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Speaker 1: Hi, everyone, Happy Saturday. This is Charles W. Chuck Bryant here.

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Speaker 1: I hope you slept well. Hope you're feeling good because

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Speaker 1: you're about to listen to How the Scientific Method Works

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Speaker 1: is from January of two thousand fifteen, and boy, this

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Speaker 1: is a good one. I really loved it because we

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Speaker 1: love science around here and we love the scientific method

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Speaker 1: and proving stuff out. So check it out right now.

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Speaker 1: Welcome to Stuff You Should Know, a production of My

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Speaker 1: Heart Radios How Stuff Works. Hey, and welcome to the podcast.

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Speaker 1: I'm Josh Clark. There's Charles W. Chuck Bryant, there's Jerry

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Speaker 1: Stuffy should Know grinning. It's even a while. I know,

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Speaker 1: it's funny, like those words come pouring out of my

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Speaker 1: mouth and then it's cool. You wake up in the

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

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Speaker 1: you in the face. She's sleep She has to dry

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Speaker 1: my brow. Yes, we prerecorded some for December, as we

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Speaker 1: like to do to take a little time off at

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Speaker 1: the end of the year and not explain things for

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Speaker 1: a few weeks in our real lives. Like people ask

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Speaker 1: me things like what happened to that stick of butter? Yeah,

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Speaker 1: I don't know, don't ask. Don't even ask me. I

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Speaker 1: could tell you, but I'm not gonna exactly. That's how

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Speaker 1: it goes in my house. Find your own butter. December

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Speaker 1: was find your own butter Munthy. That's a good that's

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Speaker 1: that's a good one. That should be a T shirt

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Speaker 1: and stuff. You should know. Find your own butter or

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Speaker 1: December is find your own butter mundy. Yeah, that's right.

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Speaker 1: Maybe a stick of butter and some garland on it. Yeah,

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Speaker 1: I like that. So it's good to see you again, man.

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Speaker 1: Good to be back in here. Yeah, it is nice

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Speaker 1: to be back because as much as the break was great,

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Speaker 1: I'm happy to be explaining things again. Well that's good

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Speaker 1: because if we got in here and you're like, I

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Speaker 1: can't do this, I can't do it again, we'll be

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Speaker 1: in trouble. Yeah. Yeah, So I'm glad we're all feeling good. Jerry,

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Speaker 1: you're feeling good. Jerry's got two thumbs up in a

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Speaker 1: big goofy smile, two of her three thumbs. She looks

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Speaker 1: like Bob from that male enhancement pill. I had. Oh

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Speaker 1: 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. And I

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Speaker 1: see the guy that's super muscle. Now I'm thinking of

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Speaker 1: someone different. I think you're thinking of Jack La 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 year old. No. Remember, there was like a

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Speaker 1: male enhancement pill and I'm making air quotes here for

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Speaker 1: erectile dysfunction. Well, there go the air quotes. But yes,

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Speaker 1: and it was like in the early two thousand's, I

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Speaker 1: 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've

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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 why bother?

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Speaker 1: I 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 won't have to come back in and record an

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Speaker 1: insert the guy that's on the back of all those

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Speaker 1: pill bottles in my bathroom so chucky. I don't even

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

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

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Speaker 1: Enlightenment episode. Okay, we talked a lot about how there's

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Speaker 1: this kind of um tug of war over the human

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Speaker 1: psyche between rationalism and mysticism. I guess you could you

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Speaker 1: could put it well, I feel like we're talking today

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Speaker 1: about the scientific method. Yeah, great idea. By the way,

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Speaker 1: thank you very much. It's been a long time coming. Um,

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Speaker 1: because I realized, like I, I don't understand as fully

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Speaker 1: as I don't understand science. I understand the scientific method

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Speaker 1: because it's pretty cut and dry, and it's beautiful and

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Speaker 1: elegant and simple. But then you just take this thing

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Speaker 1: and it came out of the birth of rationalism, and

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Speaker 1: when you place it into the world and make it

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Speaker 1: function there's a lot of implications. Is it being used properly?

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Speaker 1: Is it being used responsibly? Like are we putting what

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Speaker 1: constitutes faith into that? You know, like it just raises

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Speaker 1: all this other stuff. And it made me realize, like

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Speaker 1: I don't understand science as much as I want to,

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Speaker 1: So researching this it was awesome. Yeah, and this is

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Speaker 1: a cool episode, I think, because not only are we

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

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Speaker 1: to talk about just science, like what is science in general?

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Speaker 1: And some of the rock stars along the way who really,

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Speaker 1: you know, laid out the path remarkably and like many

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Speaker 1: many years ago, like coming up with these amazing discoveries

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Speaker 1: that still like hold you know, you can like hold

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Speaker 1: their feet to the fire for a lot this stuff. Yeah,

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

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

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Speaker 1: the 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, oh, Newton,

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Speaker 1: most of what he said was wrong, But that's understandable

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Speaker 1: because it was a long time ago, like his stuff

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

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

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Speaker 1: so much to humanity it's mind boggling, but I couldn't

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

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Speaker 1: let's let's just quit stalling and talk about science. Like,

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

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

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Speaker 1: 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. Boom end

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Speaker 1: of podcast. Uh So, the first part of that is

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Speaker 1: science is practical and it is you know, they make

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Speaker 1: a good he makes. Bill Harris makes a great point

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Speaker 1: in here. It's not just stuff you do in a lab,

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Speaker 1: and it's not just for scientists. It is all about

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Speaker 1: being hands on and active, and it's all about discovery

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Speaker 1: and asking questions about I mean, that's how everything is

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Speaker 1: ultimately solved, is by someone looking at something and having

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Speaker 1: a question about it exactly. And then the scientific method

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Speaker 1: comes in when you say, and this is how you

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Speaker 1: properly get to that answer exactly. Um. And he makes

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Speaker 1: another good point to that the idea that there's a method,

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Speaker 1: a scientific method, makes it seem like it's it's secreted

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Speaker 1: away among the the fraternity of scientists. And like you said,

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Speaker 1: any anybody can use it. It's just kind of part

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Speaker 1: of being a curious human is it's not even anyone

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Speaker 1: can use it. Everyone does use it, you just might

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Speaker 1: not even know that you're using it, like if you

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Speaker 1: I mean, one of the examples that use later is

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Speaker 1: if like your car overheats, when you figure out why

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

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Speaker 1: exactly based on reasoning. Yeah, okay, in deduction and induction. Man,

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Speaker 1: there's so much to talk about. Okay, So let's let's

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Speaker 1: talk about that definition that you had. So the first

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Speaker 1: part is that science is it's a practical activity. So

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Speaker 1: science is practical, right, It's it's this, Um. The basis

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Speaker 1: of the whole thing is discovery. Right. You see something,

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Speaker 1: you see birds in flight, and you say, where are

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Speaker 1: those birds going? And if you just went and laid

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Speaker 1: down on the ground and went to sleep. After that,

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Speaker 1: then you're not you're not carrying out science. But if

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Speaker 1: you went I want to find out where those birds

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Speaker 1: are going, and you follow them and you start taking notes,

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Speaker 1: that's that is the basis of science, is discovery. Yeah,

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Speaker 1: and that's the observational part as well. Um, sometimes you're

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Speaker 1: using a microscope or a telescope. Sometimes you're using your eyeballs.

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Speaker 1: But no matter what your tool is, uh, you're gonna

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Speaker 1: be watching something and recording what's called data or data,

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Speaker 1: depending on I don't know what kind of person you are. Yeah,

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Speaker 1: what do you say? I think? I say both. I

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

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Speaker 1: I say data data, I say data data. Yeah, all right,

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Speaker 1: we'll go with data. You say both. I feel like

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Speaker 1: it just comes out of my mouth one way or

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

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

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Speaker 1: data 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'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 as behavioral,

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Speaker 1: like I'm gonna watch that bird um eat and 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? You know, I don't do that. No,

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Speaker 1: you really should not do that. No, that'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

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

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Speaker 1: with you, they're both useful. Okay. Uh, it is an

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Speaker 1: intellectual pursuit um. So you can make observations on data

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Speaker 1: all day long. But until you bring reason, in this case,

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

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

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

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

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Speaker 1: and it depending. Again. It's really weird. 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 No, I

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

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

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

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

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

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

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

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

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Speaker 1: data points, and uh, that means that this broad generalization

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Speaker 1: is true. So you go from specific, small observations to

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Speaker 1: a broad generalization. And the reason that a lot of

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Speaker 1: people say, well, inductive reasoning doesn't have any place in

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Speaker 1: science is because you're saying, those birds over there are

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Speaker 1: all brown. Therefore all birds of that type are brown.

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Speaker 1: Even though I haven't seen every single bird of that

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Speaker 1: type in the world. I'm saying that all those birds

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Speaker 1: are brown, and a lot of people say there's no

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Speaker 1: place for that in science. Well, if you want to

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Speaker 1: go out and prove that, then that's your business. You know.

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Speaker 1: You can't just say that and be like and I'm done,

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Speaker 1: right exactly, I guess you could, but be much of scientists, right,

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

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

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Speaker 1: I'm gonna put them together and see if this broad

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

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

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

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Speaker 1: great example for inductive reasoning with Edwin Hubbell 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 it

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Speaker 1: the one from Rebel Without a Cause? Now that's um

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Speaker 1: Griffith Park Observatory, which has been redesigned, and uh is

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Speaker 1: really cool now, Yeah, I mean it was kind of

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Speaker 1: cool before, but it was definitely like, uh, sort of

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Speaker 1: a bass museum that time forgot. Oh really, so they've

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Speaker 1: updated it. I'll bet that was cool though in its

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Speaker 1: own way. Yeah, it was neat. I used to live

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Speaker 1: near there, so it was kind of But that's like

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Speaker 1: the famous one, at least in the movies. Yeah, that's

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Speaker 1: where they have the big knife fight and there's this

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Speaker 1: James Dean statue there too. Oh I didn't like a

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Speaker 1: bust um. So yes, Edwin Hubble, he's at Mountain Wilson

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Speaker 1: and he's looking through the Hooker telescope, which was the

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Speaker 1: biggest one. And at the time everyone said the Milky

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Speaker 1: Way Galaxy is it? That's what we've got going on?

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Speaker 1: Did you know this? Uh? Yeah, I knew that because

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Speaker 1: we're talking. Yeah, not that long ago did not realize this.

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Speaker 1: And he started looking through this telescope and said, you

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Speaker 1: know what, these nebula that everyone says are part of

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Speaker 1: our galaxy look to me like they're beyond our galaxy.

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Speaker 1: Not only that, they look like they're moving away from us.

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Speaker 1: So he made this, uh, with through inductive reasoning, made

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Speaker 1: this observation that you know what, I think there are

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Speaker 1: many many galaxies out there, and not only that, I

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Speaker 1: think they are expanding. And uh, through technological advancement with

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Speaker 1: telescopes over the years, scientists, you know, it proved to

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Speaker 1: be true. Yeah. Pretty cool. So this is a really

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Speaker 1: good example of him saying, like, I've made some observations

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Speaker 1: and now I'm going to say this broad generalization. Right,

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Speaker 1: So these these galaxies appear to be moving away from another.

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Speaker 1: So the whole universe is expanding. Right. That's inductive reasoning.

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Speaker 1: It's a pretty brave thing, uh, especially back then, because

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Speaker 1: you're really putting your reputation at steak. It really is,

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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: the party into party, and and Hubble came up with

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Speaker 1: his own observations, and rather than just experimenting, experimenting, experimenting himself,

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Speaker 1: which I'm sure he continued to do, he created this

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Speaker 1: basis of work that he probably realized is going to

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Speaker 1: survive him. Right. And then later on scientists came down

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Speaker 1: the road and they tested his hypothesis and they found

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Speaker 1: it was correct, and so his hypothesis became a theory.

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Speaker 1: It eventually became part of the basis of the Big

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Speaker 1: Bang theory that the universe started as a huge explosion

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Speaker 1: and it's expanding still because we're because it exploded at

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Speaker 1: one point, right. And they did that by carrying out

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Speaker 1: other tests or experiments exactly. So this is how science works.

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Speaker 1: Like some guy back in in n makes some observations

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Speaker 1: in California, he proposes this big broad generally generalization, and

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Speaker 1: over the next like ensuing half a century more and

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Speaker 1: more scientists all around the world start testing his hypothesis

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Speaker 1: and find it to be true, so it becomes a theory. Yeah. Well,

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Speaker 1: well let's finish up here with science. The last part

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Speaker 1: of the definition is that it's systematic, and it's methodical,

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

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Speaker 1: experiments and tests to be repeated and verified, and it

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Speaker 1: is is. It's a system, it's a way of working

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Speaker 1: things out. It's a way of working and that is

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Speaker 1: the scientific method. Basically. Yeah, you have your idea, you

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Speaker 1: pose a question, you theorize hyper you put a hypothesis

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Speaker 1: out there, and then you go about trying to either

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Speaker 1: prove it or disprove it. Yeah, exactly. And then the

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Speaker 1: way that you go about proving or disproving it, that's

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Speaker 1: the scientific method. Everything else is just scientific inquiry. The

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Speaker 1: way you go about, the standardized way of going about

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Speaker 1: scientific inquiry is the scientific method. And we, friend, we'll

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Speaker 1: talk about the scientific method right after this. All right,

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Speaker 1: you brought up a point. I think we should go

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Speaker 1: ahead and just get right to my friend, let's do

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Speaker 1: hypotheses and theories. One thing to to stay together. One

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

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Speaker 1: hear poop o ers 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 poop poo that Do you remember

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Speaker 1: what website that was? No? No, Also, 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: too Explorables. It's like an online university basically of free

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

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

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

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

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Speaker 1: So 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 and

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

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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 where science goes. The theory is not just something

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Speaker 1: you postulate. So if this may or may not be true,

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Speaker 1: the theory is b on the hypothesis, and it's something

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

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

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

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

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

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

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

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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 bathroom, she's downstairs playing

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Speaker 1: with friends, would be the Jerry bathroom break theory. Right.

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Speaker 1: And then if it turns out that you find that

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Speaker 1: Jerry spending an hour a day pretending to be in

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Speaker 1: the bathroom but actually being downstairs playing words with friends,

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Speaker 1: if the universe couldn't exist without her doing that every day,

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

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Speaker 1: think that was a good example you came up. Well,

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Speaker 1: it's a great example, as it turns out. Uh. I

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

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

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Speaker 1: him in the head and then tell him what we

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Speaker 1: just said about the bathroom breaks, and they'll say, who's Jerry,

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Speaker 1: or just just queue up that whole bit and stand

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

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Speaker 1: a boom box over your head with the smug look

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Speaker 1: on your face. Uh all right, So should we go

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Speaker 1: back in in the old way back machine a little

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

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

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Speaker 1: Where 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 cream 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 free Greece. 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 for you. So you you teach us with

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

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Speaker 1: and necessary was because of something else we've talked about,

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

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

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

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Speaker 1: the Dark Ages until about a century after, there was

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Speaker 1: not 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 it is again, science wasn't

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Speaker 1: really born yet and there was a huge struggle between

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

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

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Speaker 1: that this was mainly in Europe over in the Islamic world,

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

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Speaker 1: there were a lot of advancements being made. Uh, just

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

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

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Speaker 1: under its thumb for a while and it's a pretty

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Speaker 1: big threat and said, you know, you can't do this stuff.

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

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Speaker 1: because here are your answers. Yeah. But eventually the Renaissance

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Speaker 1: came about in the twelfth century and people woke up

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Speaker 1: and I saw some of the work in the Islamic

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Speaker 1: world and said, you know what, maybe let's start reading

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Speaker 1: up on Aristotle and told the me and euclid it

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Speaker 1: once again. Yeah, they're like, we forgot about these guys. Yeah,

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Speaker 1: I mean, it literally kind of vanished for a while

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Speaker 1: it did. From the west. Yes, fortunately it was still around,

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Speaker 1: you know, and in its home places. But yes, in

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Speaker 1: the West they were lost. The Roman stuff was almost

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Speaker 1: entirely lost because it was being suppressed by the locals.

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Speaker 1: And I think the Greek knowledge was completely vanished. Yes, somehow,

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Speaker 1: somehow they got there was some um. We got another

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Speaker 1: listener mail after the Enlightenment when they said that it

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Speaker 1: was an Islamic scholar who was the one who translated

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Speaker 1: Aristotle into Latin or something like that, and that without

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Speaker 1: this guy, like, the West wouldn't have had much to

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Speaker 1: start with. Because that's where that birth of rationalism came

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Speaker 1: from from, was this rediscovery of Greek and Roman classical thought.

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Speaker 1: And this was the basis of scientific inquiry of rationalism,

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Speaker 1: of saying, like, okay, there's there's set rules to things,

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Speaker 1: and we need to discover these rules and how the

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Speaker 1: principles of how the universe works, like there has to

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Speaker 1: be principles, and we need to find this in a rational,

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Speaker 1: methodical way. And right out of the gate, Europe said, okay,

425
00:24:44,680 --> 00:24:47,600
Speaker 1: well whatever you say is right, then, Aristotle. We're used

426
00:24:47,600 --> 00:24:51,720
Speaker 1: to just believing everything without questioning it. And luckily Albert Magnus,

427
00:24:51,720 --> 00:24:55,600
Speaker 1: I think is who it was. Um was Albertus Magnus

428
00:24:55,760 --> 00:24:59,119
Speaker 1: or Roger Bacon, who said, no, it is Bacon. Roger Bacon,

429
00:24:59,160 --> 00:25:03,800
Speaker 1: who just has this great name, Roger Bacon, the Bacon brothers. Yeah,

430
00:25:03,960 --> 00:25:07,120
Speaker 1: he said, Roger. They weren't brothers though, but they were

431
00:25:07,160 --> 00:25:09,200
Speaker 1: they related at all, you know. I looked that up

432
00:25:09,240 --> 00:25:12,600
Speaker 1: and I don't think people know either way. I don't

433
00:25:12,640 --> 00:25:14,720
Speaker 1: think there's any proof, but a lot of people think,

434
00:25:15,200 --> 00:25:17,760
Speaker 1: because of their names and the way things went back then,

435
00:25:17,840 --> 00:25:20,199
Speaker 1: that they may very well have been red And I

436
00:25:20,200 --> 00:25:23,159
Speaker 1: mean they were separated by three d or so years.

437
00:25:23,200 --> 00:25:26,280
Speaker 1: Although Roger was a was a monk, so he would

438
00:25:26,320 --> 00:25:29,080
Speaker 1: not have had children. So if they were, it's an

439
00:25:29,119 --> 00:25:33,240
Speaker 1: excellent point. It wasn't necessarily through his line, you know. Yeah,

440
00:25:33,240 --> 00:25:35,960
Speaker 1: it could have been a nephew or something. Yeah, or

441
00:25:36,000 --> 00:25:39,639
Speaker 1: his brother Kevin might have had the line that matched.

442
00:25:39,960 --> 00:25:43,040
Speaker 1: So Roger was the one who said, everybody stopped. Just

443
00:25:43,080 --> 00:25:47,040
Speaker 1: because Aristotle wrote something doesn't mean it's fact, especially when

444
00:25:47,320 --> 00:25:51,320
Speaker 1: we find contradictions to it. Yeah, it doesn't. Our cells

445
00:25:51,320 --> 00:25:54,520
Speaker 1: not automatically right, and this is a huge advancement. Yeah,

446
00:25:54,560 --> 00:25:57,840
Speaker 1: And Albertus Magnus was the one I believe who said

447
00:25:59,200 --> 00:26:02,919
Speaker 1: you know this and called revealed truth, which is basically

448
00:26:03,160 --> 00:26:07,120
Speaker 1: God says this instead of a truth found by experimenting,

449
00:26:07,760 --> 00:26:11,119
Speaker 1: is maybe we should experiment instead and not take this

450
00:26:11,200 --> 00:26:14,040
Speaker 1: revealed truth as the truth. Right. And we mentioned in

451
00:26:14,119 --> 00:26:18,800
Speaker 1: the Enlightenment episode as well about scholasticism about using scientific

452
00:26:18,920 --> 00:26:24,320
Speaker 1: inquiry to explain theology, which was you know, you're still

453
00:26:24,320 --> 00:26:27,399
Speaker 1: working from a theological standpoint, right, but you're starting to

454
00:26:27,480 --> 00:26:32,320
Speaker 1: use scientific inquiry and the the idea that you shouldn't

455
00:26:32,359 --> 00:26:35,040
Speaker 1: just accept things as truth. That was again a huge,

456
00:26:35,280 --> 00:26:40,240
Speaker 1: huge breakthrough. Uh. Francis Bacon, the other Bacon brother, he's

457
00:26:40,280 --> 00:26:42,159
Speaker 1: one of the heroes of this story. Yeah, he was

458
00:26:42,200 --> 00:26:47,240
Speaker 1: an attorney and philosopher and its possibly Shakespeare. Oh really,

459
00:26:47,560 --> 00:26:52,760
Speaker 1: I never heard that interesting? So what do you mean

460
00:26:52,840 --> 00:26:56,760
Speaker 1: like wrote those under the pseudonym huh? And there the

461
00:26:56,800 --> 00:26:58,960
Speaker 1: Shakespeare sister was the other theory too, right. That was

462
00:26:59,000 --> 00:27:02,000
Speaker 1: a woman. I've heard that, and she couldn't like you know,

463
00:27:02,080 --> 00:27:06,520
Speaker 1: women couldn't be the play, right, so's her dumb brother, William.

464
00:27:06,600 --> 00:27:09,800
Speaker 1: That's good? Was it her brother? I think that was

465
00:27:09,880 --> 00:27:14,760
Speaker 1: one of the theories. This was a good Smith song too. Uh,

466
00:27:14,880 --> 00:27:17,520
Speaker 1: Shakespeare sister was that the name of it. Wouldn't it

467
00:27:17,560 --> 00:27:21,000
Speaker 1: a band too? I think it was? What was it? Maybe?

468
00:27:22,280 --> 00:27:25,960
Speaker 1: Uh So, anyway, he was a philosopher and a lawyer,

469
00:27:26,480 --> 00:27:31,520
Speaker 1: and he said, you know what, the Baconian method basically

470
00:27:31,560 --> 00:27:35,080
Speaker 1: became the scientific method. He was the first dude who

471
00:27:35,119 --> 00:27:37,680
Speaker 1: really said, this is how the steps that you should

472
00:27:37,680 --> 00:27:42,560
Speaker 1: take to uh investigate science. There has to be a framework.

473
00:27:42,560 --> 00:27:45,000
Speaker 1: And the whole point of this that we take this

474
00:27:45,200 --> 00:27:49,879
Speaker 1: so for granted now because it's so intuitively and on

475
00:27:50,000 --> 00:27:54,880
Speaker 1: its face right, yeah, as far as scienceific inquiry goes.

476
00:27:54,920 --> 00:27:58,639
Speaker 1: But this is an enormous breakthrough to say, you follow

477
00:27:58,760 --> 00:28:03,840
Speaker 1: this step, the up this framework, and if everybody who

478
00:28:04,040 --> 00:28:07,640
Speaker 1: carries out science follows the same framework, then science will

479
00:28:07,640 --> 00:28:11,480
Speaker 1: be universal and interchangeable and anyone in the world, and

480
00:28:11,560 --> 00:28:15,600
Speaker 1: not just now, but anytime, we'll be able to carry

481
00:28:15,600 --> 00:28:19,480
Speaker 1: out the same experiment and we'll be able to verify

482
00:28:19,600 --> 00:28:24,840
Speaker 1: or disprove it. Yea, And that is amazing that that happened.

483
00:28:25,040 --> 00:28:27,080
Speaker 1: That's why Francis Bacon is one of the heroes of

484
00:28:27,160 --> 00:28:29,560
Speaker 1: the story. And he didn't come up with this entirely

485
00:28:29,600 --> 00:28:31,480
Speaker 1: on his own, but he was the one who said

486
00:28:32,200 --> 00:28:34,000
Speaker 1: this is what we're gonna do. I'm going to give

487
00:28:34,000 --> 00:28:36,280
Speaker 1: it a name. I'm going to spell it out, and

488
00:28:36,560 --> 00:28:39,959
Speaker 1: from now on you can call me the dad of

489
00:28:40,000 --> 00:28:43,000
Speaker 1: the scientific method. Yeah. And that's why Newton was such

490
00:28:43,040 --> 00:28:46,160
Speaker 1: a rock star, because he so rigorously stuck to the

491
00:28:46,160 --> 00:28:50,760
Speaker 1: scientific method that all these centuries later, his uh, you know,

492
00:28:50,840 --> 00:28:53,520
Speaker 1: his systems of laws are they have stood the test

493
00:28:53,520 --> 00:28:56,040
Speaker 1: of time. And uh, I think it's a good point

494
00:28:56,040 --> 00:29:01,080
Speaker 1: to bring up to that the collaboration of sciences is

495
00:29:01,800 --> 00:29:06,800
Speaker 1: really the hallmark of advancement and moving forward. It's not

496
00:29:06,880 --> 00:29:10,040
Speaker 1: working in a vacuum. It's sharing your ideas and working

497
00:29:10,040 --> 00:29:13,560
Speaker 1: with one another. And the whole h little sidebar here

498
00:29:13,560 --> 00:29:16,400
Speaker 1: on celth here I thought was pretty cool, which was

499
00:29:16,400 --> 00:29:20,480
Speaker 1: when science quit. We're not quit, but started looking at

500
00:29:20,520 --> 00:29:23,320
Speaker 1: small things instead of looking at the universe around them

501
00:29:23,320 --> 00:29:27,760
Speaker 1: and at the stars. And uh said, basically, you know,

502
00:29:27,840 --> 00:29:32,680
Speaker 1: through the advancement of lens grinding, Antonio van Leben Hook,

503
00:29:33,160 --> 00:29:37,720
Speaker 1: specifically a Dutch tradesman, was pretty good at making simple microscopes,

504
00:29:38,120 --> 00:29:40,560
Speaker 1: and all of a sudden, contemporaries like Robert Hook said,

505
00:29:40,560 --> 00:29:43,680
Speaker 1: you know what, let's start looking at tiny things because

506
00:29:43,680 --> 00:29:46,760
Speaker 1: they're in might lie the answer too many many things yeah,

507
00:29:46,800 --> 00:29:51,840
Speaker 1: and they're right. Robert Hook found cork. He discovered cells

508
00:29:51,920 --> 00:29:56,200
Speaker 1: by looking at cork through an early microscope. So in

509
00:29:56,240 --> 00:30:01,080
Speaker 1: this story, science is hastened by technological advance, spent lens

510
00:30:01,120 --> 00:30:05,080
Speaker 1: grinding to make microscopes, and then this new technology is

511
00:30:05,160 --> 00:30:08,280
Speaker 1: used to further science, right, Yeah, it's like mutual inspiration

512
00:30:08,360 --> 00:30:12,680
Speaker 1: between Leavin Hook and Hook. Levin Hook. Yeah, it was

513
00:30:12,760 --> 00:30:18,520
Speaker 1: neat because Hook heard about Leavin Hooks microscopes, got his

514
00:30:18,600 --> 00:30:22,360
Speaker 1: hands on one or a microscope, looked at him like cork,

515
00:30:22,600 --> 00:30:26,440
Speaker 1: and said, oh, there's such thing as cells. Levin Hook said, oh,

516
00:30:26,480 --> 00:30:28,920
Speaker 1: that's pretty neat. Let me try. And he said, oh,

517
00:30:28,960 --> 00:30:32,080
Speaker 1: there's such a thing as quote little animals, which we

518
00:30:32,160 --> 00:30:36,560
Speaker 1: call protozoan bacteria. And one of the Royal Societies. After

519
00:30:36,720 --> 00:30:41,320
Speaker 1: Leavin Hook presented his findings, turned back to Hook and said, hey, Hook,

520
00:30:41,360 --> 00:30:43,920
Speaker 1: we know you're pretty handy with the microscope. You confirmed

521
00:30:44,000 --> 00:30:49,000
Speaker 1: Leavin Hook's findings are their little animals? Hook said, there are, indeed,

522
00:30:49,160 --> 00:30:51,480
Speaker 1: I can see them with my microscope. That's right. And

523
00:30:51,520 --> 00:30:57,000
Speaker 1: that inspired a German botanist name Matthias schleiden Uh to

524
00:30:57,400 --> 00:31:00,320
Speaker 1: look at a lot of plants, and he was the

525
00:31:00,360 --> 00:31:02,440
Speaker 1: first guy to say, you know what, plants are composed

526
00:31:02,480 --> 00:31:05,160
Speaker 1: of cells. And he's having dinner one night with his

527
00:31:05,280 --> 00:31:08,680
Speaker 1: zoologist buddy. Yeah, this is about a hundred years later. Yeah,

528
00:31:08,720 --> 00:31:13,280
Speaker 1: Theodore schran and said, you know what, dude, Uh, order

529
00:31:13,400 --> 00:31:17,080
Speaker 1: the wine and order the steak. Trust me, because this

530
00:31:17,120 --> 00:31:22,160
Speaker 1: place is fantastic. And uh, also, plants are made of cells.

531
00:31:22,360 --> 00:31:24,680
Speaker 1: Don't tell anyone. And he went, you know what, dude,

532
00:31:24,840 --> 00:31:28,440
Speaker 1: I have been investigating animals with microscopes and they're made

533
00:31:28,440 --> 00:31:30,880
Speaker 1: of cells too. And so they figured out at this

534
00:31:30,960 --> 00:31:34,280
Speaker 1: dinner that everything is made of cells. All living things

535
00:31:34,280 --> 00:31:37,160
Speaker 1: are made of cells. Boom, Okay, so this is huge.

536
00:31:37,240 --> 00:31:40,200
Speaker 1: This is a big advancement, right that we're hitting upon

537
00:31:40,360 --> 00:31:44,200
Speaker 1: right now. Huge, but it laid the further foundation, right,

538
00:31:44,360 --> 00:31:48,760
Speaker 1: So initial scientific inquiry led to further scientific inquiry and

539
00:31:48,840 --> 00:31:54,000
Speaker 1: further scientific conclusions and generalizations. All living things are made

540
00:31:54,040 --> 00:31:57,680
Speaker 1: of cells, and then it was extrapolated elsewhere. Right. Yeah,

541
00:31:57,720 --> 00:32:01,840
Speaker 1: Like twenty years later, Rudolph Virtual said, you know what,

542
00:32:01,960 --> 00:32:05,400
Speaker 1: not only is everything made of living cells, but they

543
00:32:05,640 --> 00:32:08,920
Speaker 1: all come from pre existing cells, which was a huge

544
00:32:08,920 --> 00:32:12,560
Speaker 1: deal at the time because people believed in spontaneous generation

545
00:32:13,160 --> 00:32:16,640
Speaker 1: at the time, Like if you left some um wheat

546
00:32:16,640 --> 00:32:19,760
Speaker 1: seed in a sweaty shirt. It would spawn mice. I

547
00:32:19,800 --> 00:32:23,120
Speaker 1: think was one of them. There's a lot of weird ones.

548
00:32:23,240 --> 00:32:26,600
Speaker 1: Press basil between some bricks and you'll get a scorpion.

549
00:32:27,360 --> 00:32:30,280
Speaker 1: Was one, like they were really out there? Yeah, well

550
00:32:30,320 --> 00:32:32,560
Speaker 1: the one that is well not true, but the one

551
00:32:32,600 --> 00:32:36,000
Speaker 1: that you could actually see was rotten meat would eventually

552
00:32:36,480 --> 00:32:39,920
Speaker 1: uh spawn maggots. How did they possibly get there? Yeah,

553
00:32:40,040 --> 00:32:43,560
Speaker 1: spontaneous generation. But that's the obvious explanation, and if you

554
00:32:43,600 --> 00:32:47,280
Speaker 1: think about it, they're working from Okam's razor and Ockham's razor, says,

555
00:32:47,320 --> 00:32:51,920
Speaker 1: the simplest explanation is usually the right one, all all

556
00:32:51,960 --> 00:32:54,920
Speaker 1: other things given. Well, the thing is is spontaneous generation

557
00:32:54,960 --> 00:32:58,440
Speaker 1: has never been shown to be possible. If we've got

558
00:32:58,480 --> 00:33:02,480
Speaker 1: the cell thing over here, let's investigate that. So this

559
00:33:02,680 --> 00:33:06,640
Speaker 1: uh what was the guy's name? Virtual? Yes, he's saying, okay,

560
00:33:06,720 --> 00:33:09,280
Speaker 1: well wait a minute, I get this cell theory I'm

561
00:33:09,280 --> 00:33:11,560
Speaker 1: working on that's been around for a couple of decades.

562
00:33:11,800 --> 00:33:15,600
Speaker 1: Hypothesis probably the cell hypothesis at the nice catch. Don't

563
00:33:15,600 --> 00:33:17,840
Speaker 1: feel bad though, because this article that you sent said

564
00:33:17,880 --> 00:33:22,960
Speaker 1: that scientists today like still like confuse those terms just

565
00:33:24,080 --> 00:33:26,200
Speaker 1: and the the house of works. Article makes a good

566
00:33:26,200 --> 00:33:28,640
Speaker 1: point in saying that science and everything that has to

567
00:33:28,640 --> 00:33:31,000
Speaker 1: do with it is in the scientific method is very

568
00:33:31,080 --> 00:33:36,640
Speaker 1: fluid and open to interpretation and experimentation. Obviously, but so

569
00:33:36,720 --> 00:33:40,160
Speaker 1: he says, um, okay, this cell hypothesis, this is a

570
00:33:40,200 --> 00:33:45,440
Speaker 1: pretty good explanation for what we now call spontaneous generation.

571
00:33:45,960 --> 00:33:47,800
Speaker 1: He didn't do anything about it. He just put it

572
00:33:47,840 --> 00:33:51,680
Speaker 1: out there. Yeah, and then along comes Louis Pastier, who

573
00:33:51,760 --> 00:33:54,120
Speaker 1: does do something about it. He figures out a great

574
00:33:54,160 --> 00:33:59,800
Speaker 1: experiment to try to disprove spontaneous generation. Yeah, it's pretty

575
00:34:00,040 --> 00:34:04,680
Speaker 1: pool to um. He basically took a broth uh, put

576
00:34:04,720 --> 00:34:08,040
Speaker 1: equal amounts in two different beakers. One had a straight

577
00:34:08,080 --> 00:34:11,360
Speaker 1: neck and one had an S shaped neck. He boiled

578
00:34:11,360 --> 00:34:13,240
Speaker 1: it just to make sure everything and it was killed,

579
00:34:13,800 --> 00:34:16,120
Speaker 1: and then just let it sit there in the same conditions,

580
00:34:16,239 --> 00:34:19,719
Speaker 1: open to the to the world and are open to

581
00:34:19,719 --> 00:34:23,560
Speaker 1: the room, like it wasn't corked. In other words, he

582
00:34:23,719 --> 00:34:26,640
Speaker 1: noticed that the one with the straight neck eventually became

583
00:34:26,680 --> 00:34:30,120
Speaker 1: cloudy and discolored h meaning there was some junk growing

584
00:34:30,160 --> 00:34:32,560
Speaker 1: in there, and the one in the S shaped neck

585
00:34:32,719 --> 00:34:36,520
Speaker 1: did not do anything. It remained the same. So led

586
00:34:36,600 --> 00:34:39,840
Speaker 1: him to think what well, he thought that germs, that

587
00:34:39,880 --> 00:34:42,839
Speaker 1: there were such things as germs which m leaving Hoke

588
00:34:42,920 --> 00:34:47,040
Speaker 1: and Hook had already shown um and that if that

589
00:34:47,280 --> 00:34:50,120
Speaker 1: in the S shaped flask they had gotten trapped in

590
00:34:50,160 --> 00:34:54,120
Speaker 1: the neck, in this the open neck, they had been

591
00:34:54,160 --> 00:34:58,440
Speaker 1: able to just enter unobstructed and had generated there. The

592
00:34:58,520 --> 00:35:02,200
Speaker 1: reason that the S shaped ask was still sterile was

593
00:35:02,280 --> 00:35:05,160
Speaker 1: because there is no such thing as spontaneous generation. If

594
00:35:05,160 --> 00:35:08,920
Speaker 1: there were, then no S shaped neck would impede anything

595
00:35:08,960 --> 00:35:12,040
Speaker 1: like that, and boom, there you have it. So he

596
00:35:12,280 --> 00:35:17,759
Speaker 1: disproved that spontaneous generation is a thing, right, that's right

597
00:35:17,920 --> 00:35:21,920
Speaker 1: through the scientific method. Exactly. Here's the leap that a

598
00:35:21,920 --> 00:35:25,160
Speaker 1: lot of people make, scientists included that really is a

599
00:35:25,200 --> 00:35:30,160
Speaker 1: great disservice to science. He didn't prove cell theory right.

600
00:35:30,400 --> 00:35:34,200
Speaker 1: What he did was take that cell hypothesis and present

601
00:35:34,360 --> 00:35:41,440
Speaker 1: some really persuasive evidence that it's probably right. Yeah, But

602
00:35:41,560 --> 00:35:44,239
Speaker 1: like this article you sent points out, disproving something is

603
00:35:44,320 --> 00:35:46,799
Speaker 1: just as important as proving something. So here's the thing

604
00:35:47,200 --> 00:35:49,040
Speaker 1: that's the most you can hope for a science is

605
00:35:49,120 --> 00:35:53,320
Speaker 1: disproving sure with science, unless you're talking about math. With science,

606
00:35:53,440 --> 00:35:56,959
Speaker 1: there's no such thing as proof a theory, even a law,

607
00:35:57,239 --> 00:36:01,200
Speaker 1: universal law still has the potential for being undermined by

608
00:36:01,280 --> 00:36:05,200
Speaker 1: one single experiment, one single observation, and therefore there is

609
00:36:05,400 --> 00:36:10,640
Speaker 1: no real ultimate proof in science. There's just theories and

610
00:36:10,800 --> 00:36:15,239
Speaker 1: support for theories, and then ultimately laws aim further and

611
00:36:15,280 --> 00:36:20,080
Speaker 1: further support for laws, right, but they're not proven. What

612
00:36:20,239 --> 00:36:25,480
Speaker 1: science does ultimately is disproved things or lend support for

613
00:36:25,520 --> 00:36:29,680
Speaker 1: existing theories, are existing interpretations of why things happen the

614
00:36:29,719 --> 00:36:32,719
Speaker 1: way they do. And that's what past true did. So

615
00:36:32,760 --> 00:36:36,600
Speaker 1: if you look at the experiment, he disproved spontaneous generation,

616
00:36:36,960 --> 00:36:39,880
Speaker 1: but he lent support to the cell theory, and probably

617
00:36:39,920 --> 00:36:42,719
Speaker 1: with his experiment it went from the cell hypothesis to

618
00:36:42,840 --> 00:36:46,759
Speaker 1: the cell theory because it was just so persuasive. And

619
00:36:46,800 --> 00:36:48,719
Speaker 1: that's what a theory is. It means that a lot

620
00:36:48,760 --> 00:36:53,239
Speaker 1: of people out there who are reasonable say this explanation

621
00:36:53,320 --> 00:36:55,759
Speaker 1: is probably the right one. Yeah, it's predictive. If you

622
00:36:55,800 --> 00:36:57,480
Speaker 1: do it over and over, you're probably going to get

623
00:36:57,480 --> 00:37:01,280
Speaker 1: the same result. But that's not to say that past

624
00:37:01,320 --> 00:37:03,799
Speaker 1: year showed that if you do this a million in

625
00:37:03,920 --> 00:37:08,920
Speaker 1: one times that the S shaped flask won't turn cloudy.

626
00:37:09,239 --> 00:37:11,840
Speaker 1: He didn't prove that. You can't prove that, which is

627
00:37:11,920 --> 00:37:16,759
Speaker 1: again science can disprove and when support can't prove. Very

628
00:37:16,800 --> 00:37:19,719
Speaker 1: good point. So right after this message break, we're going

629
00:37:19,760 --> 00:37:41,960
Speaker 1: to get into the actual steps of the scientific method. Alright, dude,

630
00:37:42,080 --> 00:37:46,000
Speaker 1: I guess that long last, we're there. Like you mentioned before,

631
00:37:46,560 --> 00:37:50,000
Speaker 1: the scientific method is fluid and it's not like when

632
00:37:50,040 --> 00:37:52,959
Speaker 1: you get your science degree they hand you a little

633
00:37:53,000 --> 00:37:57,799
Speaker 1: laminated card like the Miranda rights that cop scary that

634
00:37:58,000 --> 00:37:59,920
Speaker 1: you know, list out all the different steps you have

635
00:37:59,920 --> 00:38:04,719
Speaker 1: to take. Um. But generally maybe yeah, I would we

636
00:38:04,760 --> 00:38:06,759
Speaker 1: should carry those around, all right. We should make little

637
00:38:06,800 --> 00:38:10,040
Speaker 1: wallet cards of the scientific method just to carry the stuff.

638
00:38:10,080 --> 00:38:11,719
Speaker 1: You should know a logo on it. Oh yeah, I'll

639
00:38:11,760 --> 00:38:15,839
Speaker 1: make a million bucks a random and sell them. Uh.

640
00:38:16,160 --> 00:38:19,560
Speaker 1: Generally speaking, though it follows these steps. The first thing

641
00:38:19,560 --> 00:38:21,920
Speaker 1: you do, like we mentioned earlier, is you observe something.

642
00:38:22,440 --> 00:38:26,480
Speaker 1: You ask a question. Uh next, Like Darwin was known,

643
00:38:26,920 --> 00:38:29,080
Speaker 1: I think when we did our podcast on him too,

644
00:38:29,280 --> 00:38:31,960
Speaker 1: you would spend like a week on three square feet

645
00:38:32,480 --> 00:38:36,040
Speaker 1: of ground. It was like even longer than that. Remember

646
00:38:36,120 --> 00:38:38,080
Speaker 1: Remember was wasn't it He said that he didn't he

647
00:38:38,080 --> 00:38:40,440
Speaker 1: wasn't gonna MOA's lawn for like three years because he

648
00:38:40,480 --> 00:38:43,719
Speaker 1: wanted to see what what happened? Yeah, so he's the

649
00:38:43,800 --> 00:38:47,719
Speaker 1: ultimate and qualitative data of just observing, writing things down

650
00:38:47,840 --> 00:38:51,040
Speaker 1: and asking questions. And the reason you ask your question

651
00:38:51,920 --> 00:38:55,239
Speaker 1: is so you can narrow something down like that. I

652
00:38:55,239 --> 00:38:58,120
Speaker 1: think the example they use in here is on galapa

653
00:38:58,160 --> 00:39:01,759
Speaker 1: ghosts like the beaks of what bird? Was it? Finches? Yeah,

654
00:39:01,800 --> 00:39:03,720
Speaker 1: the finch bird. He noticed a bunch of different beaks,

655
00:39:04,239 --> 00:39:07,120
Speaker 1: so he finally posed a question like, um, you know,

656
00:39:07,160 --> 00:39:10,040
Speaker 1: I think these beaks are different for a very specific reason,

657
00:39:10,480 --> 00:39:13,160
Speaker 1: and I aim to find out why. Yes, he said,

658
00:39:13,160 --> 00:39:18,920
Speaker 1: what caused the diversification of finches on Galapagos? Who should

659
00:39:18,920 --> 00:39:21,640
Speaker 1: have done that with an accent? Well, yeah, he would

660
00:39:21,640 --> 00:39:24,160
Speaker 1: have had a British accent. Huh yeah, unless he was

661
00:39:24,280 --> 00:39:27,319
Speaker 1: pretending to be someone else. I think of him as like, um,

662
00:39:27,480 --> 00:39:31,240
Speaker 1: sounding like Hemingway or something. Oh yeah, just drunken, violent

663
00:39:31,640 --> 00:39:34,360
Speaker 1: kind of. But he wasn't. He was like the opposite

664
00:39:34,360 --> 00:39:37,120
Speaker 1: of that. Yeah. Well I saw that the movie. So

665
00:39:37,360 --> 00:39:41,760
Speaker 1: a picture of his voice as the dude that played

666
00:39:41,840 --> 00:39:44,840
Speaker 1: him who I can't remember right now, ed Norton. No,

667
00:39:47,239 --> 00:39:50,359
Speaker 1: I finally saw Birdman though. Did you see that? Yeah? Yeah,

668
00:39:50,920 --> 00:39:54,319
Speaker 1: great movie? Um, I disagree, Oh you didn't like it?

669
00:39:54,840 --> 00:40:01,040
Speaker 1: What Wow, that surprises me. Um, we'll get into that

670
00:40:01,239 --> 00:40:06,239
Speaker 1: off here. So, uh sorry you just threw me with that.

671
00:40:06,560 --> 00:40:09,680
Speaker 1: Make an observation. Yes, he's on galapagost and he's like,

672
00:40:09,760 --> 00:40:12,400
Speaker 1: what the hex with all these different finches one small island?

673
00:40:12,640 --> 00:40:15,640
Speaker 1: Why would there be different species of finch so asking?

674
00:40:15,719 --> 00:40:19,319
Speaker 1: And and why are they all seeming to survive and

675
00:40:19,360 --> 00:40:23,560
Speaker 1: coexist so well? What's what makes Yeah, then he leads

676
00:40:23,560 --> 00:40:26,799
Speaker 1: to the question what's making all of these species of

677
00:40:26,840 --> 00:40:30,440
Speaker 1: finches so diverse? Right? Or Bill Harris uses a pretty

678
00:40:30,440 --> 00:40:34,359
Speaker 1: good example that's something everyone can understand, like what car

679
00:40:34,400 --> 00:40:38,480
Speaker 1: body shape is the best for air resistance? Like one

680
00:40:38,520 --> 00:40:40,360
Speaker 1: the shape like a box, or one the shape like

681
00:40:40,400 --> 00:40:43,279
Speaker 1: aerodynamic like a bird. And he carries that out In

682
00:40:43,560 --> 00:40:48,759
Speaker 1: the next step. You formulate your hypothesis based on your

683
00:40:49,239 --> 00:40:53,279
Speaker 1: you know, for knowledge and maybe observations like so, you

684
00:40:53,280 --> 00:40:55,560
Speaker 1: know what, I think that a car shaped like a

685
00:40:55,600 --> 00:40:58,520
Speaker 1: bird is probably more aerodynamic than one shape like a box. Yeah.

686
00:40:58,520 --> 00:41:00,160
Speaker 1: If you're thinking, if you're the type of per soon

687
00:41:00,160 --> 00:41:03,840
Speaker 1: he's sitting around asking questions about aerodynamics, you probably already

688
00:41:03,880 --> 00:41:06,680
Speaker 1: have some sort of sense that a box is less

689
00:41:06,719 --> 00:41:10,720
Speaker 1: aerodynamic than a bird. That's right. Boxes rarely fly unless

690
00:41:10,719 --> 00:41:13,600
Speaker 1: they're carried by one of those delightful Amazon delivery drones.

691
00:41:16,120 --> 00:41:17,759
Speaker 1: They don't have those yet, right, They're not gonna do that,

692
00:41:17,800 --> 00:41:23,200
Speaker 1: are they. There's like a pizza delivery drone service. Man.

693
00:41:23,360 --> 00:41:27,000
Speaker 1: I think where you have pizza are grilled cheese in

694
00:41:27,080 --> 00:41:29,399
Speaker 1: New York and you go stand on an X after

695
00:41:29,440 --> 00:41:31,640
Speaker 1: you order and it like comes and drops it. That

696
00:41:31,760 --> 00:41:34,439
Speaker 1: is the dumbest thing of error. And I can't wait

697
00:41:34,480 --> 00:41:37,160
Speaker 1: to do it where they're making a lot of money.

698
00:41:37,600 --> 00:41:40,239
Speaker 1: That's pretty funny. Um, Yet we can't get food to

699
00:41:40,239 --> 00:41:42,839
Speaker 1: the homeless somehow exactly. We can drop a grilled cheese

700
00:41:42,880 --> 00:41:45,359
Speaker 1: on someone's head. They're like, you homeless, gut, get off

701
00:41:45,400 --> 00:41:50,400
Speaker 1: of that x exactly. Um. Alright, So your hypothesis I

702
00:41:50,440 --> 00:41:53,640
Speaker 1: don't think we ever mentioned is typically represented as an

703
00:41:53,680 --> 00:41:57,120
Speaker 1: if then statement, Yeah, if you're doing good sciences, Yeah,

704
00:41:57,160 --> 00:42:04,120
Speaker 1: like if the cars profile. Uh, well, the the example

705
00:42:04,160 --> 00:42:06,440
Speaker 1: he uses if the body's profile related to the amount

706
00:42:06,440 --> 00:42:10,239
Speaker 1: of air it produces, which is the more general statement. Yeah,

707
00:42:10,280 --> 00:42:12,520
Speaker 1: that's like based on a theory. Yeah, and it's gonna

708
00:42:12,520 --> 00:42:14,919
Speaker 1: get more specific than the car designed, like the body

709
00:42:14,920 --> 00:42:18,160
Speaker 1: of a bird will be more aerodynamic than one like

710
00:42:18,200 --> 00:42:21,320
Speaker 1: a box. So that's inductive reasoning, starting with the broad

711
00:42:21,480 --> 00:42:24,600
Speaker 1: statement and going to something narrow, and it's if. Then

712
00:42:24,800 --> 00:42:28,280
Speaker 1: at the same time, Yeah, and now you have a test.

713
00:42:28,320 --> 00:42:30,239
Speaker 1: You have a question that can be answered, you can

714
00:42:30,280 --> 00:42:32,200
Speaker 1: figure out a way to answer it. Yeah, and he

715
00:42:32,239 --> 00:42:36,640
Speaker 1: points out to this is pretty important that, uh, your hypothesis,

716
00:42:36,640 --> 00:42:40,560
Speaker 1: if it's formulated correctly, means it is testable and it's falsifiable,

717
00:42:40,960 --> 00:42:44,800
Speaker 1: which are often one and the same, you know. Yeah,

718
00:42:44,840 --> 00:42:47,600
Speaker 1: And that's again we go to the people who say

719
00:42:47,640 --> 00:42:53,040
Speaker 1: that they're they're soft sciences, aren't real science. There's pseudoscience

720
00:42:53,080 --> 00:42:56,080
Speaker 1: because a lot of the data that they come up with,

721
00:42:56,120 --> 00:42:59,520
Speaker 1: a lot of the hypotheses they come up with aren't falsifiable,

722
00:42:59,520 --> 00:43:03,480
Speaker 1: they're not testable. It's a it's a thing. It's an issue,

723
00:43:03,880 --> 00:43:07,520
Speaker 1: it's a thing. So next up in the steps, you're

724
00:43:07,520 --> 00:43:11,640
Speaker 1: gonna experiment. And when you experiment, you can't just go

725
00:43:11,680 --> 00:43:14,080
Speaker 1: in there willy nilly and do whatever you want. Um,

726
00:43:14,120 --> 00:43:17,120
Speaker 1: you have to set up specific conditions and they must

727
00:43:17,120 --> 00:43:20,840
Speaker 1: be controlled, and you want to everything that's supposed to

728
00:43:20,880 --> 00:43:24,120
Speaker 1: be identical needs to be identical. So basically you have

729
00:43:24,320 --> 00:43:28,040
Speaker 1: two variables. At least you have an independent variable and

730
00:43:28,080 --> 00:43:31,120
Speaker 1: you have a dependent variable, and if you're talking about

731
00:43:31,400 --> 00:43:35,799
Speaker 1: car shape, that is the independent variable in this study,

732
00:43:35,960 --> 00:43:39,920
Speaker 1: that's the one that's manipulated exactly, it's the one you're controlling.

733
00:43:39,920 --> 00:43:43,359
Speaker 1: The independent variable is the one you, the researcher, is controlling.

734
00:43:43,680 --> 00:43:46,800
Speaker 1: So in this case, you're controlling the shape of the car.

735
00:43:47,120 --> 00:43:50,120
Speaker 1: You have yourself a bird shaped car and you have

736
00:43:50,200 --> 00:43:52,560
Speaker 1: yourself a box shaped car. So the shape of the

737
00:43:52,560 --> 00:43:56,160
Speaker 1: car changed because you made it change. Now when you

738
00:43:56,200 --> 00:43:58,719
Speaker 1: blast a bunch of air over it during your experiment,

739
00:43:59,200 --> 00:44:02,560
Speaker 1: what your measure ring is the dependent variable. So you're

740
00:44:02,600 --> 00:44:06,959
Speaker 1: measuring what happens based on the change that you made.

741
00:44:07,200 --> 00:44:09,480
Speaker 1: That's right, and you want to you you want to

742
00:44:09,480 --> 00:44:12,520
Speaker 1: study one single variable at a time, basically, Yeah, don't

743
00:44:12,520 --> 00:44:16,400
Speaker 1: get fancy, just just do good science, step by step, methodical.

744
00:44:16,920 --> 00:44:20,359
Speaker 1: You also have to have your control group in any experiment. Uh,

745
00:44:20,360 --> 00:44:24,840
Speaker 1: And an experimental group and then controlled group is what's

746
00:44:24,840 --> 00:44:28,080
Speaker 1: gonna allow you to compare the test results to that

747
00:44:28,160 --> 00:44:31,880
Speaker 1: baseline measurement. Yeah, and you need that baseline measurement, so

748
00:44:32,600 --> 00:44:36,160
Speaker 1: it's not just like chance. Basically exactly like if Pasture

749
00:44:36,200 --> 00:44:38,760
Speaker 1: had just done the S shaped neck and nothing happen.

750
00:44:39,520 --> 00:44:42,799
Speaker 1: He wouldn't have necessarily been able to say that he

751
00:44:42,920 --> 00:44:46,320
Speaker 1: was right even though he was right. He needed that control,

752
00:44:46,400 --> 00:44:49,440
Speaker 1: which was the open flask. Right. Or with the cars,

753
00:44:49,480 --> 00:44:52,360
Speaker 1: you need two cars, like you said, one bird shaped

754
00:44:52,360 --> 00:44:55,160
Speaker 1: and one box shaped, right, Or then maybe in this case,

755
00:44:55,200 --> 00:44:57,200
Speaker 1: since the bird shape and the box shape both show

756
00:44:57,280 --> 00:44:59,879
Speaker 1: up in the hypothesis, you need a third like egg

757
00:45:00,040 --> 00:45:02,640
Speaker 1: shaped one or something like that. I bet that would

758
00:45:02,640 --> 00:45:07,359
Speaker 1: be pretty streamline. Yeah, yeah, yeah. But the key though,

759
00:45:07,600 --> 00:45:10,000
Speaker 1: is all of those variables have to be um. All

760
00:45:10,040 --> 00:45:12,520
Speaker 1: the other variables have to be the same, like you

761
00:45:12,520 --> 00:45:14,600
Speaker 1: have to have them. They have to be the same weight,

762
00:45:14,719 --> 00:45:18,120
Speaker 1: they have to be painted the same, the tires, everything,

763
00:45:18,160 --> 00:45:21,120
Speaker 1: the windows. One can't have an antenna on the other not,

764
00:45:21,560 --> 00:45:23,640
Speaker 1: They've all they've got to be identical other than the

765
00:45:23,640 --> 00:45:26,960
Speaker 1: one variable, right, the independent variable that you're that's the

766
00:45:27,000 --> 00:45:29,480
Speaker 1: one you want different. Everything else you want the same

767
00:45:29,600 --> 00:45:31,960
Speaker 1: or else it's possible that, oh, well this one had

768
00:45:32,560 --> 00:45:36,120
Speaker 1: bigger tires, so that actually made it more aerodynamic. Yeah,

769
00:45:36,160 --> 00:45:38,239
Speaker 1: and you're just doing yourself a favor by doing all

770
00:45:38,239 --> 00:45:40,760
Speaker 1: that stuff. You know, you want to rule out everything

771
00:45:40,760 --> 00:45:44,839
Speaker 1: else but that one variable. After that, you want to

772
00:45:45,040 --> 00:45:50,120
Speaker 1: analyze your data, so you can draw your conclusion, and

773
00:45:50,200 --> 00:45:54,360
Speaker 1: sometimes it's kind of straightforward and easy. Sometimes takes a

774
00:45:54,400 --> 00:45:57,879
Speaker 1: lot of work and a lot of various tools draw

775
00:45:57,840 --> 00:46:01,359
Speaker 1: all that out. Let's say you're just asking a car

776
00:46:01,360 --> 00:46:05,040
Speaker 1: and a wind tunnel. You're measuring the wind resistance using

777
00:46:05,120 --> 00:46:08,280
Speaker 1: certain awesome instruments and that kind of stuff, and you're

778
00:46:08,320 --> 00:46:11,200
Speaker 1: taking that data, and then afterward you're going to analyze.

779
00:46:11,239 --> 00:46:13,600
Speaker 1: You're going to compare the data that you gathered from

780
00:46:14,040 --> 00:46:17,399
Speaker 1: the bird shaped car the box shaped car, and then

781
00:46:17,440 --> 00:46:20,120
Speaker 1: the control the egg shaped car. You're gonna compare them,

782
00:46:20,160 --> 00:46:23,120
Speaker 1: and you're gonna say, well, the wind resistance was less

783
00:46:23,640 --> 00:46:27,040
Speaker 1: for the bird shaped car then the box shaped car,

784
00:46:27,120 --> 00:46:31,880
Speaker 1: which means that my hypothesis was correct. And here all

785
00:46:31,880 --> 00:46:34,399
Speaker 1: the data points. Whereas Louis paste or could just say,

786
00:46:34,800 --> 00:46:39,120
Speaker 1: look at the Beaker's exactly, don't be an idiot, I'm

787
00:46:39,160 --> 00:46:43,839
Speaker 1: a scientist. That one's got gross stuff. You can see it, right.

788
00:46:44,000 --> 00:46:47,719
Speaker 1: But the other thing about science to chuck ideally is

789
00:46:47,840 --> 00:46:51,280
Speaker 1: let's say that egg shaped one turned out the control

790
00:46:51,320 --> 00:46:54,839
Speaker 1: group turned out to have better wind resistance than anything. Well,

791
00:46:54,920 --> 00:46:57,920
Speaker 1: just by virtue of carrying out this experiment correctly, you

792
00:46:57,920 --> 00:47:02,080
Speaker 1: would have stumbled upon and even better aerodynamic design, and

793
00:47:02,120 --> 00:47:04,760
Speaker 1: you would have come up with that little egg shaped

794
00:47:04,800 --> 00:47:08,160
Speaker 1: Mercedes suv that was so huge, like ten years ago,

795
00:47:08,480 --> 00:47:13,240
Speaker 1: the Mercedes egg coming to a store near you. So, um,

796
00:47:13,280 --> 00:47:16,000
Speaker 1: that's a big, big part of the scientific method is

797
00:47:16,880 --> 00:47:24,200
Speaker 1: carrying out a a an experiment, controlling the variables, analyzing

798
00:47:24,200 --> 00:47:27,280
Speaker 1: the data. And then there's a step that he missed

799
00:47:27,320 --> 00:47:31,280
Speaker 1: that is very rarely part of a scientific method list

800
00:47:32,000 --> 00:47:35,399
Speaker 1: that is to share your data. And this is a

801
00:47:35,480 --> 00:47:38,600
Speaker 1: huge problem with science right now. Yeah, the article you said,

802
00:47:38,600 --> 00:47:41,600
Speaker 1: it was really eye opening. Uh, scientific research has changed

803
00:47:41,640 --> 00:47:43,799
Speaker 1: the world. Now it needs to change itself. It's an

804
00:47:43,800 --> 00:47:46,480
Speaker 1: economist article. It's up on the internet. Yeah, it was

805
00:47:46,560 --> 00:47:50,799
Speaker 1: kind of scary that it's I mean, here's some of

806
00:47:50,840 --> 00:47:53,560
Speaker 1: the data he points out is one rule of thumb

807
00:47:53,640 --> 00:47:59,080
Speaker 1: among biotech venture capitalists is about half of published research

808
00:47:59,440 --> 00:48:02,839
Speaker 1: can't even be replicated. And on the biotech firm am

809
00:48:02,920 --> 00:48:05,879
Speaker 1: Jin found that they could reproduce only six of their

810
00:48:05,880 --> 00:48:09,880
Speaker 1: fifty three landmark studies in cancer research, So you can't

811
00:48:09,880 --> 00:48:14,240
Speaker 1: repeat these things. It's like everyone's fighting for dollars in fame,

812
00:48:14,880 --> 00:48:17,920
Speaker 1: and maybe not fame, but to some our career advancements

813
00:48:18,680 --> 00:48:21,359
Speaker 1: such that they're kind of not doing that final step

814
00:48:21,400 --> 00:48:25,239
Speaker 1: any longer. No, and it's not necessarily just them, it's

815
00:48:25,360 --> 00:48:28,600
Speaker 1: the other scientists aren't going back and saying, well, let

816
00:48:28,600 --> 00:48:32,600
Speaker 1: me see if your results are reproducible. People are just

817
00:48:32,680 --> 00:48:35,960
Speaker 1: taking it on faith. We need another Roger Bacon to

818
00:48:36,000 --> 00:48:38,440
Speaker 1: come along and be like, dude, we can't just blindly

819
00:48:38,480 --> 00:48:41,839
Speaker 1: accept that one person carried out this one study and

820
00:48:41,880 --> 00:48:44,920
Speaker 1: then just go do clinical trials on it without anybody

821
00:48:44,960 --> 00:48:49,919
Speaker 1: reproducing it to see if the results can be verified independently. Yeah.

822
00:48:49,960 --> 00:48:52,400
Speaker 1: Because uh, and this is a good time to mention bias.

823
00:48:52,480 --> 00:48:56,480
Speaker 1: There is such a thing as bias, and it still happens. Um.

824
00:48:56,640 --> 00:49:02,360
Speaker 1: A scientist is usually to prove something or disprove something

825
00:49:03,000 --> 00:49:06,839
Speaker 1: that they want a specific result. Like, even if you're

826
00:49:06,880 --> 00:49:10,919
Speaker 1: super open minded, you're probably hoping to disprove or prove

827
00:49:11,000 --> 00:49:14,880
Speaker 1: something one way or the other, and your confirmation bias

828
00:49:15,000 --> 00:49:17,600
Speaker 1: might you know, even if you don't think you're doing it,

829
00:49:17,640 --> 00:49:20,960
Speaker 1: you might nudge out some results that don't support your

830
00:49:21,120 --> 00:49:27,360
Speaker 1: hypothesis and so you won't make it into that awesome journal. Um,

831
00:49:27,440 --> 00:49:30,240
Speaker 1: which this author points out that journals need to start

832
00:49:31,080 --> 00:49:36,839
Speaker 1: uh putting in what he calls uninteresting results and experiments, right,

833
00:49:36,960 --> 00:49:39,960
Speaker 1: or like the stuff that's not sexy, right, or studies

834
00:49:40,000 --> 00:49:43,920
Speaker 1: that failed to show that their hypothesis was correct. Yeah,

835
00:49:43,960 --> 00:49:46,960
Speaker 1: stuff is disproved. Those things still need to well not

836
00:49:47,080 --> 00:49:49,719
Speaker 1: even disproved. Well, yeah, I guess it is disproved. But yes,

837
00:49:49,760 --> 00:49:52,520
Speaker 1: like the guy set out to say, like the red

838
00:49:53,160 --> 00:49:57,680
Speaker 1: balloon uses less helium than a silver balloon, and it

839
00:49:57,719 --> 00:49:59,759
Speaker 1: turns out that no, they use the same amount if

840
00:49:59,800 --> 00:50:03,239
Speaker 1: he lium. Well, if that study gets published and put

841
00:50:03,239 --> 00:50:07,000
Speaker 1: out there into the scientific literature on helium and balloons,

842
00:50:07,480 --> 00:50:09,719
Speaker 1: then it's going to prevent some other scientists down the

843
00:50:09,800 --> 00:50:13,440
Speaker 1: road from wasting time, money, and helium, which, as you remember,

844
00:50:13,600 --> 00:50:18,279
Speaker 1: is an increasingly needed commodity. Um By carrying out the

845
00:50:18,320 --> 00:50:23,160
Speaker 1: same experiment, whether whether the results are positive or negative

846
00:50:23,200 --> 00:50:25,920
Speaker 1: or what, the study is meant to be shared. And

847
00:50:25,960 --> 00:50:28,120
Speaker 1: that's the point of the scientific method, is to to

848
00:50:28,280 --> 00:50:30,640
Speaker 1: reduce bias. And if you follow it all the way

849
00:50:30,680 --> 00:50:33,600
Speaker 1: through ideally and do all of the steps, including share

850
00:50:33,640 --> 00:50:39,120
Speaker 1: your research, whether it's happy or sad, then science benefits.

851
00:50:39,120 --> 00:50:41,880
Speaker 1: The world benefits, and by not doing that, the world

852
00:50:42,080 --> 00:50:45,400
Speaker 1: does not benefit. Yeah. He points out that these days

853
00:50:45,520 --> 00:50:50,880
Speaker 1: only four of published papers are quote unquote negative results

854
00:50:51,400 --> 00:50:55,200
Speaker 1: and it used to be like or more um And

855
00:50:55,280 --> 00:50:56,799
Speaker 1: he says, because it's a lot of it has to

856
00:50:56,840 --> 00:51:01,600
Speaker 1: do with this sort of you know, getting in these

857
00:51:01,640 --> 00:51:04,080
Speaker 1: journals and you're the rock star scientists and this study

858
00:51:04,160 --> 00:51:06,680
Speaker 1: is super sexy. Like if they kind of quit going

859
00:51:06,719 --> 00:51:08,879
Speaker 1: that route and made it what it should be, then

860
00:51:09,680 --> 00:51:13,680
Speaker 1: research dollars would be better spent and people could you know,

861
00:51:14,080 --> 00:51:16,040
Speaker 1: he said, the peer reviewed thing isn't even all it's

862
00:51:16,040 --> 00:51:18,560
Speaker 1: cracked up to be. Know that. He mentioned a study

863
00:51:18,760 --> 00:51:21,480
Speaker 1: from a medical journal that gave a bunch of peer

864
00:51:21,480 --> 00:51:26,840
Speaker 1: reviewers some stuff with deliberate errors inserted into the research,

865
00:51:26,880 --> 00:51:29,560
Speaker 1: into the studies, and even when they were told that

866
00:51:29,600 --> 00:51:32,200
Speaker 1: they were being tested to find this, they still missed

867
00:51:32,200 --> 00:51:35,319
Speaker 1: a lot of it. Yeah. So yeah, the science used

868
00:51:35,360 --> 00:51:37,680
Speaker 1: to kind of re evaluate the way it's carrying out science.

869
00:51:37,719 --> 00:51:42,400
Speaker 1: It's not science. The problem isn't science itself. The problem

870
00:51:42,440 --> 00:51:44,640
Speaker 1: isn't the scientific method. It's the way that it's being

871
00:51:44,719 --> 00:51:46,759
Speaker 1: used or not followed through. And a lot of it

872
00:51:46,800 --> 00:51:49,759
Speaker 1: has to do with academia and the people funding science. Yeah,

873
00:51:49,800 --> 00:51:51,960
Speaker 1: and he said, you know, these days there's up seven

874
00:51:52,000 --> 00:51:55,400
Speaker 1: million researchers, and back in the day, even in like

875
00:51:55,480 --> 00:51:58,799
Speaker 1: the nineteen fifties there or like a few thousand maybe, right,

876
00:51:58,960 --> 00:52:01,759
Speaker 1: So there's just a lot of career competition. He calls

877
00:52:01,760 --> 00:52:05,520
Speaker 1: it careerism. And so you fake a result or two,

878
00:52:05,640 --> 00:52:08,360
Speaker 1: or you just nudge out some results that don't support

879
00:52:08,360 --> 00:52:12,279
Speaker 1: your hypothesis. You want the bigger paycheck or the fame

880
00:52:12,400 --> 00:52:16,800
Speaker 1: or notoriety, and all of a sudden, science is not science. Yeah,

881
00:52:16,960 --> 00:52:23,080
Speaker 1: you know, it's pseudoscience exactly. And speaking of pseudoscience, I

882
00:52:23,120 --> 00:52:25,520
Speaker 1: think we've reached the point where, um, we should talk

883
00:52:25,520 --> 00:52:28,640
Speaker 1: about the limitations of the scientific method, because it does

884
00:52:28,719 --> 00:52:32,160
Speaker 1: have its limits, right, like the way that the scientific

885
00:52:32,200 --> 00:52:34,879
Speaker 1: method is set up, especially if you go through um,

886
00:52:34,960 --> 00:52:39,360
Speaker 1: if you include falsification, which most scientists now say is

887
00:52:39,400 --> 00:52:43,400
Speaker 1: a thing like falsifiability of your hypothesis means that you

888
00:52:43,440 --> 00:52:47,239
Speaker 1: have a real scientific hypothesis there if it can be

889
00:52:47,320 --> 00:52:51,080
Speaker 1: disproven by some observation or some measurement or whatever, then

890
00:52:51,120 --> 00:52:54,920
Speaker 1: it's falsifiable. And if it's not falsifiable, then it's not

891
00:52:55,000 --> 00:52:59,680
Speaker 1: really science. So the thing is force something to be falsifiable,

892
00:52:59,680 --> 00:53:01,680
Speaker 1: and it is actually a philosopher that came up with

893
00:53:01,719 --> 00:53:04,520
Speaker 1: the concept of falsification, a guy named Carl Popper in

894
00:53:04,520 --> 00:53:07,799
Speaker 1: the nineteen thirties, and he was the one that said, like,

895
00:53:07,920 --> 00:53:11,319
Speaker 1: you're you have to be able to falsify something for

896
00:53:11,400 --> 00:53:14,680
Speaker 1: it to be disproven or supported, and if not, then

897
00:53:14,680 --> 00:53:18,399
Speaker 1: it's pseudoscience. Well, part and parcel of that is that

898
00:53:18,560 --> 00:53:22,360
Speaker 1: what you're saying has to be able to be detected empirically.

899
00:53:22,640 --> 00:53:25,279
Speaker 1: There's some way that it has to the presence of

900
00:53:25,280 --> 00:53:28,640
Speaker 1: it has to be measured or inferred. And so a

901
00:53:28,640 --> 00:53:32,560
Speaker 1: lot of people say, well, then with the scientific method

902
00:53:33,000 --> 00:53:36,279
Speaker 1: it reaches it's the limits of its current usefulness when

903
00:53:36,320 --> 00:53:40,120
Speaker 1: it tries to explain the supernatural. When somebody says, like,

904
00:53:40,480 --> 00:53:44,360
Speaker 1: ghosts are real, exactly, you can't prove that, well, you

905
00:53:44,400 --> 00:53:48,799
Speaker 1: also can't disprove it either, right, And so if you

906
00:53:48,880 --> 00:53:53,680
Speaker 1: are a scientist who says, uh, because the scientific method

907
00:53:53,760 --> 00:53:57,840
Speaker 1: can't prove or disprove the existence of ghosts or God,

908
00:53:58,680 --> 00:54:01,480
Speaker 1: there is no such thing as ghosts or God, you're

909
00:54:01,520 --> 00:54:04,880
Speaker 1: making a leap of faith just as much as the

910
00:54:04,880 --> 00:54:08,080
Speaker 1: same person who says science can't prove or disprove the

911
00:54:08,120 --> 00:54:11,439
Speaker 1: existence of ghosts or God, therefore God's and ghosts are real.

912
00:54:12,560 --> 00:54:15,240
Speaker 1: They're both leaps of faith. And that really the most

913
00:54:15,280 --> 00:54:19,080
Speaker 1: scientific approach to the existence of the supernatural, whether it

914
00:54:19,200 --> 00:54:22,560
Speaker 1: is ghost or god, is that we simply don't know,

915
00:54:22,960 --> 00:54:26,520
Speaker 1: and that we cannot know scientifically. And but that doesn't

916
00:54:26,560 --> 00:54:29,560
Speaker 1: mean that it does exist or doesn't exist. And that's

917
00:54:29,560 --> 00:54:32,720
Speaker 1: saying that science shows that it does or doesn't exist,

918
00:54:32,840 --> 00:54:35,920
Speaker 1: is by by definition, the opposite of what science shows.

919
00:54:36,400 --> 00:54:41,480
Speaker 1: Science shows neither. It's not capable of showing or showing

920
00:54:41,520 --> 00:54:44,799
Speaker 1: that something doesn't exist. That's a good point. Uh. The

921
00:54:44,840 --> 00:54:48,279
Speaker 1: other place where science can get corrupted is when it

922
00:54:48,480 --> 00:54:52,279
Speaker 1: blurs the lines, or when people blur the lines between uh,

923
00:54:52,480 --> 00:54:57,800
Speaker 1: moral judgments and science value judgments. Like you can study

924
00:54:57,840 --> 00:55:00,319
Speaker 1: global warming, you can study cause and effect, you can

925
00:55:00,320 --> 00:55:05,040
Speaker 1: report data, but when you make that secondly to say,

926
00:55:05,320 --> 00:55:07,319
Speaker 1: and this is a scientist, I mean someone can come

927
00:55:07,320 --> 00:55:09,840
Speaker 1: along and say global warming is bad, shouldn't drive your suv.

928
00:55:10,239 --> 00:55:13,799
Speaker 1: That's fine, But a scientist can't do a study and

929
00:55:13,840 --> 00:55:18,319
Speaker 1: say that because that's, uh, that's a value judgment. And

930
00:55:18,400 --> 00:55:22,319
Speaker 1: that's where science can get corrupted pretty much. Right. You

931
00:55:22,320 --> 00:55:25,040
Speaker 1: can you can study glow warming and results until the

932
00:55:25,040 --> 00:55:28,719
Speaker 1: cows come home, but you can't assert that if you

933
00:55:28,840 --> 00:55:31,839
Speaker 1: use this lightbulb, you're a bad person, right or um

934
00:55:31,920 --> 00:55:36,120
Speaker 1: ocean acidification is bad. It's not good for humans. But

935
00:55:36,160 --> 00:55:39,479
Speaker 1: if you're a jelly fits, it's awesome, you know. So yes,

936
00:55:39,760 --> 00:55:42,600
Speaker 1: and again you made a great point. It's not science,

937
00:55:42,880 --> 00:55:48,400
Speaker 1: it's people using science to make value judgments. Yeah. So, ultimately,

938
00:55:48,640 --> 00:55:52,040
Speaker 1: the scientific method, although it does have its limitations and

939
00:55:52,160 --> 00:55:56,800
Speaker 1: that it needs empirical data, uh to prove or disprove something,

940
00:55:57,680 --> 00:56:01,000
Speaker 1: it's not that it's not flawed. That's not a flaw.

941
00:56:01,080 --> 00:56:05,520
Speaker 1: That's a limitation, and it's it's when it's misused then

942
00:56:05,560 --> 00:56:09,560
Speaker 1: its results become flawed or skewed. And that's the people

943
00:56:09,600 --> 00:56:13,799
Speaker 1: doing it, man, not science. That's right. It's pretty interesting stuff. Yeah, man,

944
00:56:13,840 --> 00:56:15,719
Speaker 1: this is a good one. I thought so too. Man.

945
00:56:16,320 --> 00:56:18,759
Speaker 1: Let it's start out with a bang boom. It's all

946
00:56:18,840 --> 00:56:21,759
Speaker 1: downhill from here. Uh. If you want to know more

947
00:56:21,760 --> 00:56:25,719
Speaker 1: about the scientific method, check out that article on the economists,

948
00:56:25,800 --> 00:56:29,040
Speaker 1: check out explorables uh, and then of course check out

949
00:56:29,280 --> 00:56:31,560
Speaker 1: the scientific method in the search bar at how stuff

950
00:56:31,560 --> 00:56:34,120
Speaker 1: works dot com. And since I said that it's time

951
00:56:34,160 --> 00:56:40,800
Speaker 1: for listener mail, that's right. But quickly before listener mail, uh,

952
00:56:41,000 --> 00:56:44,399
Speaker 1: we get asked by listeners all the time, what can

953
00:56:44,440 --> 00:56:46,879
Speaker 1: we do? Since you have a free podcast, we can't

954
00:56:46,880 --> 00:56:49,560
Speaker 1: pay for it. What can we do to help you, guys?

955
00:56:49,600 --> 00:56:53,640
Speaker 1: And one thing you can do that we would appreciate is, uh,

956
00:56:53,840 --> 00:56:56,360
Speaker 1: go to iTunes and leave a rating and a review

957
00:56:56,480 --> 00:57:00,319
Speaker 1: for us. That makes big, big difference and pen us

958
00:57:00,400 --> 00:57:03,000
Speaker 1: up there in the rankings, which means more people find

959
00:57:03,000 --> 00:57:06,240
Speaker 1: stuff you should know. After they listen to Cereal, they'll

960
00:57:06,280 --> 00:57:08,560
Speaker 1: just say, well, jeez, there's other podcasts in the world,

961
00:57:09,120 --> 00:57:13,399
Speaker 1: what is this pod cast? So ratings and reviews really

962
00:57:13,480 --> 00:57:15,440
Speaker 1: help us out and it doesn't cost you anything but

963
00:57:15,480 --> 00:57:17,960
Speaker 1: a few minutes. Um, be honest, We're not saying go

964
00:57:18,080 --> 00:57:20,520
Speaker 1: leave us some great review, but go leave us a

965
00:57:20,600 --> 00:57:25,000
Speaker 1: great review. You said it, Uh, and tell tell one

966
00:57:25,000 --> 00:57:27,080
Speaker 1: person about stuff you should know. We would appreciate that

967
00:57:27,120 --> 00:57:30,880
Speaker 1: to turn somebody onto the show. And um, that's it.

968
00:57:30,960 --> 00:57:34,280
Speaker 1: That's our version of a pledge drive because they wow,

969
00:57:34,640 --> 00:57:36,960
Speaker 1: we do that what once every three years? No? Not

970
00:57:36,960 --> 00:57:40,760
Speaker 1: not very obnoxious and at last all right, So onto

971
00:57:40,760 --> 00:57:43,040
Speaker 1: listener mail. This is from my sister in law. Actually,

972
00:57:43,680 --> 00:57:47,480
Speaker 1: oh yeah, this is some nepotism. Yeah, Jenny Jenny Bryant,

973
00:57:47,560 --> 00:57:52,200
Speaker 1: she mentioned in the homeschool episode homeschooled her kids for

974
00:57:52,240 --> 00:57:55,400
Speaker 1: a little while, and she sort of corrected me Uh

975
00:57:55,600 --> 00:57:58,080
Speaker 1: love the homeschooling episode. Guys. One very big trend these

976
00:57:58,160 --> 00:58:02,760
Speaker 1: days in the home schooling community is what Abbey my

977
00:58:02,840 --> 00:58:06,760
Speaker 1: niece does, which is hybrid homeschooling. So two to three

978
00:58:06,840 --> 00:58:09,680
Speaker 1: days a week she is at school and then the

979
00:58:09,720 --> 00:58:13,840
Speaker 1: rest of the time she's a plant. She's not a plant. Uh,

980
00:58:14,000 --> 00:58:15,720
Speaker 1: the rest of time she's at home. So she says

981
00:58:15,720 --> 00:58:18,880
Speaker 1: it's a great option with curriculum provided and new topics

982
00:58:18,880 --> 00:58:22,040
Speaker 1: sought at school and then worked out at home. Many

983
00:58:22,080 --> 00:58:25,960
Speaker 1: of these schools are accredited making getting into college, including

984
00:58:26,000 --> 00:58:29,960
Speaker 1: Ivy League schools, Hassle Free and Abbey School has sports teams,

985
00:58:30,680 --> 00:58:34,600
Speaker 1: homecoming Abbey is actually an excellent volleyball player, Beta club,

986
00:58:34,640 --> 00:58:38,240
Speaker 1: newspaper staff, all the good stuff. The flexibility is great

987
00:58:38,240 --> 00:58:40,560
Speaker 1: for families, and we are huge fans of how the

988
00:58:40,640 --> 00:58:43,920
Speaker 1: hybrid approach prepare students for college by allowing them time

989
00:58:44,000 --> 00:58:46,600
Speaker 1: outside of class to manage their work and life schedules.

990
00:58:47,320 --> 00:58:53,080
Speaker 1: So that's from Jenny actually via text first listener mail

991
00:58:53,200 --> 00:58:55,640
Speaker 1: via text How did you print that out? Did you

992
00:58:55,680 --> 00:58:59,280
Speaker 1: retype it and print it? Oh? Dude, are you serious?

993
00:58:59,800 --> 00:59:03,360
Speaker 1: You can part from texts? No, you just copy pasted

994
00:59:03,440 --> 00:59:06,320
Speaker 1: to an email? Oh yeah, yeah, I forgot about that. Men, then,

995
00:59:06,480 --> 00:59:09,280
Speaker 1: how in the world did you did you do that

996
00:59:09,320 --> 00:59:13,160
Speaker 1: with your thoughts? I have a niece who is excellent

997
00:59:13,200 --> 00:59:16,680
Speaker 1: at volleyball too. Hold, we should get them together. I

998
00:59:16,680 --> 00:59:19,040
Speaker 1: don't know. It's ten eleven, okay or something like that.

999
00:59:19,080 --> 00:59:21,760
Speaker 1: Abby just turned thirteen, so there. Oh, maybe they face

1000
00:59:21,840 --> 00:59:24,880
Speaker 1: off against one another. Yeah, is she in Atlanta? Yes,

1001
00:59:24,920 --> 00:59:28,680
Speaker 1: she's up in Canton. You never know where's aving. She's

1002
00:59:28,720 --> 00:59:31,240
Speaker 1: in Roswell. But they think with volleyball they kind of

1003
00:59:31,240 --> 00:59:34,040
Speaker 1: have played all over the state. Bizarre if they play

1004
00:59:34,080 --> 00:59:35,880
Speaker 1: each other. Yeah, we'll just see each other at a

1005
00:59:35,880 --> 00:59:38,280
Speaker 1: match one day on opposite sides of the court with

1006
00:59:38,440 --> 00:59:43,480
Speaker 1: our arms folded. Yeah. Uh, what else? I got nothing else? Well,

1007
00:59:43,640 --> 00:59:46,880
Speaker 1: like Chuck said, go leave us a review. And if

1008
00:59:46,920 --> 00:59:48,440
Speaker 1: you want to get in touch with us, you can

1009
00:59:48,440 --> 00:59:51,120
Speaker 1: tweet to us at s y s K podcast. You

1010
00:59:51,160 --> 00:59:53,840
Speaker 1: can join us on Facebook, dot com, slash Stuff you

1011
00:59:53,840 --> 00:59:58,840
Speaker 1: Should Know. You can email us and we still do that. Yeah,

1012
00:59:58,880 --> 01:00:02,240
Speaker 1: you can't text me at uh stuff podcasts at how

1013
01:00:02,280 --> 01:00:04,600
Speaker 1: Stuff Works dot com and has always joined us at

1014
01:00:04,600 --> 01:00:09,720
Speaker 1: our home on the web. Stuff you Should Know dot Com.

1015
01:00:09,800 --> 01:00:11,560
Speaker 1: Stuff you Should Know is a production of I Heeart

1016
01:00:11,600 --> 01:00:14,520
Speaker 1: Radios how stuff works. For more podcasts for my heart Radio,

1017
01:00:14,600 --> 01:00:17,280
Speaker 1: visit the iHeart Radio app, Apple podcasts, or wherever you

1018
01:00:17,320 --> 01:00:18,480
Speaker 1: listen to your favorite shows.