Speaker 1: Welcome to Stuff You Should Know, a production of I
Speaker 1: Heart Radio. Hey, and welcome to the podcast. I'm Josh Clark,
Speaker 1: and there's Charles w Chuck, Bryan, and Jerry's here. Jerry's back.
Speaker 1: Everybody looking well rested and sun kissed and everything, and
Speaker 1: this is stuff you should know. She's like a beautiful,
Speaker 1: juicy orange. That's right. That's right, Chuck, that's a really
Speaker 1: apt description. Ready to be squaws. I wish I could
Speaker 1: squeeze her, but we're still not squeezing. Uh no, not
Speaker 1: in this pandemic. Are you crazy? You out of your mind? No,
Speaker 1: no squeezing. Um Yeah, even Robert, Robert Plant wouldn't let
Speaker 1: you anywhere near him. Okay, check figure out that joke.
Speaker 1: Robert the squeeze my my lemon, the lemon song, No
Speaker 1: until the juice runs down my leg. Yeah, that's the
Speaker 1: lemon song. Right. No, I don't think so. I think
Speaker 1: it is. I don't think it is, Okay, I think
Speaker 1: it's a I don't think it is the lemon song, man.
Speaker 1: I think it's a whole lot of love. It's whole
Speaker 1: lot of love, all right. It's maybe the dirtiest thing
Speaker 1: that was ever said in like a top ten song. Okay, regardless,
Speaker 1: I'll just let the emails take care of this, I
Speaker 1: really think. No, the Lemon song is ums I love
Speaker 1: those lemons, right, No, the Lemon song is all about
Speaker 1: how how how you have friends, like you want to
Speaker 1: have friends, and like friends are good to have. Okay, yeah,
Speaker 1: may be all wrong. No, I think it's a whole
Speaker 1: lot of love. Yeah it is. I'm a dcent sure buddy.
Speaker 1: All right, well I encourage you not to google the lyrics.
Speaker 1: Then well we um we could ask our good friend
Speaker 1: and um stuff. You should know writer Ed Grabmanowski the
Speaker 1: grab Stir. Look at that because he is in a
Speaker 1: band and has been for a while. We've mentioned it before,
Speaker 1: space Lord, which has just a super cool Zeppelin esque
Speaker 1: sound to them. Um and they just there and um
Speaker 1: they just released a new single, which you can find
Speaker 1: on band camp by searching space Lord. Not the space Lords. No,
Speaker 1: not Vinny in the space Lords. Yeah space Lord. Just
Speaker 1: look for space Lord with some cool uh graphics, and
Speaker 1: that'll you know that's Ed. You'll know it's the Grabster
Speaker 1: but uh yeah, good stuff. We also have a game
Speaker 1: out that Trivial Pursuit made. Yeah, we should plug our
Speaker 1: own stuff every now and then. We just did. Yes,
Speaker 1: it is a co branded game with Trivial Pursuit from Hasbro,
Speaker 1: and it is not a trivial pursuit game that you
Speaker 1: are used to. It is a stuff you should know
Speaker 1: game that Trivial Pursuit was happy to UH co brandwith.
Speaker 1: So just what, I don't want his emails that are
Speaker 1: like this, this this is a Trivial Pursuit. This is some
Speaker 1: other different game. You're always worried about the emails, aren't you.
Speaker 1: Don't you just ignore them? Let him roll off, roll
Speaker 1: off my back, Like, I'm disappointed in you guys for this.
Speaker 1: I haven't even listened to the episode, but I'm disappointed
Speaker 1: about that. I just got one of those. Did you
Speaker 1: see that It just rolls off your back? Yeah, yeah,
Speaker 1: those are those are always great. I didn't listen, But
Speaker 1: here's what was wrong. I read that person back. Actually,
Speaker 1: I was like, we actually kind of did exactly what
Speaker 1: you hoped we would do. And they're like, oh, sorry
Speaker 1: for being presumptuous anyway, Oh all is forgiven. So, um,
Speaker 1: we're talking today about bias Chuck, and I want to
Speaker 1: set the scene a little bit because you know, um,
Speaker 1: one of the things that I'm always like harping up
Speaker 1: about is like the death of expertise, right, And it's
Speaker 1: a real problem, like this idea that science can't be trusted,
Speaker 1: that people who go and spend a decade or more
Speaker 1: learning about a specific thing that they go out and
Speaker 1: become an expert in or that's their profession, that's their training,
Speaker 1: um that those people what they have to say is
Speaker 1: basically meaningless, or that it's it's no better than somebody
Speaker 1: on the internet's opinion about so that specific subject that
Speaker 1: that person spent ten or twelve years being trained to
Speaker 1: be an expert in. Like that kind of stuff to
Speaker 1: me is like super dangerous, Like it it's it's in
Speaker 1: a there's an erosion of something, and it's a an
Speaker 1: erosion of intelligence to start with, but it's also an
Speaker 1: erosion of just believing in facts and knowing that you're
Speaker 1: not being taken for a ride or hustled. And is
Speaker 1: it a huge enormous problem that we're just beginning to
Speaker 1: wake up too and is still unfolding. It's not like
Speaker 1: it happened and now we're like real from it. It's
Speaker 1: still happening in real time, and it is a massive,
Speaker 1: huge issue one of the biggest issues that that humanity faces,
Speaker 1: I think because it encompasses so many other large issues
Speaker 1: like climate change, existential risks, the pandemic, um politics, all
Speaker 1: of them kind of fall under this this erosion of
Speaker 1: belief and facts and that there are people out there
Speaker 1: who know more than you do. Um, it's a big problem. Yeah,
Speaker 1: imagine being someone who studied and researched something intensely for
Speaker 1: ten or fifteen years, uh that with when presenting facts
Speaker 1: to be met with. I don't know about that. That's
Speaker 1: a response I hear a lot in the South. Yeah,
Speaker 1: or that they saw something on YouTube that flatly contradicts that,
Speaker 1: and it's like that it doesn't matter what you just said.
Speaker 1: Is ridiculous that somebody posted something on YouTube and that
Speaker 1: that like that has as much weight is what somebody
Speaker 1: who spent ten or twelve year studying this very thing
Speaker 1: has to say about it, like knows exactly what they're
Speaker 1: talking about, has to say about it. It's it's maddening. Yeah,
Speaker 1: there's there's something about people from the South in general,
Speaker 1: and I think that are in this group that I
Speaker 1: have literally heard that response from a lot of different
Speaker 1: people when I've been like oh no, no no, no, here
Speaker 1: the facts actually, and then when presented with something that
Speaker 1: they can't refute, they say, I don't know about that,
Speaker 1: and like that's it. That's the end of the conversation.
Speaker 1: That's different than the people I've encountered. The people I
Speaker 1: encountered like their brow furrows and they start pointing fingers
Speaker 1: and their their tone goes up, like you are you
Speaker 1: hanging out at the country club or something. I think
Speaker 1: it's different types of people that, you know, there's ignorance,
Speaker 1: and then there's also people that actually think they're better
Speaker 1: informed that will fire back with YouTube clips. Right. So
Speaker 1: the reason I brought that up is because and one
Speaker 1: of the reasons that that is being allowed to exist,
Speaker 1: that that does exist, I think it's a it's a
Speaker 1: reaction to something else that's going on simultaneously, which is
Speaker 1: there are a lot of experts out there who are
Speaker 1: performing really sloppy science, sometimes outright fraudulent science, and they're
Speaker 1: frittering away whatever faith the general public or society has
Speaker 1: in their expertise and in their profession. And there are
Speaker 1: a ton of scientists out there. I would say the
Speaker 1: vast majority, by far of scientists are legitimate upstanding, upright
Speaker 1: dedicants to science, right, That's where they that's where they
Speaker 1: place there, that's where they hang their hat, that's where
Speaker 1: their heart is, that's that's what they believe in, and
Speaker 1: that's what they work to support. But science has like
Speaker 1: kind of a problem, Chuck in that it's allowing way
Speaker 1: too much for bias, which is what we're gonna talk about,
Speaker 1: to creep into science, um and your mind science and
Speaker 1: basically produce papers that are just useless and trash. And
Speaker 1: there's a whole lot of reasons for it, but it's
Speaker 1: a it's something that needs to be addressed if we're
Speaker 1: ever going to get back on a footing with a
Speaker 1: faith in experts and expertise and just facts that there
Speaker 1: are such things as objective facts. Yeah, I mean a
Speaker 1: lot of times it's financially related, whether it's a lack
Speaker 1: of funding, a desire for more funding, a desire just
Speaker 1: to keep your your uh, your lab running and people
Speaker 1: paid on staff. Which you know, all this stuff is understandable.
Speaker 1: You want to keep doing this work, but you can't
Speaker 1: let that get in the way. It's like it's like
Speaker 1: in Rushmore at the end when Margaret Yang faked the
Speaker 1: results of that science experiment because she didn't want it
Speaker 1: to be wrong. You know, I don't remember what um,
Speaker 1: I don't remember that part was that like a deleted scene? No? No, no,
Speaker 1: was it in the end when they meet up and
Speaker 1: he's flying the Uh. I think he's flying the kite
Speaker 1: with Dirk and she's talking about her science fair project
Speaker 1: and he was really impressed with it, and she was like,
Speaker 1: I fake the results. And the reason why was because
Speaker 1: because she didn't want to be wrong. Uh. And I
Speaker 1: think a lot of times people will get into a
Speaker 1: certain body of research or data too because they want
Speaker 1: to prove a certain thing and if they can't, it
Speaker 1: might be really hard to live with that. So that
Speaker 1: weighs into it. Uh, money for personal gain, uh, advancing
Speaker 1: your career, you know, publisher, parish, that whole thing. Like,
Speaker 1: we're gonna talk about all this, but there are a
Speaker 1: lot of reasons that it's been allowed to creep in,
Speaker 1: But all of it is at the disservice of their
Speaker 1: the fundamentals of what they base their careers on to
Speaker 1: begin with. Yeah, it's at the it's at the disservice
Speaker 1: of science itself, right, because the whole point of science
Speaker 1: and then the scientific publishing, the whole publishing industry, um
Speaker 1: is to to basically create a hypothesis, test your hypothesis,
Speaker 1: and then share the results with the world. And that's
Speaker 1: I deally what would happen because you're building this body
Speaker 1: of scientific knowledge. But money and corporate interests and academic
Speaker 1: publishing have all kind of come in and taken control
Speaker 1: of this whole thing, and as a result, a lot
Speaker 1: of the stuff that gets published are trash papers that
Speaker 1: shouldn't be published. A lot of the really good papers
Speaker 1: that don't come up with sexy results don't get published.
Speaker 1: And then, like you said, um, people using science for
Speaker 1: personal gain. There are a very small chadra of thoroughly
Speaker 1: evil people who are willing to use their scientific credentials
Speaker 1: to create doubt in the general public, to to prevent
Speaker 1: like people from understanding that climate change was real for
Speaker 1: twenty years, or um, that fossil fuels actually do contribute
Speaker 1: to to anthropogenic climate change. But what remains focusing on
Speaker 1: is like bias in the in the sense that people
Speaker 1: carrying out studies are human beings, and human beings are flawed.
Speaker 1: We're just flawed, and we bring those flaws to our studies,
Speaker 1: and that you really have to work hard at rooting
Speaker 1: those flaws and those biases out to produce a really good,
Speaker 1: thorough scientific study with good reliable results that can be
Speaker 1: reproduced by anybody using the same methods. Um and that
Speaker 1: science is just starting to wake up to the idea
Speaker 1: that it is really biased and it needs to take
Speaker 1: these into account in order to to progress forward from
Speaker 1: the point that it's at right now, which is tenuous,
Speaker 1: I think, perhaps more tenuous than ever the point that
Speaker 1: science is at. I think. So science isn't going away.
Speaker 1: It's not going anywhere. It's probably the greatest of course
Speaker 1: humans have ever come up with. Right, It's not going anywhere,
Speaker 1: But it is a terrible position that it's in, and
Speaker 1: it's going to take some genuine leadership in the scientific
Speaker 1: community from a bunch of different quarters in a bunch
Speaker 1: of different fields to basically step up and be like, guys,
Speaker 1: this is really bad and we need to change it now,
Speaker 1: and a lot of people need to be called out.
Speaker 1: In science typically shies away from naming names and calling
Speaker 1: out by name fraudulent scientists because scientists seem to like
Speaker 1: to um suppose the best in people, which is not
Speaker 1: always the case, right, And having said all of this,
Speaker 1: there could we could root out every bias and and
Speaker 1: and really clean up the scientific publishing community. And there's
Speaker 1: still a set a certain set of people in this
Speaker 1: country and in the world who that wouldn't matter to
Speaker 1: and would still shut down facts and because it doesn't
Speaker 1: fit their narrative, so for sure, But the people have
Speaker 1: always they've always been there, right, and they're always going
Speaker 1: to be there. There's always it's just countrarians that they
Speaker 1: are you can call, and free thinkers you can call them, stubborn,
Speaker 1: you can call purposefully, purposefully ignorant who knows they're always
Speaker 1: going to exist. The problem that this crisis that science
Speaker 1: finds itself in right now is that it's allowed that
Speaker 1: that population to grow and grow, and like people who
Speaker 1: otherwise didn't never really question science have been allowed to
Speaker 1: kind of trickle into that fold. And that those are
Speaker 1: the people that we should be worried about, the ones
Speaker 1: who would would know better if they believed in science again, right,
Speaker 1: And our way into this is to talk about different
Speaker 1: kinds of biases in true stuff. You should know fashion
Speaker 1: a top ten that is not a top ten. That's
Speaker 1: exactly right. We ate into at least three in this
Speaker 1: intro and hopefully shining a light on some of this stuff.
Speaker 1: People at least be more aware of different biases. And uh, well, yeah,
Speaker 1: you know. The first one is is good old confirmation bias.
Speaker 1: I mean, these aren't ranked because confirmation bias would probably
Speaker 1: be number one as far as people's awareness of it.
Speaker 1: But there are different examples, um that people use for
Speaker 1: confirmation bias. And I kind of enjoyed the one from
Speaker 1: the house Stuff Works article even though it was from
Speaker 1: N three. After X rays were discovered in Germany, there
Speaker 1: was a French scientist named Renee Blond Blonde Lot. Yeah,
Speaker 1: he looked at as the X rays he said, wow,
Speaker 1: Well who said, ay, I see N rays I've discovered
Speaker 1: in rays And everyone's like, what's an N ray? He said, well,
Speaker 1: it's like a corona when electricity discharges from a crystal
Speaker 1: and you can only see it in your peripheral vision.
Speaker 1: And American Robert Wood laid the wood and said, I'm
Speaker 1: gonna come to your lab and check this out and
Speaker 1: secretly remove the crystals during one of the experiments, and
Speaker 1: Blonde Lot still saw these N rays and so that's
Speaker 1: confirmation bias. He wanted to see those in rays. And
Speaker 1: then later, even though it was disproved, other French scientists
Speaker 1: supposedly published papers or published papers based on that research
Speaker 1: because they wanted it to be true. So that's what
Speaker 1: confirmation biases is when you're starting out with a hypothesis
Speaker 1: that is going to shape the methodology methodology of your
Speaker 1: study to to confirm it right. And then it can
Speaker 1: also occur where you're um. You you're interpreting info um
Speaker 1: to fit your hypothesis, so you're seeking out stuff that
Speaker 1: supports your hypothesis, and then the stuff that you is
Speaker 1: that's just there in front of you, the results, they
Speaker 1: are there in front of your like, ah, this thing
Speaker 1: proves that those end rays actually exist, um or this
Speaker 1: phenomenon cannot be due to anything but end raise. Therefore
Speaker 1: end Raise exists all of its confirmation bias. And that,
Speaker 1: like you said, that's number one, because that's not just
Speaker 1: a scientific bias. I mean, like that is that's like
Speaker 1: every human uses confirmation bias, and that it's two fold.
Speaker 1: We we avoid contradictory information because we UM I don't
Speaker 1: like to be wrong, and we find information that confirms
Speaker 1: our point of view because we like to be right.
Speaker 1: That's it's confirmation bias, and it's everywhere among everyone. That's right,
Speaker 1: Although I will say I know it happens a lot politically,
Speaker 1: but myself and the people that I congregate with, uh
Speaker 1: question their own leaders as much as they do leaders
Speaker 1: from the other parties. Oh, it's good, it's very good
Speaker 1: to do. And I don't know, there shouldn't be sacred
Speaker 1: calves and politics. That's a bad jam, well know. And
Speaker 1: it's like I've always being been like at the forefront
Speaker 1: of calling out my own parties wrongs and saying no, no, no,
Speaker 1: that's you need to do better than that, whereas I
Speaker 1: see a lot of other people in other situations truly
Speaker 1: bury and ignore those things because they don't you know,
Speaker 1: I just don't want to face that. Yeah. And it's
Speaker 1: not like it's not even like I don't want to
Speaker 1: face it. It just doesn't fit their worldview, so they
Speaker 1: just don't include it. It It just gets tossed out. But
Speaker 1: the point is is like it's not an active process necessarily, right,
Speaker 1: I think we should probably check our first break. I
Speaker 1: think so too. Chi All right, we'll be right back
Speaker 1: and talk about sampling bias right after this alright, Chuck,
Speaker 1: we're back, and we're coming back with UM, something called
Speaker 1: sampling bias, which is it turns out a sub type
Speaker 1: of a larger thing called selection bias, and one other
Speaker 1: thing we should say. We kind of got into it
Speaker 1: before I could say this. There are different stages in
Speaker 1: a study where bias can occur. It can happen in
Speaker 1: like the planning the pre study phase, UM and and uh,
Speaker 1: it can happen during the actual study, and then it
Speaker 1: can happen after the study as well. And so when
Speaker 1: we're talking about any kind of selection bias, including sampling bias,
Speaker 1: this is pre study bias where when you're actually setting
Speaker 1: up the study, this bias is where this is where
Speaker 1: the bias is going. Yeah, and you know what, I
Speaker 1: think it also bears saying that biases you have to
Speaker 1: work really hard to avoid it because it's it's almost
Speaker 1: like a disease that's always trying to get involved. And
Speaker 1: it's not like just do better, everybody and quit being biased.
Speaker 1: It's like it's way more complicated than that, because it
Speaker 1: is always knocking at the door, like you said, in
Speaker 1: all three phases, trying to sneak in there, and it
Speaker 1: takes a lot of work in all three phases to
Speaker 1: avoid it. So it's not as I don't want it
Speaker 1: to come across this as easy as us just saying
Speaker 1: like you shouldn't do that, stop it. No, But the
Speaker 1: first step is to recognizing that there's a lot of
Speaker 1: bias and different kinds of bias that are just sitting
Speaker 1: there waiting for a scientist. And then if you start
Speaker 1: admitting that it's there, you can start being on the
Speaker 1: lookout for it, and you can start adjusting for it,
Speaker 1: and then other people who read your papers or here
Speaker 1: you know, read news articles about your papers, can be
Speaker 1: on the lookout for that kind of thing. Yeah, so exactly, Uh,
Speaker 1: sampling bias is your you know, your sample set not
Speaker 1: being accurate and a good representation of the whole. A
Speaker 1: lot of times you'll find this um in either studies
Speaker 1: that are really small scale because you don't have a
Speaker 1: large sample and you don't have the kind of money
Speaker 1: like near you. Like maybe you work for university, so
Speaker 1: you work with university students as your first sample set,
Speaker 1: who are not indicative of anything, but you know, people
Speaker 1: eighteen to twenty one years old or so. Now, remember
Speaker 1: we talked about weird Western educated and realized rich and democrats.
Speaker 1: That's exactly the thing. It's like, I mean, it's a
Speaker 1: it's a decent place to start if you don't have
Speaker 1: much money and you want to get the ball rolling.
Speaker 1: It's not like, oh, you shouldn't do university studies at
Speaker 1: all and using students, but those findings definitely don't represent
Speaker 1: the wide nation and it needs to grow and get
Speaker 1: more funding if you want to actually have a legitimate
Speaker 1: claim to something. Another way that sampling bias can come
Speaker 1: up is from like the group that you're recruiting from.
Speaker 1: Like if you're doing a strictly online survey, but you're
Speaker 1: trying to apply your findings to the wider society, that's
Speaker 1: just not gonna happen because there's so many people who
Speaker 1: aren't Internet savvy enough to take an Internet survey. Like,
Speaker 1: by by nature, you are a little savvier than the
Speaker 1: average person if you're hanging out on the Internet and
Speaker 1: taking a survey. And then also kind of tangential that
Speaker 1: I like to tell myself that at least um and
Speaker 1: then tangential that is something called self selection bias, which
Speaker 1: is where the people who say, let's say you're doing
Speaker 1: a study on wellness and you know what eating tuna
Speaker 1: can do for your health. Um, people who are interested
Speaker 1: in wellness and health are going to be much more
Speaker 1: likely to volunteer for that study than people who couldn't
Speaker 1: care less about health and have no desire whatsoever to
Speaker 1: further sciences understanding of what makes you healthy. So you
Speaker 1: would have to go out and find those people and
Speaker 1: recruit them rather than just relying on the people who
Speaker 1: volunteered based on the flyer you put up in the
Speaker 1: student right, or you know, study all financial demographics or
Speaker 1: pull all financial demographics rather than just and you know,
Speaker 1: sometimes it's a methodology in which they do try and
Speaker 1: recruit people steers them in that direction unknowingly that I know.
Speaker 1: In the article they've talked about the presidential campaign with
Speaker 1: Roosevelt and alf Landon Republican alf Landon, they were doing
Speaker 1: polling with like country clubs, rosters and uh, people who
Speaker 1: drove cars and stuff. At the time that was kind
Speaker 1: of a luxury, so it was all out of whack.
Speaker 1: Everyone's like Landing's gonna win in a landslide. It's because
Speaker 1: you just kind of basically stuck your polling to uh,
Speaker 1: you know, I don't know about wealthy individuals, but people
Speaker 1: who are a little more well off. And I think, um,
Speaker 1: we talked about that in our polling episode, that is
Speaker 1: that fiasco with polling. I also saw one more two
Speaker 1: that I want to mention because that has a really
Speaker 1: great anecdote attached to It's called survivorship bias, where when
Speaker 1: you're studying something, say like business or something, you're you're
Speaker 1: probably going to just be looking at the extant businesses,
Speaker 1: the businesses that have survived twenty years, thirty years, fifty
Speaker 1: years or something like that, and you're not taking into
Speaker 1: account all of the failures. So when you put together
Speaker 1: like a prognosis for business in America, it might have
Speaker 1: a son your outlook than it should, because all you're
Speaker 1: looking at are the ones that managed to survive and thrive.
Speaker 1: And that's survivorship bias. And did you see that The
Speaker 1: anecdote about the World War Two fighter pilots. It was
Speaker 1: actually pretty funny because they studied uh planes that had
Speaker 1: been returned, that had been fired upon managed to get
Speaker 1: back safely, and they're like, well, let's look at all
Speaker 1: these different bullet holes and where this plane was hit,
Speaker 1: and let's beef up all those areas on the body,
Speaker 1: and a mathematician named Abraham Wald said, uh, no, those
Speaker 1: are the places where they got shot and did Okay,
Speaker 1: what you should really do is find these planes that
Speaker 1: actually went down and beef up those sections of the planet.
Speaker 1: And that survivorship bias. It's just it's failing to take
Speaker 1: into account the failures and that have to do with
Speaker 1: what you're trying to study. What about channeling bias. Channeling
Speaker 1: bias is another kind of selection bias. Did you get
Speaker 1: this one? It wasn't the best example of channeling bias. Yeah,
Speaker 1: I mean that's I got it, all right? Did you
Speaker 1: not get it? I got it, but it took a
Speaker 1: lot of work before I finally did. Well. It's basically
Speaker 1: when let's say you have a patient and they're like,
Speaker 1: their degree of illness might influence what group they're put into.
Speaker 1: So if a doctor or if a surgeon was trying
Speaker 1: to study outcomes of obsticular surgery, they might because they're
Speaker 1: surgeons and they want to help people out, they may
Speaker 1: perform that surgery on maybe younger, healthier people who might
Speaker 1: have better outcomes than someone who is in a different,
Speaker 1: like higher age group. Right, And the the article is
Speaker 1: kind of ends it there, and I was like, so
Speaker 1: what's the problem. And I finally found this example where
Speaker 1: where it says like, Okay, let's say you're studying a
Speaker 1: new heartburn medicine or something or something to treat like
Speaker 1: like gurd and it's new, it's hardcore, it's cutting edge UM.
Speaker 1: And the people who are likely is to get this
Speaker 1: new hardcore UM and acid are the ones who are
Speaker 1: probably in worse shape, right, so, say they're on the
Speaker 1: verge of going to the e ER. Anyway, well, if
Speaker 1: you look back at all of the people who have
Speaker 1: ever been prescribed this new hardcore and acid, you're gonna
Speaker 1: see like a lot of them ended up in the
Speaker 1: e R, even though it had nothing to do with
Speaker 1: this hardcore and acid. And then similarly, the people who
Speaker 1: have so so gird it's not particularly bad, they'll probably
Speaker 1: be prescribed the old drug, the standby that everybody knows
Speaker 1: it's fine, that's going to work. So if you compare
Speaker 1: the old drug and the new drug, it looks like
Speaker 1: the old drug is super safe, but the new drug
Speaker 1: will put you into the e R. Whereas UM, that's channeling.
Speaker 1: You've channeled different different people with different prognoses into different groups,
Speaker 1: and they're kind of pitted against each other, um in
Speaker 1: a in an effort to obscure the truth. If you
Speaker 1: wanted to really know the genuine health outcomes for that
Speaker 1: an acid, you would have to give it to people
Speaker 1: with not so bad gurd and people with really bad
Speaker 1: gurd and see what happens. See if the e ER
Speaker 1: visits continue for people who wouldn't otherwise be going to
Speaker 1: the e R Everyone, right, and not just for you,
Speaker 1: Like if you're debating surgery and you're like, oh, well
Speaker 1: it says shows really good outcomes. We're like, well yeah,
Speaker 1: but who are they operating on right? Right? Yes? So
Speaker 1: I would like to invite anyone UM who got what
Speaker 1: I was saying or got channeling because of what I
Speaker 1: was saying, I invite you to email and let me know.
Speaker 1: I'm doing like a little bit of surveys here, and
Speaker 1: I'd like to know if I confuse things more or
Speaker 1: make it more understandable. Well, but I know it's funny
Speaker 1: either way. I got that part. I'm just trying to
Speaker 1: figure out its understanding. But here with your methodology talking
Speaker 1: about the stuff you should know. No listener who by
Speaker 1: nature is smarter than your average beyer. Well, I'm not
Speaker 1: going to publish it. I'm gonna file draw it either way.
Speaker 1: Oh what a teaser. UH question order bias is the
Speaker 1: next one that is uh. And this is mainly obviously
Speaker 1: with UM when you're just doing uh, like polling and
Speaker 1: stuff or like an online survey or you know, it
Speaker 1: could be just asking people a set of questions like
Speaker 1: in a social science research set, and the way you
Speaker 1: order things can affect the outcome. And this is the
Speaker 1: thing at all, like everything from the brain's tendency to
Speaker 1: organize information into patterns to the brain uh, simply paying
Speaker 1: attention more and being more interested early on. Like I
Speaker 1: know there was one The General Social Survey was a
Speaker 1: big long term study of American attitudes and in they
Speaker 1: were asked to identify the three most important qualities for
Speaker 1: a child to have, and they were given a list
Speaker 1: of these qualities. Honesty was just listed higher on the list.
Speaker 1: When it was it was pick sixty six percent of
Speaker 1: the time. When it was further down on the list,
Speaker 1: it's forty eight percent important. And that's simply because people
Speaker 1: are just reading this list that are like important and
Speaker 1: then yeah, by the time they got down, you know,
Speaker 1: three quarters of the way through the list, they started
Speaker 1: thinking about what they're gonna have for dinner. People get
Speaker 1: pooped when you're giving them lists of stuff, or you
Speaker 1: can people and get them all sort of worked up.
Speaker 1: Like if you have a question like, uh, during the
Speaker 1: Trump administration, how mad were you at that guy about
Speaker 1: stuff he did? And you're like super mad? And then
Speaker 1: you were like, well, how did you feel generally about
Speaker 1: how your life was affected during his administration? You might
Speaker 1: say it was awful, Whereas if they hadn't have asked
Speaker 1: that first question, they were just like, what was your
Speaker 1: life like from two thousand and I am blocking out
Speaker 1: the dates. What you might say, well, you know, it
Speaker 1: wasn't It was okay, man, I had a lot of
Speaker 1: sandwiches just over those four years though. Um. Yeah, so,
Speaker 1: and like you said, that's priming, which is a big
Speaker 1: it's a big thing that you have to worry about
Speaker 1: when you're doing any kind of survey. UM. So there's
Speaker 1: some of the ways that you can combat that. You
Speaker 1: can randomize your question order. Um. Sometimes you'll have a
Speaker 1: survey where one question is predicated on a previous question.
Speaker 1: So one thing you might want to do is UM
Speaker 1: ask that set of questions in a few different ways. UM.
Speaker 1: So that yeah, so that you can kind of maybe, um,
Speaker 1: compare the answers to all three, at them up and
Speaker 1: divide them by three and there's your average answer kind
Speaker 1: of thing. Um. There's a lot of things you can
Speaker 1: do to kind of, I guess, um, manipulate to de
Speaker 1: manipulate your respondent when you're doing a survey like that, manipulate, manipulate.
Speaker 1: Look it up. You won't find anything on it, but
Speaker 1: you could look it up still. Oh it's a Roxy
Speaker 1: music album. Wow, Chuck, Wow, that was great. What's next?
Speaker 1: So with question order bias, we've entered the during the
Speaker 1: study kind of bias, like this is this is why
Speaker 1: you're actually conducting the study, and so is interviewer bias. Um,
Speaker 1: an interviewer bias. It's kind of like, well, question order
Speaker 1: biases has to do more with the study design, but
Speaker 1: it's a bias that emerges during the study interview or
Speaker 1: bias just straight up is in the middle of the study,
Speaker 1: and it has to do with the person actually asking
Speaker 1: the questions in an interview. UM. It can also I
Speaker 1: think apply to somebody conducting uh, like the like a
Speaker 1: a clinical trial and a drug. If they know whether
Speaker 1: somebody is getting placebo or not, it might affect their behavior.
Speaker 1: But ultimately what it is the the person who's wearing
Speaker 1: the white lab coat is influencing the outcome of the
Speaker 1: of the study just through their behavior, through their tone
Speaker 1: of voice, through the way that they're asking a question.
Speaker 1: Sometimes it can be really overt and like let's say, um,
Speaker 1: like a super devout Christian is is, you know, doing
Speaker 1: a study on whether how many what part of the
Speaker 1: population believes Jesus saves? And they might be like, you know, Jesus,
Speaker 1: don't do you think Jesus saves? Is the question? Don't
Speaker 1: you like? It seems like it? Huh, that kind of
Speaker 1: thing would be a pretty extreme example, but it's it's
Speaker 1: sometimes how you understand things is in the absurdities, you know. Yeah,
Speaker 1: I thought this example in the House of Works article
Speaker 1: is kind of funny. It was about just like a
Speaker 1: medical questionnaire where the interviewer knows that the subject has
Speaker 1: a disease that they're talking about, and they may probe
Speaker 1: more intensely for the known risk factors. And they gave
Speaker 1: smoking as an example, and it said so they may
Speaker 1: say something like, are you sure you've never smoked, never,
Speaker 1: not even one. Like, if I heard that coming from
Speaker 1: a researcher, even without knowing a lot about this, would say, what,
Speaker 1: what kind of a researcher are you? Like it seems
Speaker 1: like you're looking for an answer you should you should
Speaker 1: say you are ethically compromised, or even facial expressions or
Speaker 1: you know, body language. All that stuff weighs in I
Speaker 1: don't know, tybrow, Like, why don't they just have the
Speaker 1: robots Alexa or Google or Syria or somebody ask them? Well,
Speaker 1: that's one good thing about something like an Internet survey
Speaker 1: is like it's it's just questions, and as long as
Speaker 1: you design the questions and you randomize their presentation like it,
Speaker 1: it's gonna be fairly helpful in that respect. But then
Speaker 1: it's kind of its own pitfalls and pratfalls. You can
Speaker 1: attract a lot of people who are just taking it
Speaker 1: to mess with you, and there's a lot of problems
Speaker 1: with with all of it. But again, if you're aware
Speaker 1: of all the problems, you can plan for him. And
Speaker 1: then even if you can't plan for him or control them,
Speaker 1: you can write about it in the actual study and
Speaker 1: be like this this study I remember running across studies
Speaker 1: before where they're basically like, there are you know, there
Speaker 1: was a kind of bias that we couldn't we couldn't
Speaker 1: control for, so we can't really we can't really say
Speaker 1: whether it affected this the outcome or not. And I thought, wow,
Speaker 1: this is really refreshing and like even daring kind of
Speaker 1: like I was thrilled. Um, But you don't see that
Speaker 1: very often. But that, from what I am understand, is
Speaker 1: the direction that science is going towards now well, and
Speaker 1: the reason you don't see that. And then something we'll
Speaker 1: talk about is is what actually ends up getting published.
Speaker 1: The it may be less likely to get published if
Speaker 1: they're like, hey, you know what, you know what I'm saying? Yeah,
Speaker 1: I know. So let's do recall an acquiescence bias because
Speaker 1: they're very much related, and then we'll take a break.
Speaker 1: That's our plan. What do you think of it? Everyone
Speaker 1: says it sounds good to me, all right. So this
Speaker 1: is also during study, and this is so in the
Speaker 1: very much the way that an interviewer can influence the outcome,
Speaker 1: the participant can actually influence the outcome too, especially if
Speaker 1: they're being asked questions or they're they're being asked to
Speaker 1: self report. Um, there's a couple of ways that us
Speaker 1: just being humans can foul up the works on on
Speaker 1: the findings of a study. The first is recall bias. Yeah.
Speaker 1: This is when you're obviously you're trying to recall something
Speaker 1: from the past. And it's amazing what might jump out
Speaker 1: at you from your past when probed with a certain question,
Speaker 1: certain correlations that really have nothing to do with it
Speaker 1: with you. But you may be like, oh, well, you
Speaker 1: know what, now that I think back, I remember around
Speaker 1: that time I was I was watching a lot of
Speaker 1: Dancing with the Stars. I kind of binge that show.
Speaker 1: So maybe that's why I had homicidal tendencies. I don't
Speaker 1: think you need to study to prove that. I think
Speaker 1: that's just intuition, you know. Yeah. Um but yeah, so,
Speaker 1: and if enough people do that, especially if there's something
Speaker 1: out kind of in like the zeitgeist about that, how
Speaker 1: like people who watch too much Dancing with the Stars
Speaker 1: want to kill other people? Um, like, a number of
Speaker 1: your participants might recall that thing, whereas other people who
Speaker 1: don't watch Dancing with the Stars aren't going to recall that.
Speaker 1: And so in the same way that um, survivorship bias
Speaker 1: influences that those people who don't have that memory to
Speaker 1: recall that memory can't possibly be included in the study results,
Speaker 1: which means that Dancing with the Stars is going to
Speaker 1: kind of percolate to the top as like a major
Speaker 1: risk factor in homicidal tendencies. Right, that's not good. You
Speaker 1: don't want you don't want Dancing with the Stars unfairly canceled.
Speaker 1: You want to be canceled because it is terrible. I've
Speaker 1: never seen it. I'm sure it's great if you're not dancing.
Speaker 1: I haven't either. But watch we're gonna be asked to
Speaker 1: be on Oh my God, and they'd have to change
Speaker 1: the name of the show to Dancing with the mid
Speaker 1: level Internet famous right exactly. Wow, dust off my jazz shoes.
Speaker 1: It would be us in Chocolate Rain. And you know
Speaker 1: I love that guy. Tjon Day is his name. Yeah,
Speaker 1: we actually met him that time. I remember it was great. Man. Um.
Speaker 1: Another thing, Chuck that people that has to do with
Speaker 1: recall biases. That, um, like, we just tend to have
Speaker 1: fault to your memories with stuff that makes us look bad,
Speaker 1: like say, unhealthy habits. So if if you're doing a
Speaker 1: study on UM junk food and and health outcomes, and
Speaker 1: you interview a bunch of people who are in terrible health,
Speaker 1: and all of them are like, only like cheese. It's
Speaker 1: like once in a blue moon or something like that
Speaker 1: in the researcher, right, so once in blue moon cheese.
Speaker 1: It's um like the results of the study, you're gonna
Speaker 1: suggest that it takes just a very small amount of cheese.
Speaker 1: It's to put you in the hospital with long term
Speaker 1: chronic health conditions. And that's that is a problem with
Speaker 1: recall bias, Like it is. It's it's the participants affecting
Speaker 1: it in this case because they just aren't paying attention
Speaker 1: to aren't really thinking about No, you you've eaten a
Speaker 1: lot of cheese. It's and it takes a lot of cheese.
Speaker 1: It's to put you in the hospital, not a very
Speaker 1: little amount. It's not the best example, but it kind
Speaker 1: of gets the point cross I think now, is this
Speaker 1: part of acquiescence bias. No, that was that was the
Speaker 1: end of recall bias. Acquiescence biases, it's different, but it's
Speaker 1: certainly related. Both of them kind of fall under an
Speaker 1: umbrella of participant bias. Yeah, and acquiescence bias. I feel
Speaker 1: like there's the opposite too. I just don't know they
Speaker 1: have a name, um, because acquiescence biases is generally like
Speaker 1: people people want to be agreeable, and they want to
Speaker 1: answer in the affirmative, and they want to especially they
Speaker 1: found um, if you are maybe less educated, you might
Speaker 1: be more willing to just go along with something and
Speaker 1: say yeah, sure, yeah, yeah, um to maybe appear smarter
Speaker 1: or just to be more agreeable. I do think it's
Speaker 1: the opposite can happen too, especially with political um research
Speaker 1: in social studies and that I think there are also
Speaker 1: people that are like, oh you're from the what well, yeah, sure,
Speaker 1: I'd love to be interviewed, and then they go into
Speaker 1: it with a sort of opposite mental where they're completely
Speaker 1: disagreeable no matter what anyone says or asked. Yeah, I
Speaker 1: didn't run across that, but I'm absolutely sure that that
Speaker 1: is a bias out there. But you can avoid these
Speaker 1: by doing it more smartly, right, more smartly. Yeah, there's
Speaker 1: ways that you can, Um, you can frame your questions,
Speaker 1: like like people don't like to to admit that they
Speaker 1: didn't actually vote American Democracy, so in instead of saying
Speaker 1: there was a there was a pew a pew suggestion
Speaker 1: p um where they said um, rather than saying like,
Speaker 1: did you vote in the last election. A lot of
Speaker 1: people who didn't vote are gonna be like, sure, yeah,
Speaker 1: of course, why why would you ask that um? Instead
Speaker 1: you would phrase it as like, um, in the two
Speaker 1: thousand and twelve presidential election, Uh, did things come up
Speaker 1: that prevented you from voting? Or were you able to vote?
Speaker 1: And you would probably actually want to train your researcher
Speaker 1: to use that same intonation to make it seem casual
Speaker 1: either way, Like you want to give the person a
Speaker 1: sense of comfort that they're not being judged no matter
Speaker 1: how they answer. A good way. That's a good way
Speaker 1: to get away around acquiescence bias. Absolutely, yeah, the old
Speaker 1: backdoor policy. That's right, where you can squeeze a woman.
Speaker 1: All right, are we taking a break? I think we're
Speaker 1: We're mandated by the SEC to do that. After that joke,
Speaker 1: all right, well, we'll be back and finish up with
Speaker 1: our final two biases right after this. I also I
Speaker 1: want to apologize to all the parents who listen with
Speaker 1: their six year olds these days. My daughter is sick.
Speaker 1: She doesn't care about what we do. That's great. So
Speaker 1: it's still just flying overhead, right, I mean she didn't
Speaker 1: even listen. She like movie crush a little bit. Well,
Speaker 1: some kids there are six listen and hey, shout out
Speaker 1: to all of you guys. Listen. No, I'm always whenever
Speaker 1: I see that, whenever someone writes in says their kid
Speaker 1: my daughter's age actually listens, I'm like, really, oh, yes,
Speaker 1: this is my daughter. She loves it, right, Yeah, and
Speaker 1: she voted in the last election to like, uh, my
Speaker 1: daughter likes to watch videos of kids playing with toys
Speaker 1: on YouTube? Kids? Is she into that? Now? Those are
Speaker 1: the worst videos. I'm starting to get on her now
Speaker 1: with just in terms of taste. I'm like, hey, you
Speaker 1: can watch something, but like watch something with a story
Speaker 1: that's good. It's like, this is garbage. I like it.
Speaker 1: I can totally see your saying just like that three
Speaker 1: defiant and happy impression. Alright. Publication bias is one we
Speaker 1: we kind of poked around it earlier a little bit
Speaker 1: with the whole publisher parish mentality. Can I add something
Speaker 1: more to that real quick before we get into publication bias,
Speaker 1: you you know, mind to what to the to just
Speaker 1: talking about publication in general. So I don't think that
Speaker 1: it's fully grasped by most people. It certainly wasn't by
Speaker 1: me until really diving into this, that the academic publishing
Speaker 1: industry has a stranglehold on science right now, in a
Speaker 1: very similar effect that twenty four hour cable news had
Speaker 1: on like journalism, to where it was like it became
Speaker 1: this voracious beast that was willing to just spit out
Speaker 1: money constantly feed it in exchange for yeah, feed it,
Speaker 1: give me more stories, give me more, give me more pundits,
Speaker 1: give me like that was the rise of pundits. Pundits
Speaker 1: didn't really exist prior to that. They just hung out
Speaker 1: on the editorial pages of newspapers. And then twenty four
Speaker 1: hour news came along, and there's not possibly enough news stories,
Speaker 1: like good news stories, to keep going for twenty four hours,
Speaker 1: so you have to talk about the news stories and
Speaker 1: analyze them, and then you start getting into who's wrong
Speaker 1: and all that stuff. The publishing industry is very much
Speaker 1: like that now, where it's this beast that must be fed,
Speaker 1: and so there's there can't possibly be that many high
Speaker 1: quality scientific papers. So scientific papers have just kind of
Speaker 1: dipped down in quality, and then um, one of the
Speaker 1: other things that the publishing industry has done is said,
Speaker 1: we really like like studies that have results. They're called
Speaker 1: positive results, where like it turned up that that you
Speaker 1: found uh correlation between something, or the compound you tried
Speaker 1: on that tumor shrunk the tumor. Like those are what
Speaker 1: we're interested in, the whole furthering of science with positive
Speaker 1: and negative outcomes. Just to say this did work, this
Speaker 1: doesn't work, don't bother trying it. We don't care about
Speaker 1: that kind of stuff. And that's a huge issue for
Speaker 1: the scientific community, Like they have to get control of
Speaker 1: the publishing community again, um if if they're going to
Speaker 1: come out from under this dark class. Yeah, I mean
Speaker 1: they found a in two thousand and ten a study
Speaker 1: about papers in social sciences especially, we're about two and
Speaker 1: a half I'm sorry, two point three times more likely
Speaker 1: to show positive results then papers in physical sciences. Even
Speaker 1: so some some bodies of research or even more apt
Speaker 1: uh to publish positive results. And that means if you're
Speaker 1: going you know this going into your profession, and you
Speaker 1: know this going into your set of research, and it's
Speaker 1: you know, that's when it becomes sort of put up
Speaker 1: or shut up time. As far as standing firm on
Speaker 1: doing good work even if it doesn't get published, right,
Speaker 1: and so that that confirmation bias can really come in
Speaker 1: where you start, hopefully inadvertently but certainly not in all cases, inadvertently,
Speaker 1: start cherry picking data to get a positive outcome where
Speaker 1: there really wasn't one there before, or you use a
Speaker 1: kind of a weird statistical method to to suss out
Speaker 1: the correlation between the variables so that you can have
Speaker 1: a positive outcome. Because if you're not publishing papers, like
Speaker 1: your academic career is not progressing and you can actually
Speaker 1: like lose jobs, so you need to be published. The
Speaker 1: publishing industry wants your paper, but they just want positive outcomes.
Speaker 1: So a high quality, well designed, well executed study that
Speaker 1: found a negative outcome to where they said, well, this
Speaker 1: compound we tried didn't actually shrink the tumor, that's that's
Speaker 1: going to be ignored in favor of a low quality
Speaker 1: paper that found some compound that shrunk a tumor just
Speaker 1: because they like positive outcomes. It's ridiculous. Yeah, And I
Speaker 1: mean that kind of goes hand in hand with the
Speaker 1: last one. You know, there's a lot of overlap with
Speaker 1: these and a lot that work sort of in concert
Speaker 1: with one another. And file draw bias is you know,
Speaker 1: it is what it sounds like. It's like you you
Speaker 1: got a negative outcome, and whether or not you were
Speaker 1: being funded by a company that definitely doesn't want that
Speaker 1: information getting out there, or if it's just as a
Speaker 1: result of it being less likely to be published because
Speaker 1: it doesn't have a positive outcome, you just stick it
Speaker 1: in the file drawer and it goes by by, right,
Speaker 1: and again, like part of the point of science and
Speaker 1: scientific publishing is to generate this body of knowledge. So
Speaker 1: if you're about to do a study, you can search
Speaker 1: and say, oh, somebody already tried the same exact thing
Speaker 1: and they found that it doesn't work. I'm gonna not
Speaker 1: try to reproduce that. I'm just gonna not go with it,
Speaker 1: um and move on to try something else. It's a
Speaker 1: huge waste of resources otherwise. And then also you could
Speaker 1: you can if you aren't publishing that kind of stuff, Um,
Speaker 1: you're missing out on well, I mean you're missing out
Speaker 1: on the real data. If the bad data is vile
Speaker 1: drawered like you're missing out on the truth. You're missing
Speaker 1: out on the whole picture, right, And also Again, it's
Speaker 1: not just that the poor negative outcomes they need to
Speaker 1: be included to. Yes, that's true, but you're also promoting
Speaker 1: positive outcome studies that actually aren't good studies. There's this
Speaker 1: thing called the proteus effect where the initial studies, these
Speaker 1: initial papers on a subject um in sevent cases, a
Speaker 1: follow up study that seeks to reproduce them can't reproduce them.
Speaker 1: They don't come to the same finding, the same conclusions,
Speaker 1: which suggests that a study was really terrible. Um, if
Speaker 1: it can't be reproduced, or if it's reproduced, somebody comes
Speaker 1: to a different finding, different conclusion, that's not a good study.
Speaker 1: So the idea of publishing positive and negative outcomes together
Speaker 1: would definitely kind of slow that whole crazy twenty four
Speaker 1: hour news cycle. Positive outcome study. I don't see how
Speaker 1: it's even legal to bury not bury, but I guess
Speaker 1: just not even just a file drawer, a study suddy
Speaker 1: that included like a drug having negative effects like and
Speaker 1: I know that Congress is stepped up to try and
Speaker 1: pass laws too. I think there was one UH in
Speaker 1: two thousand seven requiring researchers to report results of human
Speaker 1: studies of experimental treatments. Uh, and then they tried to
Speaker 1: strengthen that in sixteen. Basically this like, you know, even
Speaker 1: if your drug doesn't come to market, like, we need
Speaker 1: to have these studies and the results. Like, how, how's
Speaker 1: it even legal? It seems like you're bearing and it's
Speaker 1: almost falsification. Well, it is, for sure, because you're also
Speaker 1: like if you're if you're talking about studies where you
Speaker 1: have multiple studies on say one drug that's an antidepressant,
Speaker 1: and all you're doing is publishing the ones that have
Speaker 1: positive outcomes for that antidepressant, and you're just not publishing
Speaker 1: the ones that that showed no outcomes or maybe even harm,
Speaker 1: then yeah, that should be illegal, especially when you're talking
Speaker 1: about something like an antidepressant or in the biomedical field.
Speaker 1: But it's certainly unethical for any field of science in particular.
Speaker 1: Just bury the stuff you don't like that doesn't support
Speaker 1: your conclusion. It's a kind of a meta form of
Speaker 1: um confirmation bias, just putting aside the stuff that doesn't
Speaker 1: fit your hypothesis or your worldview, and um just promoting
Speaker 1: the stuff that does. I saw one one way around
Speaker 1: this is the Lancet, you know, the very respected medical journal.
Speaker 1: I think it's British. The Lancet um has taken to
Speaker 1: um accepting papers based on the study design and methodology
Speaker 1: and goals. So when you first plan your study and
Speaker 1: you have it all together before you ever start, that's
Speaker 1: when you would apply to have your paper studied and
Speaker 1: published in the Lancet, and that's when they decide whether
Speaker 1: it's a high quality enough study to publish or not.
Speaker 1: So then they're locked into publishing your study, whether your
Speaker 1: outcome is negative or positive, and has the knock on
Speaker 1: effect of the Lancet basically being like this is this
Speaker 1: is a trash study. We would never publish this, don't
Speaker 1: even bother. So it's saving funds and then the high
Speaker 1: quality studies are the ones that are going to get published.
Speaker 1: And then also the positive outcomes and the negative outcomes
Speaker 1: get get published regardless because they have no idea what
Speaker 1: outcome is going to be because they accept the paper
Speaker 1: before the paper, before the study has even been conducted.
Speaker 1: I saw another thing that said that uh paper would
Speaker 1: be more likely to get published in the Lancet if
Speaker 1: it had cool illustrations. That's right, That never hurts. Everybody
Speaker 1: knows that that's not unethical, especially in color it just
Speaker 1: put a few of those New Yorker cartoons forget about it.
Speaker 1: Everybody loves those. Are you got anything else, I've got
Speaker 1: nothing else. You know, this is a little soapboxy, but
Speaker 1: this is something that we believe in. It's it's kind
Speaker 1: of like our episode on the Scientific Method a little bit.
Speaker 1: Mm hmm. I like it too. Yeah, thanks for doing
Speaker 1: it with me, man, Thank you for doing it with me.
Speaker 1: Thank you for squeezing my limits. Sure. Uh, if you
Speaker 1: want to know more or about scientific bias, there's a lot, fortunately,
Speaker 1: a lot of sites and great articles dedicated um to
Speaker 1: routing that stuff out and to make you a smarter
Speaker 1: consumer of science. And so go check that out and
Speaker 1: learn more about it. And since I said learn more
Speaker 1: about it, it means it's time for a listener mail.
Speaker 1: You know. Sometimes the listener mail dovetails quite nicely with
Speaker 1: the topic, and that was the case today with our inclusion.
Speaker 1: Oh yeah, on the Media Bias List, which was pretty exciting. Yeah,
Speaker 1: what an honor. You know, there's something called the media
Speaker 1: bias Is it called the media Bias List? I believe so.
Speaker 1: And it's you know, what it does is it takes
Speaker 1: news outlets and newspapers and you know, TV and stuff
Speaker 1: like that, and they just sort of it's a big
Speaker 1: chart where they're ranked according to like how biased they are,
Speaker 1: you know, kind of up down, left and right. And
Speaker 1: they included podcasts this year they did, and we were
Speaker 1: on the US and it was really kind of cool.
Speaker 1: We had a bunch of people right in. And this
Speaker 1: is from Nicholas Bett. Oh, he said, I found this
Speaker 1: post while I was scrolling through Facebook, uh and waiting
Speaker 1: for the NFL season to start. Add fonts media? Is
Speaker 1: it fonts or fontests? We should know? I'm not sure
Speaker 1: one of the two. It's a it's a watchdog organization
Speaker 1: known for the media bias chart. Um. They do a
Speaker 1: media bias chart where they rank every news outlets political bias,
Speaker 1: and in the recent update they included you guys, and
Speaker 1: wouldn't you know it the most politically fair piece of
Speaker 1: media you can possibly consume and all of the known
Speaker 1: universes stuff you should know. You guys say you're liberal.
Speaker 1: But until I heard Chuck outright stated, I didn't even
Speaker 1: know wow that well, I think I think it slips
Speaker 1: through their son. Well, yeah, we're certainly human beings and
Speaker 1: we have their own biases, but we definitely try to
Speaker 1: keep them in check. Is we try to, And I
Speaker 1: think it's just been confirmed because they're not. Just like,
Speaker 1: listen to a couple of shows and I want to
Speaker 1: see these guys seem okay, like they really listen, then
Speaker 1: they really rank people. Um. They probably saw that too,
Speaker 1: or perhaps they listen to the North Korea episode where
Speaker 1: Josh suggested wolf Blitzer apply hot paper clips to a
Speaker 1: center thighs while writing a nice piece on Trump's Curreyan relations.
Speaker 1: Hilarious either way, Thank you guys for your fairness and
Speaker 1: hilarity of all these years. You're both the best. That
Speaker 1: is from Nicholas bad Oh. Thanks a lot of Nicholas.
Speaker 1: Thanks to everybody who wrote in to say that they
Speaker 1: saw that. We appreciate it. Uh. And it was neat
Speaker 1: to see ourselves right in the middle of the rainbow.
Speaker 1: Love being in the middle of that rainbow. I do
Speaker 1: to Chuck. It's nice and warm and cozy in there,
Speaker 1: isn't it. Yes, Well, if you want to get in
Speaker 1: touch with this, like Nicholas and the Gang did, you
Speaker 1: can send us an email to Stuff Podcast at iHeart
Speaker 1: radio dot com. Stuff you Should Know is a production
Speaker 1: of iHeart Radio. For more podcasts My heart Radio, visit
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Speaker 1: to your favorite shows. Two