker 1: the scientific method that all these centuries later, his uh,

481
00:28:19,400 --> 00:28:22,040
Speaker 1: you know, his systems of laws are they have stood

482
00:28:22,040 --> 00:28:24,480
Speaker 1: the test of time. And uh, I think it's a

483
00:28:24,480 --> 00:28:28,159
Speaker 1: good point to bring up to that the collaboration of

484
00:28:28,200 --> 00:28:34,800
Speaker 1: scientists is really the hallmark of advancement and moving forward.

485
00:28:35,359 --> 00:28:38,400
Speaker 1: It's not working in a vacuum. It's sharing your ideas

486
00:28:38,440 --> 00:28:41,680
Speaker 1: and working with one another and the whole uh little

487
00:28:41,720 --> 00:28:44,000
Speaker 1: sidebar here on celth here I thought was pretty cool,

488
00:28:44,880 --> 00:28:48,360
Speaker 1: which was when science quit. We're not quit, but started

489
00:28:48,400 --> 00:28:51,680
Speaker 1: looking at small things instead of looking at the universe

490
00:28:51,720 --> 00:28:56,000
Speaker 1: around them and at the stars. And uh said basically,

491
00:28:56,440 --> 00:29:00,160
Speaker 1: you know, through the advancement of lens grinding, Antonio van

492
00:29:00,880 --> 00:29:05,440
Speaker 1: Levin Hook specifically Dutch tradesman was pretty good at making

493
00:29:05,480 --> 00:29:09,040
Speaker 1: simple microscopes, and all of a sudden, contemporaries like Robert

494
00:29:09,040 --> 00:29:11,400
Speaker 1: Hook said, you know what, let's start looking at tiny

495
00:29:11,440 --> 00:29:14,600
Speaker 1: things because they're in might lie the answer too many

496
00:29:14,640 --> 00:29:18,440
Speaker 1: many things. Yeah, and they're right. Robert Hook found cork

497
00:29:19,160 --> 00:29:23,240
Speaker 1: or he discovered cells by looking at cork through an

498
00:29:23,280 --> 00:29:27,560
Speaker 1: early microscope. So in this story, science is hastened by

499
00:29:27,680 --> 00:29:32,920
Speaker 1: technological advancement lens grinding to make microscopes, and then this

500
00:29:32,960 --> 00:29:36,000
Speaker 1: new technology is used to further science. Right. Yeah, it's

501
00:29:36,000 --> 00:29:41,000
Speaker 1: like mutual inspiration between Levin Hook and Hook n Hook.

502
00:29:41,360 --> 00:29:46,720
Speaker 1: It was neat because Hook heard about Leavin Hooks microscopes,

503
00:29:47,080 --> 00:29:50,120
Speaker 1: got his hands on one or a microscope, looked at

504
00:29:50,240 --> 00:29:52,800
Speaker 1: him like cork and said, oh, there's such a thing

505
00:29:52,880 --> 00:29:56,320
Speaker 1: as cells. Levin Hook said, oh, that's pretty neat. Let

506
00:29:56,320 --> 00:29:58,480
Speaker 1: me try, and he said, oh, there's such a thing

507
00:29:58,480 --> 00:30:03,280
Speaker 1: as quote little animal, which we call protozoan bacteria. And

508
00:30:03,720 --> 00:30:07,200
Speaker 1: one of the Royal Societies. After Leavin Hook presented his findings,

509
00:30:07,480 --> 00:30:10,360
Speaker 1: turned back to Hook and said, hey, Hook, we know

510
00:30:10,400 --> 00:30:13,600
Speaker 1: you're pretty handy with the microscope. You confirmed Leavin Hook's

511
00:30:13,880 --> 00:30:17,840
Speaker 1: findings are their little animals? Hook said, there are. Indeed,

512
00:30:18,000 --> 00:30:20,320
Speaker 1: I can see them with my microscope. That's right. And

513
00:30:20,360 --> 00:30:25,840
Speaker 1: that inspired a German botanist name Matthias Schleiden ah to

514
00:30:26,240 --> 00:30:29,160
Speaker 1: look at a lot of plants and he was the

515
00:30:29,200 --> 00:30:31,320
Speaker 1: first guy to say, you know what, plants are composed

516
00:30:31,320 --> 00:30:34,000
Speaker 1: of cells. And he's having dinner one night with his

517
00:30:34,120 --> 00:30:37,520
Speaker 1: zoologist buddy. Yeah, and this is about a hundred years later. Yeah,

518
00:30:37,560 --> 00:30:42,160
Speaker 1: Theodore schran and said, you know what, dude, Uh, order

519
00:30:42,240 --> 00:30:45,920
Speaker 1: the wine and order the steak. Trust me, because this

520
00:30:45,960 --> 00:30:51,000
Speaker 1: place is fantastic. And uh, also, plants are made of cells.

521
00:30:51,200 --> 00:30:53,520
Speaker 1: Don't tell anyone. And he went, you know what, dude,

522
00:30:53,680 --> 00:30:57,280
Speaker 1: I have been investigating animals with microscopes and they're made

523
00:30:57,280 --> 00:31:00,800
Speaker 1: of cells too. And so they figured out at this dinner, Yeah,

524
00:31:00,840 --> 00:31:03,239
Speaker 1: that everything is made of cells. All living things are

525
00:31:03,280 --> 00:31:06,200
Speaker 1: made of cells. Boom. Okay, so this is huge. This

526
00:31:06,280 --> 00:31:09,520
Speaker 1: is a big advancement, right that we're hitting upon right now.

527
00:31:10,320 --> 00:31:14,520
Speaker 1: But it laid the further foundation, right, So initial scientific

528
00:31:14,560 --> 00:31:19,360
Speaker 1: inquiry led to further scientific inquiry and further scientific conclusions

529
00:31:20,120 --> 00:31:23,800
Speaker 1: and generalizations. All living things are made of cells, and

530
00:31:23,800 --> 00:31:27,400
Speaker 1: then it was extrapolated elsewhere. Right. Yeah, Like twenty years later,

531
00:31:27,520 --> 00:31:32,240
Speaker 1: Rudolph Virtual said, you know what, not only is everything

532
00:31:32,240 --> 00:31:35,520
Speaker 1: made of living cells, but they all come from pre

533
00:31:35,520 --> 00:31:38,480
Speaker 1: existing cells, which was a huge deal at the time

534
00:31:38,560 --> 00:31:42,840
Speaker 1: because people believed in spontaneous generation at the time, Like

535
00:31:42,880 --> 00:31:46,800
Speaker 1: if you left some um wheat seed and a sweaty shirt,

536
00:31:46,880 --> 00:31:49,800
Speaker 1: it would spawn mice. I think it was one of them.

537
00:31:50,600 --> 00:31:53,760
Speaker 1: There's a lot of weird ones pressed basil between some

538
00:31:53,840 --> 00:31:57,200
Speaker 1: bricks and you'll get a scorpion. Was one, like they

539
00:31:57,240 --> 00:31:59,880
Speaker 1: were really out there. Yeah, well the one that is

540
00:32:00,240 --> 00:32:02,240
Speaker 1: well not true, but the one that you could actually

541
00:32:02,280 --> 00:32:07,160
Speaker 1: see was rotten meat would eventually, uh spawn maggots. How

542
00:32:07,200 --> 00:32:10,520
Speaker 1: did they possibly get there? Yeah, spontaneous generation. But that's

543
00:32:10,560 --> 00:32:13,160
Speaker 1: the obvious explanation. And if you think about it, they're

544
00:32:13,200 --> 00:32:16,720
Speaker 1: working from Okam's razor and ocams. Razor says, the simplest

545
00:32:16,760 --> 00:32:21,760
Speaker 1: explanation is usually the right one, all all other things given. Well,

546
00:32:21,760 --> 00:32:24,760
Speaker 1: the thing is is spontaneous generation has never been shown

547
00:32:24,800 --> 00:32:28,480
Speaker 1: to be possible. If we get the sell thing over here,

548
00:32:28,560 --> 00:32:34,360
Speaker 1: let's investigate that. So this uh what was the guy's name? Virtual? Yes,

549
00:32:34,520 --> 00:32:36,760
Speaker 1: he's saying, okay, well, wait a minute, I get this

550
00:32:37,320 --> 00:32:39,560
Speaker 1: cell theory I'm working on that's been around for a

551
00:32:39,600 --> 00:32:42,960
Speaker 1: couple of decades. Hypothesis probably the cell hypothesis at the

552
00:32:43,600 --> 00:32:46,080
Speaker 1: nice catch. Don't feel bad though, because this article that

553
00:32:46,120 --> 00:32:50,160
Speaker 1: you sent said that scientists today like still like confuse

554
00:32:50,240 --> 00:32:54,600
Speaker 1: those terms just and there. The House of Works article

555
00:32:54,640 --> 00:32:56,760
Speaker 1: makes a good point in saying that science and everything

556
00:32:57,000 --> 00:32:59,040
Speaker 1: that has to do with it is in the scientific

557
00:32:59,080 --> 00:33:04,640
Speaker 1: method is very luid and open new interpretation and experimentation. Obviously,

558
00:33:05,000 --> 00:33:08,840
Speaker 1: but so he says, um, okay, this cell hypothesis, this

559
00:33:08,920 --> 00:33:12,880
Speaker 1: is a pretty good explanation for what we now call

560
00:33:13,000 --> 00:33:16,200
Speaker 1: spontaneous generation. He didn't do anything about it, He just

561
00:33:16,320 --> 00:33:20,000
Speaker 1: put it out there. Yeah, and then along comes Louis Pastier,

562
00:33:20,320 --> 00:33:22,680
Speaker 1: who does do something about it. He figures out a

563
00:33:22,720 --> 00:33:28,360
Speaker 1: great experiment to try to disprove spontaneous generation. Yeah, it's

564
00:33:28,360 --> 00:33:33,120
Speaker 1: pretty simple too. Um. He basically took a broth uh,

565
00:33:33,280 --> 00:33:36,600
Speaker 1: put equal amounts in two different beakers. One had a

566
00:33:36,640 --> 00:33:39,440
Speaker 1: straight neck and one had an S shaped neck. He

567
00:33:39,760 --> 00:33:42,080
Speaker 1: boiled it just to make sure everything and it was killed,

568
00:33:42,640 --> 00:33:44,960
Speaker 1: and then just let it sit there in the same conditions,

569
00:33:45,080 --> 00:33:48,560
Speaker 1: open to the to the world and are open to

570
00:33:48,600 --> 00:33:51,960
Speaker 1: the room like. It wasn't corked in other words, nor

571
00:33:52,360 --> 00:33:55,160
Speaker 1: he noticed that the one with the straight neck eventually

572
00:33:55,200 --> 00:33:58,640
Speaker 1: became cloudy and discolored, uh, meaning there was some junk

573
00:33:58,680 --> 00:34:01,400
Speaker 1: growing in there, and one in the S shaped neck

574
00:34:01,560 --> 00:34:05,920
Speaker 1: did not do anything. It remained the same. So think

575
00:34:06,080 --> 00:34:09,239
Speaker 1: what well, he thought that germs, that there were such

576
00:34:09,280 --> 00:34:12,640
Speaker 1: things as germs which leaving hoke and hook had already

577
00:34:12,640 --> 00:34:17,000
Speaker 1: shown um, And that if that in the S shaped

578
00:34:17,040 --> 00:34:20,440
Speaker 1: flask they had gotten trapped in the neck, in this

579
00:34:20,920 --> 00:34:23,799
Speaker 1: the open neck, they had been able to just enter

580
00:34:23,960 --> 00:34:28,440
Speaker 1: unobstructed and had generated there. The reason that the S

581
00:34:28,440 --> 00:34:31,880
Speaker 1: shape flask was still sterile was because there is no

582
00:34:31,960 --> 00:34:35,080
Speaker 1: such thing as spontaneous generation. If there were, then no

583
00:34:35,560 --> 00:34:39,480
Speaker 1: S shaped neck would impede anything like that, and boom,

584
00:34:39,719 --> 00:34:44,719
Speaker 1: there you have it. So he disproved that spontaneous generation

585
00:34:45,000 --> 00:34:50,040
Speaker 1: is a thing right through the scientific method. Exactly. Here's

586
00:34:50,080 --> 00:34:52,759
Speaker 1: the leap that a lot of people make, scientists included,

587
00:34:53,080 --> 00:34:56,320
Speaker 1: that really is a great disservice to science. He didn't

588
00:34:56,719 --> 00:35:00,560
Speaker 1: prove cell theory, right. What he did is take that

589
00:35:00,640 --> 00:35:08,239
Speaker 1: cell hypothesis and present some really persuasive evidence that it's

590
00:35:08,320 --> 00:35:12,040
Speaker 1: probably right. Yeah. But like this article you sent points out,

591
00:35:12,080 --> 00:35:15,040
Speaker 1: disproving something is just as important as proving something. So

592
00:35:15,080 --> 00:35:17,279
Speaker 1: here's the thing that's the most you can hope for

593
00:35:17,320 --> 00:35:20,839
Speaker 1: a science is disproving sure with science, unless you're talking

594
00:35:20,840 --> 00:35:23,680
Speaker 1: about math. With science, there's no such thing as proof.

595
00:35:24,239 --> 00:35:27,600
Speaker 1: A theory, even a law, universal law still has the

596
00:35:27,640 --> 00:35:32,640
Speaker 1: potential for being undermined by one single experiment, one single observation,

597
00:35:33,080 --> 00:35:37,480
Speaker 1: and therefore there is no real ultimate proof in science.

598
00:35:37,719 --> 00:35:41,960
Speaker 1: There's just theories and support for theories, and then ultimately

599
00:35:42,440 --> 00:35:46,840
Speaker 1: laws aim further and further support for laws, right, but

600
00:35:47,320 --> 00:35:52,880
Speaker 1: they're not proven. What science does ultimately is disproved things

601
00:35:53,000 --> 00:35:57,440
Speaker 1: or lend support for existing theories, are existing interpretations of

602
00:35:57,440 --> 00:36:00,640
Speaker 1: why things happen the way they do. And that's what

603
00:36:00,800 --> 00:36:03,160
Speaker 1: Pasture did. So if you look at the experiment, he

604
00:36:03,280 --> 00:36:08,239
Speaker 1: disproved spontaneous generation, but he lent support to the cell theory,

605
00:36:08,239 --> 00:36:10,640
Speaker 1: and probably with his experiment it went from the cell

606
00:36:10,760 --> 00:36:14,959
Speaker 1: hypothesis to the cell theory because it was just so persuasive.

607
00:36:15,520 --> 00:36:17,360
Speaker 1: And that's what a theory is. It means that a

608
00:36:17,400 --> 00:36:21,359
Speaker 1: lot of people out there who are reasonable say this

609
00:36:21,440 --> 00:36:24,520
Speaker 1: explanation is probably the right one. Yeah, it's predictive. If

610
00:36:24,520 --> 00:36:26,120
Speaker 1: you do it over and over, you're probably going to

611
00:36:26,160 --> 00:36:29,480
Speaker 1: get the same result. But that's not to say that

612
00:36:29,800 --> 00:36:32,640
Speaker 1: Pastures showed that if you do this a million in

613
00:36:32,760 --> 00:36:37,759
Speaker 1: one times that the S shaped flask won't turn cloudy.

614
00:36:38,080 --> 00:36:40,719
Speaker 1: He didn't prove that. You can't prove that, which is

615
00:36:40,760 --> 00:36:45,279
Speaker 1: again science can disprove and lent support can't prove a

616
00:36:45,440 --> 00:36:48,440
Speaker 1: very good point. So right after this message break, we're

617
00:36:48,440 --> 00:37:04,080
Speaker 1: going to get into the actual steps of the scientific method. Alright, dude,

618
00:37:04,239 --> 00:37:08,160
Speaker 1: I guess that long last we're there. Like you mentioned before,

619
00:37:08,719 --> 00:37:12,160
Speaker 1: the scientific method is fluid, and it's not like when

620
00:37:12,160 --> 00:37:15,080
Speaker 1: you get your science degree they hand you a little

621
00:37:15,160 --> 00:37:19,959
Speaker 1: laminated card like the Miranda rights that cop scary that

622
00:37:20,160 --> 00:37:22,080
Speaker 1: you know, list out all the different steps you have

623
00:37:22,160 --> 00:37:27,200
Speaker 1: to take. Um, But generally maybe I would we should

624
00:37:27,200 --> 00:37:29,240
Speaker 1: carry those around, all right, we should make little wallet

625
00:37:29,239 --> 00:37:32,239
Speaker 1: cards of the scientific method just to carry stuff you

626
00:37:32,239 --> 00:37:34,000
Speaker 1: should know about. Go on it. Oh yeah, I'll make

627
00:37:34,000 --> 00:37:39,279
Speaker 1: a million buttons random and sell them. Uh. Generally speaking, though,

628
00:37:39,640 --> 00:37:42,080
Speaker 1: it follows these steps. The first thing you do, like

629
00:37:42,120 --> 00:37:45,600
Speaker 1: we mentioned earlier, is you observe something. You ask a question.

630
00:37:46,280 --> 00:37:49,640
Speaker 1: Uh next, Like Darwin was known, I think when we

631
00:37:49,680 --> 00:37:52,000
Speaker 1: did our podcast on him too, he would spend like

632
00:37:52,000 --> 00:37:56,759
Speaker 1: a week on three square feet of ground. It was

633
00:37:56,800 --> 00:37:59,279
Speaker 1: like even longer than that. Remember, remember, wasn't it he

634
00:37:59,320 --> 00:38:01,520
Speaker 1: said that he did. He wasn't gonna MOA's lawn for

635
00:38:01,560 --> 00:38:04,919
Speaker 1: like three years because he wanted to see what what happened? Yeah,

636
00:38:04,920 --> 00:38:08,520
Speaker 1: so he's the ultimate and qualitative data of just observing,

637
00:38:08,960 --> 00:38:12,360
Speaker 1: writing things down and asking questions. And the reason you

638
00:38:12,400 --> 00:38:16,160
Speaker 1: ask your question is so you can narrow something down

639
00:38:17,000 --> 00:38:18,680
Speaker 1: like that. I think the example they use in here

640
00:38:18,760 --> 00:38:22,759
Speaker 1: is on Galapa ghosts, like the beaks of what bird?

641
00:38:22,840 --> 00:38:25,120
Speaker 1: Was it? Yeah? The finch bird. He noticed a bunch

642
00:38:25,120 --> 00:38:29,000
Speaker 1: of different beaks, so he finally posted a question like, um,

643
00:38:29,040 --> 00:38:30,880
Speaker 1: you know, I think these beaks are different for a

644
00:38:30,960 --> 00:38:34,520
Speaker 1: very specific reason, and I to find out why. Yes,

645
00:38:34,920 --> 00:38:38,239
Speaker 1: he said, what caused the diversification of finches on Gallopagus?

646
00:38:39,080 --> 00:38:43,439
Speaker 1: Who you should have done that with an accent. Well, yeah,

647
00:38:43,480 --> 00:38:46,120
Speaker 1: he would have had a British accent. Huh yeah, unless

648
00:38:46,120 --> 00:38:48,480
Speaker 1: he was pretending to be someone else. I think of

649
00:38:48,560 --> 00:38:52,200
Speaker 1: him as like, um, sounding like Hemingway or something. Oh yeah,

650
00:38:52,280 --> 00:38:55,719
Speaker 1: just drunken, violent kind of. But he wasn't. He was

651
00:38:55,760 --> 00:38:58,200
Speaker 1: like the opposite of that. Yeah. Well I saw that

652
00:38:58,280 --> 00:39:02,440
Speaker 1: the movie. Saw a picture his voice as the dude

653
00:39:03,480 --> 00:39:07,000
Speaker 1: that played him, who I can't remember right now, ed Norton. No,

654
00:39:09,400 --> 00:39:12,560
Speaker 1: I finally saw a birdman. Tho did you see that? Yeah?

655
00:39:13,080 --> 00:39:18,720
Speaker 1: Great movie? Um, I disagree you didn't like it? What? Wow?

656
00:39:19,160 --> 00:39:24,160
Speaker 1: That surprises me. Um, we'll get into that off year.

657
00:39:25,320 --> 00:39:30,279
Speaker 1: So uh sorry, you just threw me with that. And

658
00:39:30,280 --> 00:39:32,520
Speaker 1: he's on galapagost and he's like, what the heck with

659
00:39:32,560 --> 00:39:35,359
Speaker 1: all these different finches one small island? Why would there

660
00:39:35,360 --> 00:39:39,239
Speaker 1: be different species of finch so and why are they

661
00:39:39,239 --> 00:39:43,840
Speaker 1: all seeming to survive and coexist so? Well, what's what

662
00:39:44,080 --> 00:39:47,280
Speaker 1: makes Yeah? Then he leads to the question, what's making

663
00:39:47,320 --> 00:39:50,880
Speaker 1: all of these species of finches so diverse? Right? Or?

664
00:39:50,920 --> 00:39:54,480
Speaker 1: Bill Harris uses a pretty good example. That's something everyone

665
00:39:54,520 --> 00:39:58,359
Speaker 1: can understand, Like what car body shape is the best

666
00:39:58,400 --> 00:40:01,600
Speaker 1: for air resistance? Like one the shape like a box,

667
00:40:01,719 --> 00:40:04,400
Speaker 1: or one the shape like aerodynamic, like a bird, and

668
00:40:04,440 --> 00:40:07,480
Speaker 1: he carries that out. In the next step, you formulate

669
00:40:07,560 --> 00:40:12,839
Speaker 1: your hypothesis based on your you know, for knowledge and

670
00:40:13,360 --> 00:40:16,799
Speaker 1: maybe observations, like so, you know what, I think that

671
00:40:16,840 --> 00:40:19,319
Speaker 1: a car shaped like a bird is probably more aerodynamic

672
00:40:19,320 --> 00:40:21,240
Speaker 1: than one shape like a box. Yeah, if you're thinking,

673
00:40:21,239 --> 00:40:23,400
Speaker 1: if you're the type of person who's sitting around asking

674
00:40:23,480 --> 00:40:26,880
Speaker 1: questions about aerodynamics, you probably already have some sort of

675
00:40:26,920 --> 00:40:30,120
Speaker 1: sense that a box is less aerodynamic than a bird.

676
00:40:30,680 --> 00:40:33,960
Speaker 1: Boxes rarely fly unless they're carried by one of those

677
00:40:33,960 --> 00:40:39,200
Speaker 1: delightful Amazon delivery drones. They don't have those yet, right,

678
00:40:39,239 --> 00:40:42,200
Speaker 1: They're not gonna do that, are they. There's like a

679
00:40:42,239 --> 00:40:46,279
Speaker 1: pizza delivery drone service. Man, I think where you have

680
00:40:47,120 --> 00:40:50,200
Speaker 1: PiZZ are grilled cheese in New York and you go

681
00:40:50,320 --> 00:40:52,800
Speaker 1: stand on an XF you order and it like comes

682
00:40:52,840 --> 00:40:54,840
Speaker 1: and drops it. That is the dumbest thing of it.

683
00:40:55,920 --> 00:40:58,000
Speaker 1: And I can't wait to do it where they're making

684
00:40:58,040 --> 00:41:01,320
Speaker 1: a lot of money. Let's pretty funny. Um, Yet we

685
00:41:01,360 --> 00:41:04,080
Speaker 1: can't get food to the homeless somehow exactly, we can

686
00:41:04,160 --> 00:41:06,600
Speaker 1: drop a grilled cheese on someone's head. They're like you

687
00:41:06,640 --> 00:41:11,239
Speaker 1: homeless guy, get off that X exactly. Um, alright, So

688
00:41:11,480 --> 00:41:14,440
Speaker 1: your hypothesis, I don't think we ever mentioned, is typically

689
00:41:14,440 --> 00:41:18,080
Speaker 1: represented as an if then statement. Yeah, if you're doing

690
00:41:18,080 --> 00:41:25,359
Speaker 1: good sciences, Yeah, like if the cars profile. Uh, well,

691
00:41:25,440 --> 00:41:28,120
Speaker 1: the the example he uses if the body's profile related

692
00:41:28,160 --> 00:41:31,120
Speaker 1: to the amount of air it produces, which is the

693
00:41:31,160 --> 00:41:34,120
Speaker 1: more general statement. Yeah, that's like based on a theory. Yeah,

694
00:41:34,200 --> 00:41:36,600
Speaker 1: and it's gonna get more specific than the car designed

695
00:41:36,640 --> 00:41:39,800
Speaker 1: like the body of a bird will be more aerodynamic

696
00:41:39,840 --> 00:41:42,920
Speaker 1: than one like a box. So that's inductive reasoning, starting

697
00:41:42,920 --> 00:41:45,920
Speaker 1: with a broad statement and going to something narrow, and

698
00:41:46,000 --> 00:41:49,200
Speaker 1: it's if then at the same time. Yeah, and now

699
00:41:49,239 --> 00:41:51,600
Speaker 1: you have a test. You have a question that can

700
00:41:51,640 --> 00:41:54,080
Speaker 1: be answered, you can figure out a way to answer it. Yeah,

701
00:41:54,120 --> 00:41:57,680
Speaker 1: And he points out to this is pretty important that, uh,

702
00:41:57,719 --> 00:42:01,240
Speaker 1: your hypothesis, if it's formulated correctly, means it is testable

703
00:42:01,520 --> 00:42:05,239
Speaker 1: and it's falsifiable, which are often one and the same,

704
00:42:06,000 --> 00:42:08,719
Speaker 1: you know. Yeah, And that's again we go to the

705
00:42:08,960 --> 00:42:14,040
Speaker 1: people who say that they're they're soft sciences aren't real science.

706
00:42:14,080 --> 00:42:17,680
Speaker 1: There's pseudoscience because a lot of the data that they

707
00:42:17,719 --> 00:42:19,640
Speaker 1: come up with, a lot of the hypotheses they come

708
00:42:19,719 --> 00:42:24,600
Speaker 1: up with aren't falsifiable, they're not testable. It's a it's

709
00:42:24,640 --> 00:42:27,759
Speaker 1: a thing, it's an issue, it's a thing. So next

710
00:42:27,840 --> 00:42:32,840
Speaker 1: up in the steps, you're gonna experiment. And when you experiment,

711
00:42:33,120 --> 00:42:34,799
Speaker 1: you can't just go in there willy nilly and do

712
00:42:34,840 --> 00:42:37,480
Speaker 1: whatever you want. Um, you have to set up specific

713
00:42:37,520 --> 00:42:41,560
Speaker 1: conditions and they must be controlled, and you want to

714
00:42:41,920 --> 00:42:45,000
Speaker 1: everything that's supposed to be identical needs to be identical.

715
00:42:45,120 --> 00:42:48,560
Speaker 1: So basically, you have two variables. At least, you have

716
00:42:48,600 --> 00:42:52,160
Speaker 1: an independent variable and you have a dependent variable. And

717
00:42:52,239 --> 00:42:56,560
Speaker 1: if you're talking about car shape, that is the independent

718
00:42:56,640 --> 00:43:00,520
Speaker 1: variable in this study, that's the one that's manipulated exactly,

719
00:43:00,719 --> 00:43:03,360
Speaker 1: it's the one you're controlling. The independent variable is the

720
00:43:03,400 --> 00:43:06,680
Speaker 1: one you, the researcher, is controlling. So in this case,

721
00:43:06,960 --> 00:43:10,239
Speaker 1: you're controlling the shape of the car. You have yourself

722
00:43:10,320 --> 00:43:13,120
Speaker 1: a bird shaped car and you have yourself a box

723
00:43:13,120 --> 00:43:16,280
Speaker 1: shaped car. So the shape of the car changed because

724
00:43:16,360 --> 00:43:19,080
Speaker 1: you made it change. Now, when you blast a bunch

725
00:43:19,080 --> 00:43:22,280
Speaker 1: of air over it during your experiment, what you're measuring

726
00:43:22,640 --> 00:43:27,720
Speaker 1: is the dependent variable. So you're measuring what happens based

727
00:43:27,760 --> 00:43:30,719
Speaker 1: on the change that you made. That's right, and you

728
00:43:30,760 --> 00:43:33,000
Speaker 1: want to you, you want to study one single variable

729
00:43:33,040 --> 00:43:36,160
Speaker 1: at a time, basically, yeah, don't get fancy, just just

730
00:43:36,239 --> 00:43:39,560
Speaker 1: do good science, step by step methodical. You also have

731
00:43:39,640 --> 00:43:42,600
Speaker 1: to have your control group in any experiment, uh, and

732
00:43:42,800 --> 00:43:47,360
Speaker 1: an experimental group and the controlled group is what's gonna

733
00:43:47,440 --> 00:43:52,160
Speaker 1: allow you to compare the test results to that baseline measurement. Yeah,

734
00:43:52,239 --> 00:43:55,239
Speaker 1: and you need that baseline measurement, so it's not just

735
00:43:55,320 --> 00:43:59,160
Speaker 1: like chance. Basically exactly like if Pastor had just done

736
00:43:59,200 --> 00:44:02,279
Speaker 1: the S shaped now can nothing happen? He wouldn't have

737
00:44:02,320 --> 00:44:05,719
Speaker 1: necessarily been able to say that he was right, even

738
00:44:05,760 --> 00:44:08,920
Speaker 1: though he was right, he needed that control, which was

739
00:44:08,960 --> 00:44:11,920
Speaker 1: the open flask, right. Or with the cars, you need

740
00:44:11,960 --> 00:44:14,799
Speaker 1: two cars, like you said, one bird shaped and one

741
00:44:14,840 --> 00:44:17,600
Speaker 1: box shaped, right, Or then maybe in this case, since

742
00:44:17,680 --> 00:44:19,480
Speaker 1: the bird shape and the box shape both show up

743
00:44:19,480 --> 00:44:22,440
Speaker 1: in the hypothsis, you need a third like egg shaped

744
00:44:22,520 --> 00:44:24,880
Speaker 1: one or something like that. I bet that would be

745
00:44:24,880 --> 00:44:30,120
Speaker 1: pretty streamline. Yeah. Yeah. But the key though, is all

746
00:44:30,160 --> 00:44:32,440
Speaker 1: of those variables have to be um. All the other

747
00:44:32,480 --> 00:44:34,920
Speaker 1: variables have to be the same, like you have to

748
00:44:34,960 --> 00:44:37,000
Speaker 1: have them. They have to be the same weight, they

749
00:44:37,000 --> 00:44:40,920
Speaker 1: have to be painted the same, the tires, everything, the windows,

750
00:44:41,560 --> 00:44:43,919
Speaker 1: one can't have an antenna on the other not They've

751
00:44:43,920 --> 00:44:46,880
Speaker 1: all they've got to be identical other than the one variable, right,

752
00:44:46,960 --> 00:44:50,440
Speaker 1: the independent variable that you're that's the one you want different.

753
00:44:50,480 --> 00:44:53,160
Speaker 1: Everything else you want the same, or else it's possible that, oh,

754
00:44:53,200 --> 00:44:56,759
Speaker 1: well this one had bigger tires, so that actually made

755
00:44:56,760 --> 00:44:59,319
Speaker 1: it more aerodynamic. Yeah, and you're just doing yourself a

756
00:44:59,360 --> 00:45:01,960
Speaker 1: favor by doing all that stuff. You know, you want

757
00:45:02,000 --> 00:45:06,480
Speaker 1: to rule out everything else but that one variable. After that,

758
00:45:06,520 --> 00:45:10,319
Speaker 1: you want to analyze your data so you can draw

759
00:45:10,360 --> 00:45:15,120
Speaker 1: your conclusion. And sometimes it's kind of straightforward and easy.

760
00:45:15,200 --> 00:45:17,600
Speaker 1: Sometimes takes a lot of work and a lot of

761
00:45:18,280 --> 00:45:23,520
Speaker 1: various tools. Let's say you're just blasting a car and

762
00:45:23,600 --> 00:45:27,640
Speaker 1: a wind tunnel. You're measuring the wind resistance using certain

763
00:45:27,760 --> 00:45:30,800
Speaker 1: awesome instruments and that kind of stuff, and you're taking

764
00:45:30,800 --> 00:45:33,800
Speaker 1: that data, and then afterwards you're gonna analyze. You're gonna

765
00:45:33,840 --> 00:45:37,280
Speaker 1: compare the data that you gathered from the bird shaped

766
00:45:37,320 --> 00:45:40,319
Speaker 1: car the box shaped car, and then the control the

767
00:45:40,320 --> 00:45:43,120
Speaker 1: egg shaped car. You're gonna compare them and you're gonna say, well,

768
00:45:43,440 --> 00:45:47,320
Speaker 1: the wind resistance was less for the bird shaped car

769
00:45:47,920 --> 00:45:52,040
Speaker 1: then the box shaped car, which means that my hypothesis

770
00:45:52,480 --> 00:45:55,479
Speaker 1: was correct, and here all the data points, whereas Louis

771
00:45:55,520 --> 00:45:58,719
Speaker 1: paste or could just say look at the beakers exactly.

772
00:45:59,200 --> 00:46:02,439
Speaker 1: Don't be an idiot it I'm a scientist. That one's

773
00:46:02,440 --> 00:46:06,400
Speaker 1: got gross stuff. You can see it, right. But the

774
00:46:06,440 --> 00:46:10,399
Speaker 1: other thing about science to chuck ideally is let's say

775
00:46:10,440 --> 00:46:14,200
Speaker 1: that egg shaped one turned out the control group turned

776
00:46:14,200 --> 00:46:17,200
Speaker 1: out to have better wind resistance than anything. Well, just

777
00:46:17,280 --> 00:46:20,319
Speaker 1: by virtue of carrying out this experiment correctly, you would

778
00:46:20,320 --> 00:46:24,319
Speaker 1: have stumbled upon an even better aerodynamic design, and you

779
00:46:24,320 --> 00:46:27,520
Speaker 1: would have come up with that little egg shaped Mercedes

780
00:46:27,600 --> 00:46:30,680
Speaker 1: suv that was so huge, like ten years ago, the

781
00:46:30,760 --> 00:46:35,360
Speaker 1: Mercedes egg coming to a store near you. So um.

782
00:46:35,400 --> 00:46:38,120
Speaker 1: That's a big, big part of the scientific method is

783
00:46:39,000 --> 00:46:46,319
Speaker 1: carrying out a a an experiment, controlling the variables, analyzing

784
00:46:46,360 --> 00:46:49,400
Speaker 1: the data. And then there's a step that he missed

785
00:46:49,440 --> 00:46:53,440
Speaker 1: that is very rarely part of a scientific method list

786
00:46:54,120 --> 00:46:57,520
Speaker 1: that is to share your data. And this is a

787
00:46:57,600 --> 00:47:00,759
Speaker 1: huge problem with science right now. The article you said

788
00:47:00,760 --> 00:47:03,760
Speaker 1: it was really eye opening. Uh, scientific research has changed

789
00:47:03,800 --> 00:47:05,920
Speaker 1: the world. Now it needs to change itself. It's an

790
00:47:05,920 --> 00:47:08,640
Speaker 1: economist article. It's up on the internet. Yeah, it was

791
00:47:08,719 --> 00:47:12,960
Speaker 1: kind of scary that it's I mean, here's some of

792
00:47:12,960 --> 00:47:15,760
Speaker 1: the data he points out is one rule of thumb

793
00:47:15,800 --> 00:47:21,839
Speaker 1: among biotech venture capitalists is about half published research can't

794
00:47:21,840 --> 00:47:25,200
Speaker 1: even be replicated. And on the biotech firm and Jin

795
00:47:25,280 --> 00:47:28,360
Speaker 1: found that they could reproduce only six of their fifty

796
00:47:28,400 --> 00:47:32,400
Speaker 1: three landmark studies in cancer research. So you can't repeat

797
00:47:32,440 --> 00:47:36,360
Speaker 1: these things. It's like everyone's fighting for dollars in fame,

798
00:47:37,040 --> 00:47:39,400
Speaker 1: and maybe not fame, but to some of our career

799
00:47:39,400 --> 00:47:43,240
Speaker 1: advancements such that they're kind of not doing that final

800
00:47:43,239 --> 00:47:47,000
Speaker 1: step any longer. No, and it's not necessarily just them,

801
00:47:47,160 --> 00:47:50,480
Speaker 1: it's the other scientists aren't going back and saying, well,

802
00:47:50,600 --> 00:47:54,560
Speaker 1: let me see if your results are reproducible. People are

803
00:47:54,640 --> 00:47:57,960
Speaker 1: just taking it on faith. We need another Roger Bacon

804
00:47:58,040 --> 00:47:59,960
Speaker 1: to come along and be like, dude, we can't as

805
00:48:00,120 --> 00:48:03,360
Speaker 1: blindly accept that one person carried out this one study

806
00:48:03,880 --> 00:48:06,520
Speaker 1: and then just go do clinical trials on it without

807
00:48:06,560 --> 00:48:10,279
Speaker 1: anybody reproducing it to see if the results can be

808
00:48:10,400 --> 00:48:13,600
Speaker 1: verified independently. Yeah. Because uh, and this is a good

809
00:48:13,600 --> 00:48:16,400
Speaker 1: time to mention bias. There is such a thing as bias,

810
00:48:16,600 --> 00:48:22,200
Speaker 1: and it still happens. Um. A scientist is usually out

811
00:48:22,280 --> 00:48:26,279
Speaker 1: to prove something or disprove something that they want a

812
00:48:26,360 --> 00:48:30,600
Speaker 1: specific result. Like, even if you're super open minded, you're

813
00:48:30,600 --> 00:48:34,360
Speaker 1: probably hoping to disprove or prove something one way or

814
00:48:34,400 --> 00:48:38,520
Speaker 1: the other, and your confirmation bias might you know, even

815
00:48:38,560 --> 00:48:41,040
Speaker 1: if you don't think you're doing it, you might nudge

816
00:48:41,040 --> 00:48:44,799
Speaker 1: out some results that don't support your hypothesis, and so

817
00:48:44,880 --> 00:48:49,799
Speaker 1: you won't make it into that awesome journal um which

818
00:48:50,080 --> 00:48:53,359
Speaker 1: this author points out that journals need to start uh

819
00:48:54,000 --> 00:48:58,920
Speaker 1: putting in what he calls uninteresting results and experiments right

820
00:48:59,120 --> 00:49:02,120
Speaker 1: or like this if it's not sexy, right, or studies

821
00:49:02,160 --> 00:49:06,360
Speaker 1: that failed to show that their hypothesis was correct. Stuff

822
00:49:06,440 --> 00:49:10,399
Speaker 1: is disproved. Those things still need to well not even disproved. Well, yeah,

823
00:49:10,440 --> 00:49:12,359
Speaker 1: I guess it is disproved, but yes, like the guy

824
00:49:12,400 --> 00:49:17,319
Speaker 1: set out to say, like the red balloon uses less

825
00:49:17,360 --> 00:49:20,440
Speaker 1: helium than a silver balloon, and it turns out that

826
00:49:20,520 --> 00:49:23,120
Speaker 1: now they use the same amount of helium. Well, if

827
00:49:23,160 --> 00:49:26,400
Speaker 1: that study gets published and put out there into the

828
00:49:26,560 --> 00:49:30,160
Speaker 1: scientific literature on helium and balloons, then it's going to

829
00:49:30,239 --> 00:49:33,480
Speaker 1: prevent some other scientists down the road from wasting time, money,

830
00:49:33,560 --> 00:49:39,560
Speaker 1: and helium, which, as you'll remember, is an increasingly needed commodity. UM.

831
00:49:39,600 --> 00:49:43,800
Speaker 1: By carrying out the same experiment, whether whether the results

832
00:49:43,840 --> 00:49:47,040
Speaker 1: are positive or negative, or what the study is meant

833
00:49:47,040 --> 00:49:49,600
Speaker 1: to be shared. And that's the point of the scientific method,

834
00:49:49,640 --> 00:49:52,360
Speaker 1: is to to reduce bias. And if you follow it

835
00:49:52,400 --> 00:49:54,960
Speaker 1: all the way through ideally and do all of the steps,

836
00:49:54,960 --> 00:49:59,319
Speaker 1: including share your research whether it's happy or sad, then

837
00:50:00,080 --> 00:50:03,359
Speaker 1: science benefits. The world benefits, and by not doing that,

838
00:50:03,480 --> 00:50:06,480
Speaker 1: the world does not benefit. Yeah. He points out that

839
00:50:06,960 --> 00:50:11,680
Speaker 1: these days only four percent of published papers are quote

840
00:50:11,760 --> 00:50:15,560
Speaker 1: unquote negative results, and it used to be like or

841
00:50:15,600 --> 00:50:18,560
Speaker 1: more um And he says, because it's a lot of

842
00:50:18,560 --> 00:50:22,160
Speaker 1: it has to do with this sort of you know,

843
00:50:23,200 --> 00:50:25,719
Speaker 1: getting in these journals and you're the rockstar scientists and

844
00:50:25,719 --> 00:50:28,239
Speaker 1: this study is super sexy. Like if they kind of

845
00:50:28,320 --> 00:50:30,359
Speaker 1: quit going that route and made it what it should be,

846
00:50:30,800 --> 00:50:35,440
Speaker 1: then research dollars would be better spent and people could

847
00:50:35,560 --> 00:50:37,839
Speaker 1: you know, he said, the peer reviewed thing isn't even

848
00:50:37,840 --> 00:50:40,720
Speaker 1: all extract up to know that. He mentioned to study

849
00:50:40,920 --> 00:50:43,600
Speaker 1: from a medical journal that gave a bunch of peer

850
00:50:43,600 --> 00:50:48,960
Speaker 1: reviewers some stuff with deliberate errors inserted into the research,

851
00:50:49,000 --> 00:50:51,719
Speaker 1: into the studies, and even when they were told that

852
00:50:51,760 --> 00:50:54,360
Speaker 1: they were being tested to find this, they still missed

853
00:50:54,360 --> 00:50:57,480
Speaker 1: a lot of it. Yeah, so, yeah, the science used

854
00:50:57,520 --> 00:50:59,480
Speaker 1: to kind of re evaluate the way it's carrying out

855
00:50:59,520 --> 00:51:04,560
Speaker 1: sciences not science. The problem isn't science itself. The problem

856
00:51:04,600 --> 00:51:06,759
Speaker 1: isn't the scientific method. It's the way that it's being

857
00:51:06,840 --> 00:51:08,920
Speaker 1: used or not followed through. And a lot of it

858
00:51:08,920 --> 00:51:11,919
Speaker 1: has to do with academia and the people funding science. Yeah,

859
00:51:11,920 --> 00:51:14,120
Speaker 1: and he said, you know, these days there's a seven

860
00:51:14,120 --> 00:51:17,560
Speaker 1: million researchers, and back in the day, even in like

861
00:51:17,640 --> 00:51:21,040
Speaker 1: the nineteen fifties, there were like a few thousand maybe, right,

862
00:51:21,080 --> 00:51:23,879
Speaker 1: So there's just a lot of career competition, he calls

863
00:51:23,920 --> 00:51:27,680
Speaker 1: it careerism. And so you fake a result or two,

864
00:51:27,760 --> 00:51:30,480
Speaker 1: or you just nudge out some results that don't support

865
00:51:30,520 --> 00:51:34,439
Speaker 1: your hypothesis. You want the bigger paycheck or the fame

866
00:51:34,520 --> 00:51:38,600
Speaker 1: or notoriety, and all of a sudden, science is not science. Yeah,

867
00:51:39,120 --> 00:51:45,239
Speaker 1: you know, it's pseudoscience exactly. And speaking of pseudoscience, I

868
00:51:45,239 --> 00:51:47,640
Speaker 1: think we've reached the point where, um, we should talk

869
00:51:47,680 --> 00:51:50,759
Speaker 1: about the limitations of the scientific method, because it does

870
00:51:50,840 --> 00:51:54,280
Speaker 1: have its limits, right, Like the way that the scientific

871
00:51:54,360 --> 00:51:57,040
Speaker 1: method is set up, especially if you go through, um,

872
00:51:57,080 --> 00:52:01,200
Speaker 1: if you include falsification, which most science is now, say

873
00:52:01,440 --> 00:52:05,360
Speaker 1: is a thing like falsifiability of your hypothesis means that

874
00:52:05,440 --> 00:52:09,239
Speaker 1: you have a real scientific hypothesis there if it can

875
00:52:09,280 --> 00:52:12,880
Speaker 1: be disproven by some observation or some measurement or whatever,

876
00:52:13,080 --> 00:52:16,919
Speaker 1: then it's falsifiable. And if it's not falsifiable, then it's

877
00:52:16,960 --> 00:52:21,000
Speaker 1: not really science. So the thing is for something to

878
00:52:21,040 --> 00:52:23,440
Speaker 1: be falsifiable. And it was actually a philosopher that came

879
00:52:23,520 --> 00:52:26,160
Speaker 1: up with the concept of falsification, a guy named Carl

880
00:52:26,200 --> 00:52:29,399
Speaker 1: Popper in the ninet thirties, and he was the one

881
00:52:29,440 --> 00:52:31,960
Speaker 1: that said, like, you're you have to be able to

882
00:52:32,040 --> 00:52:36,200
Speaker 1: falsify something for it to be disproven or supported, and

883
00:52:36,239 --> 00:52:39,680
Speaker 1: if not, then it's pseudoscience. Well, part and parcel of

884
00:52:39,760 --> 00:52:42,279
Speaker 1: that is that what you're saying has to be able

885
00:52:42,280 --> 00:52:46,359
Speaker 1: to be detected empirically. There's some way that it has

886
00:52:46,400 --> 00:52:49,440
Speaker 1: to the presence of it has to be measured or inferred.

887
00:52:50,160 --> 00:52:53,480
Speaker 1: And so a lot of people say, well, then with

888
00:52:53,600 --> 00:52:57,200
Speaker 1: the scientific method it reaches it's the limits of its

889
00:52:57,239 --> 00:53:00,279
Speaker 1: current usefulness when it tries to explain the supernatural role.

890
00:53:01,000 --> 00:53:06,359
Speaker 1: When somebody says, like, are real exactly you can't prove that, well,

891
00:53:06,400 --> 00:53:10,719
Speaker 1: you also can't disprove it either, right, and So if

892
00:53:10,800 --> 00:53:15,440
Speaker 1: you are a scientist who says, uh, because the scientific

893
00:53:15,520 --> 00:53:20,000
Speaker 1: method can't prove or disprove the existence of ghosts or God,

894
00:53:20,840 --> 00:53:23,640
Speaker 1: there is no such thing as ghosts or God, you're

895
00:53:23,680 --> 00:53:27,000
Speaker 1: making a leap of faith just as much as the

896
00:53:27,040 --> 00:53:30,239
Speaker 1: same person who says science can't prove or disprove the

897
00:53:30,239 --> 00:53:33,600
Speaker 1: existence of ghosts or God, therefore God's and ghosts are real.

898
00:53:34,719 --> 00:53:37,359
Speaker 1: They're both leaps of faith. And that really the most

899
00:53:37,400 --> 00:53:41,239
Speaker 1: scientific approach to the existence of the supernatural, whether it

900
00:53:41,320 --> 00:53:44,680
Speaker 1: is a ghost or God, is that we simply don't know,

901
00:53:45,120 --> 00:53:48,680
Speaker 1: and that we cannot know scientifically. And but that doesn't

902
00:53:48,680 --> 00:53:51,680
Speaker 1: mean that it does exist or doesn't exist. And that's

903
00:53:51,680 --> 00:53:54,840
Speaker 1: saying that science shows that it does or doesn't exist,

904
00:53:54,960 --> 00:53:58,040
Speaker 1: is by by definition, the opposite of what science shows.

905
00:53:58,520 --> 00:54:03,640
Speaker 1: Science shows neither. It's not capable of showing or showing

906
00:54:03,680 --> 00:54:06,960
Speaker 1: that something doesn't exist. That's a good point. Uh. The

907
00:54:07,000 --> 00:54:10,440
Speaker 1: other place where science can get corrupted is when it

908
00:54:10,600 --> 00:54:14,319
Speaker 1: blurs the lines, or when people blur the lines between uh,

909
00:54:14,600 --> 00:54:19,920
Speaker 1: moral judgments and science value judgments. Like you can study

910
00:54:19,960 --> 00:54:22,440
Speaker 1: global warming, you can study cause and effect, you can

911
00:54:22,440 --> 00:54:27,080
Speaker 1: report data, But when you make that secondly to say,

912
00:54:27,440 --> 00:54:29,440
Speaker 1: and this is a scientist. I mean, someone can come

913
00:54:29,440 --> 00:54:32,000
Speaker 1: along and say global warming is bad, shouldn't drive your suv.

914
00:54:32,400 --> 00:54:35,920
Speaker 1: That's fine, But a scientist can't do a study and

915
00:54:36,000 --> 00:54:40,440
Speaker 1: say that because that's uh, that's a value judgment. And

916
00:54:40,520 --> 00:54:44,640
Speaker 1: that's where science can get corrupted pretty much. Right. You can.

917
00:54:44,760 --> 00:54:47,520
Speaker 1: You can study global warming and results until the cows

918
00:54:47,600 --> 00:54:51,160
Speaker 1: come home, but you can't assert that if you use

919
00:54:51,239 --> 00:54:54,440
Speaker 1: this lightbulb, you're a bad person, right, or um ocean

920
00:54:54,440 --> 00:54:58,200
Speaker 1: and acidification is bad. It's not good for humans, but

921
00:54:58,320 --> 00:55:01,600
Speaker 1: if you're a jelly fits it's awesome, you know. So, yes,

922
00:55:01,880 --> 00:55:04,719
Speaker 1: and again you made a great point. It's not science,

923
00:55:05,000 --> 00:55:10,520
Speaker 1: it's people using science to make value judgments. So ultimately,

924
00:55:10,760 --> 00:55:14,239
Speaker 1: the scientific method, although it does have its limitations in

925
00:55:14,280 --> 00:55:18,920
Speaker 1: that it needs empirical data, uh to prove or disprove something,

926
00:55:19,800 --> 00:55:23,160
Speaker 1: it's not that it's not flawed. That's not a flaw.

927
00:55:23,239 --> 00:55:27,640
Speaker 1: That's a limitation, and it's it's when it's misused then

928
00:55:27,680 --> 00:55:31,360
Speaker 1: it its results become flawed or skewed, and that's the

929
00:55:31,400 --> 00:55:34,360
Speaker 1: people doming it. Man, not science, that's right. It's pretty

930
00:55:34,400 --> 00:55:36,960
Speaker 1: interesting stuff. Yeah, man, this is a good one. I

931
00:55:37,000 --> 00:55:39,279
Speaker 1: thought so too. Man. Let us start out with a

932
00:55:39,320 --> 00:55:43,640
Speaker 1: bang boom all downhill from here. If you want to

933
00:55:43,640 --> 00:55:47,000
Speaker 1: know more about scientific method, check out that article on

934
00:55:47,040 --> 00:55:50,640
Speaker 1: the economists, check out explorables uh, and then of course

935
00:55:50,719 --> 00:55:53,239
Speaker 1: check out the scientific method in the search bar at

936
00:55:53,239 --> 00:55:55,600
Speaker 1: how stuff works dot com. And since I said that

937
00:55:55,719 --> 00:56:01,439
Speaker 1: it's time for listener mail, that's right, but quickly before

938
00:56:01,480 --> 00:56:06,160
Speaker 1: listener mail. Uh. We get asked by listeners all the time,

939
00:56:06,239 --> 00:56:08,640
Speaker 1: what can we do? Since you have a free podcast,

940
00:56:08,680 --> 00:56:10,440
Speaker 1: we can't pay for it. What can we do to

941
00:56:10,480 --> 00:56:13,400
Speaker 1: help you? Guys? And one thing you can do that

942
00:56:13,440 --> 00:56:17,200
Speaker 1: we would appreciate is, uh, go to iTunes and leave

943
00:56:17,320 --> 00:56:20,919
Speaker 1: a rating and a review for us. That makes big,

944
00:56:20,960 --> 00:56:23,360
Speaker 1: big difference in keeping us up there in the rankings,

945
00:56:23,719 --> 00:56:26,600
Speaker 1: which means more people find stuff you should know after

946
00:56:26,640 --> 00:56:29,680
Speaker 1: they listen to Cereal they'll just say, well, jeez, there's

947
00:56:29,680 --> 00:56:34,080
Speaker 1: other podcasts in the world, what is this pod? So

948
00:56:34,239 --> 00:56:36,760
Speaker 1: ratings and reviews really help us out and it doesn't

949
00:56:36,800 --> 00:56:39,480
Speaker 1: cost you anything but a few minutes. Um, be honest,

950
00:56:39,520 --> 00:56:41,759
Speaker 1: We're not saying go leave us some great review, but

951
00:56:42,080 --> 00:56:46,239
Speaker 1: go leave us a great review. You said it, uh,

952
00:56:46,280 --> 00:56:48,360
Speaker 1: and told tell one person about stuff you should know.

953
00:56:48,360 --> 00:56:50,720
Speaker 1: We would appreciate that. To turn somebody onto the show

954
00:56:51,360 --> 00:56:56,400
Speaker 1: and um, that's it. That's our version of a pledge drive. Wow,

955
00:56:56,760 --> 00:56:59,800
Speaker 1: we do what once every three years. Not not very

956
00:57:00,000 --> 00:57:03,439
Speaker 1: obnoxious and it last alright. So on the listener mail,

957
00:57:03,520 --> 00:57:06,319
Speaker 1: this is from my sister in law. Actually, oh yeah,

958
00:57:06,320 --> 00:57:11,480
Speaker 1: there's some nepotism. Yeah, Jenny, Jenny Bryant, she mentioned in

959
00:57:11,520 --> 00:57:14,919
Speaker 1: the home school episode homeschooled her kids for a little while,

960
00:57:15,400 --> 00:57:19,240
Speaker 1: and she sort of corrected me. Uh, love the homeschooling episode, guys.

961
00:57:19,280 --> 00:57:22,120
Speaker 1: One very big trend these days in the home schooling

962
00:57:22,120 --> 00:57:27,560
Speaker 1: community is what Abbey, my niece does, which is hybrid homeschooling.

963
00:57:28,120 --> 00:57:30,040
Speaker 1: So two to three days a week she is at

964
00:57:30,080 --> 00:57:32,720
Speaker 1: school and then the rest of the time she does

965
00:57:32,760 --> 00:57:36,520
Speaker 1: a plant. She's not a plant. Uh, the rest of

966
00:57:36,520 --> 00:57:38,280
Speaker 1: time she's at home. So she says it's a great

967
00:57:38,320 --> 00:57:41,640
Speaker 1: option with curriculum provided and new topics sought at school

968
00:57:41,720 --> 00:57:44,880
Speaker 1: and then worked out at home. Many of these schools

969
00:57:44,880 --> 00:57:49,000
Speaker 1: are accredited making getting into college, including Ivy League schools,

970
00:57:49,000 --> 00:57:53,520
Speaker 1: Hassle Free and Abbey School has sports teams homecoming. Abbey

971
00:57:53,600 --> 00:57:57,640
Speaker 1: is actually an excellent volleyball player. Beta club newspaper staff

972
00:57:57,680 --> 00:58:00,960
Speaker 1: all the good stuff. The flexibility is great, for families,

973
00:58:01,000 --> 00:58:03,440
Speaker 1: and we are huge fans of how the hybrid approach

974
00:58:03,480 --> 00:58:06,600
Speaker 1: prepared students for college by allowing them time outside of

975
00:58:06,640 --> 00:58:10,200
Speaker 1: class to manage their work and life schedules. So that's

976
00:58:10,200 --> 00:58:15,240
Speaker 1: from Jenny Nice actually via text. The first listener mail

977
00:58:15,360 --> 00:58:17,800
Speaker 1: via text. How did you print that out? Did you

978
00:58:17,800 --> 00:58:21,480
Speaker 1: retype it and print it? No? Dude, are you serious?

979
00:58:21,960 --> 00:58:25,520
Speaker 1: You can print from texts? No, you just copy pasted

980
00:58:25,560 --> 00:58:28,480
Speaker 1: to an evening. Oh yeah, yeah, I forgot about that. Men, then,

981
00:58:28,640 --> 00:58:31,440
Speaker 1: how in the world did you did you do that?

982
00:58:31,480 --> 00:58:35,320
Speaker 1: With your thoughts? I have a niece who is excellent

983
00:58:35,360 --> 00:58:37,840
Speaker 1: at volleyball too. Yeah, how would we should get them together?

984
00:58:38,560 --> 00:58:40,880
Speaker 1: He's I don't know, it's time of eleven, okay, something

985
00:58:40,920 --> 00:58:43,520
Speaker 1: like that. Abby just turned thirteen, so there. Oh, maybe

986
00:58:43,560 --> 00:58:47,000
Speaker 1: they face off against Yeah? Is she in Atlanta? Yes,

987
00:58:47,080 --> 00:58:51,400
Speaker 1: she's something Canton. You never know where's Evie. She's in Roswell.

988
00:58:51,480 --> 00:58:53,480
Speaker 1: But they I think with volleyball they kind of have

989
00:58:53,880 --> 00:58:56,919
Speaker 1: played all over the state. Bizarre if they played each other. Yeah,

990
00:58:56,920 --> 00:58:58,680
Speaker 1: we'll just see each other at a match one day,

991
00:58:59,120 --> 00:59:01,680
Speaker 1: on opposite sides of the court with our arms folded. Yeah,

992
00:59:03,400 --> 00:59:06,479
Speaker 1: what else? I got nothing else? Well, like Chuck said,

993
00:59:06,520 --> 00:59:09,440
Speaker 1: go leave us a review, and if you want to

994
00:59:09,440 --> 00:59:11,240
Speaker 1: get in touch with us, you can tweet to us

995
00:59:11,280 --> 00:59:13,960
Speaker 1: at s y s K podcast. You can join us

996
00:59:13,960 --> 00:59:17,200
Speaker 1: on Facebook dot com, slash stuff you Should Know. You

997
00:59:17,360 --> 00:59:21,080
Speaker 1: can email us. Can we still do that? Ye? You

998
00:59:21,120 --> 00:59:24,720
Speaker 1: can't text me at uh stuff podcast at how stuff

999
00:59:24,720 --> 00:59:26,840
Speaker 1: works dot com and has always joined us at our

1000
00:59:26,880 --> 00:59:34,280
Speaker 1: home on the web, Stuff you Should Know dot com.

1001
00:59:34,280 --> 00:59:36,720
Speaker 1: For more on this and thousands of other topics, is

1002
00:59:36,760 --> 00:59:45,440
Speaker 1: it how stuff Works dot com