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Speaker 1: Welcome to Stuff to Blow your Mind from how Stuff

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Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind.

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Speaker 1: My name is Robert Lamb, and I'm Julie Douglas. You know, Julie.

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Speaker 1: Science has done some good things for us. It's done

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Speaker 1: a lot of good things. I mean, it has really

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Speaker 1: forwarded humanity right and made us, in some ways the

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Speaker 1: kind of success story of the species that we are. Yeah,

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Speaker 1: it's kind of the skeleton of human culture, that the

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Speaker 1: thing upon which we grow and continue to grow. And

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Speaker 1: you know, and you can look at just about any

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Speaker 1: area right, medical science, exploration of inner and outer space, um,

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Speaker 1: increasing knowledge of the self, the brain, the connection, our

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Speaker 1: connection to the from the brain to the body. I mean,

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Speaker 1: pretty much everything we talk about every week is is

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Speaker 1: a testament to what science is doing and has done

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Speaker 1: for humans. And while the pursuit of science, what we

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Speaker 1: think about as science, has been around for a very

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Speaker 1: long time, this pursuit of knowledge and truth, the word

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Speaker 1: scientists is only one hundred and eighty years old. Before that,

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Speaker 1: a person might be called a natural philosopher. And before

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Speaker 1: that you had economists, you had philosophers, and what we

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Speaker 1: now call scientists. All co mingling under the same roof,

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Speaker 1: and this affected how science and and how we think

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Speaker 1: of it was defined and pursued. And we sort of

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Speaker 1: assumed that science and the scientific method were in place

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Speaker 1: from the get go, but in fact they hadn't really

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Speaker 1: been defined in the rules tightened, uh, you know, until

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Speaker 1: a couple of hundred years ago, because economists were pulling

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Speaker 1: for deductive reasoning, right, and scientists we're saying, no, I

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Speaker 1: think there's there's more of this inductive reasoning, which is

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Speaker 1: this premise that you you take an idea and then

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Speaker 1: you try to take it down to the studs and

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Speaker 1: prove it wrong, even theo you might want it to

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Speaker 1: be right. And the whole idea there is that you're

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Speaker 1: trying to get at this kind of truth. And this

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Speaker 1: is now something called the scientific method. But we sort

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Speaker 1: of take this for granted, this this fact that this

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Speaker 1: is only a fairly recent development in the long history

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Speaker 1: of humans. Yeah, I mean, it's it's basically how science works.

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Speaker 1: There were people who managed to make it work in

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Speaker 1: the past, but it was until recently that we actually

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Speaker 1: said this is what works, and this is why we

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Speaker 1: should stick to. Now. A lot of the advances in

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Speaker 1: century you can, you can boil down to a simple

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Speaker 1: idea trust, but very verify. And this plays into our

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Speaker 1: peer view system. Right, one scientist writes a paper, or

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Speaker 1: a team of scientists write a paper, maybe there's a

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Speaker 1: big breakthrough in it, maybe not. But then the idea

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Speaker 1: is that their peers come along, look at the paper

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Speaker 1: and try to replicate the results, just you know, tear

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Speaker 1: it apart, see what's happening in the paper and say, yes,

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Speaker 1: I agree this is working, or I have problems with

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Speaker 1: this or that, or this is complete bunk. Yeah. I mean,

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Speaker 1: it's this idea that science can police itself. And yet

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Speaker 1: we have some statistics coming out that point to other

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Speaker 1: factors going on and that perhaps we're not pursuing knowledge

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Speaker 1: for knowledge itself in some cases or truth and and

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Speaker 1: we'll discuss more of those factors in a bit. According

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Speaker 1: to the Economists article How Science Goes Wrong, in two

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Speaker 1: thousand twelve, biotech firm am Jin reported that they could

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Speaker 1: reproduce just six of fifty three landmark studies in cancer research,

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Speaker 1: and earlier Bear the drug company, managed to repeat just

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Speaker 1: a quarter of sixty seven similarly important papers. Now we're

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Speaker 1: not taking on this topic today because we think that

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Speaker 1: we are experts on this topic, but by no stretch

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Speaker 1: of imagination are we. But we do rely on a

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Speaker 1: lot of studies, and so we wanted to point this

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Speaker 1: out today to just for ourselves better understand what are

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Speaker 1: the conditions that lead to a good, solid study or experiment.

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Speaker 1: What are the conditions that lead to dubious data? Yeah,

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Speaker 1: certainly worth keeping in mind too when you find yourself

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Speaker 1: reading science journalism articles, you know, uh that asking yourself, well,

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Speaker 1: what is the study? You know, what are there problems

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Speaker 1: and it what could the problems be? Because of what

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Speaker 1: we'll discuss, there are a number of problems that can

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Speaker 1: and do occur in modern peer of viewed science. Now,

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Speaker 1: one of the things that will come up sometimes when

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Speaker 1: people write on this topic is careerism as one of

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Speaker 1: the factors that is problematic. And that's because we've all

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Speaker 1: heard the maximum published or parish right, and the spirit

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Speaker 1: of it is not so bad. I mean, the spirit

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Speaker 1: of it is really like less than a threat and

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Speaker 1: more like, hey, this is a challenge to push science forward.

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Speaker 1: Put forth your multiple lines of evidence, your hypotheses, your theories,

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Speaker 1: because we all want to share information. We wanted to

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Speaker 1: it apart, we want to try to validate it or

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Speaker 1: invalidate it, and generally create a better understanding of the

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Speaker 1: topic or the issue. So again it's an attempt at

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Speaker 1: reaching some sort of truth. And yet the reality of

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Speaker 1: published or perish now is more that it's this kind

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Speaker 1: of pressure to produce. So it's not enough for say

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Speaker 1: a faculty member at university to write a few really

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Speaker 1: good papers a year. Now they have this pressure to

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Speaker 1: write several And so there's this idea that questionable results

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Speaker 1: could come out of this, and instead of maybe making

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Speaker 1: it to a first tier journal, maybe that data goes

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Speaker 1: to a third tier journal. And yet it shouldn't necessarily

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Speaker 1: go any place. And the problem, as outlined in the

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Speaker 1: Economist article how Science Goes Wrong, is quote. In order

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Speaker 1: to safeguard their exclusivity, the leading journals impose high rejection

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Speaker 1: rates in excess of submitted manuscripts. The most striking findings

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Speaker 1: have the greatest chance of making it onto the page.

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Speaker 1: Little wonder that one in three researchers knows of a

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Speaker 1: colleague who has pepped up a paper by say, excluding

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Speaker 1: inconvenient data from results based on a gut feeling. So

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Speaker 1: we're talking about here is cherry picking information. And then

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Speaker 1: all of this, this kind of careerism is compounded by

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Speaker 1: the pressure to generate grant funding. So there's this idea

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Speaker 1: that more and more scientists are having a bigger percentage

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Speaker 1: of their salary covered by contingent or research funding dollars.

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Speaker 1: So that means that you now have this pressure to

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Speaker 1: keep the flow of funding going with positive results. So

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Speaker 1: you can say, yeah, see, this is exactly what I

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Speaker 1: thought was going to happen, proving out um. That shouldn't

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Speaker 1: be the case. There shouldn't be those sort of strings

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Speaker 1: tied to it, and in an ideal world that wouldn't

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Speaker 1: be the case. But that's what we're dealing with. And

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Speaker 1: then there's this failures to prove hypothesis are actually rarely

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Speaker 1: offered for publication, let alone accepted. Uh, you know, and

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Speaker 1: you can sort of squirrel away a lot of this

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Speaker 1: to you know, what scientific journal doesn't want to be

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Speaker 1: on the forefront of science, you know, full of amazing

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Speaker 1: new discoveries and and and wonderful new ideas, right that's

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Speaker 1: you know, that's really essential to the overall drive of science.

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Speaker 1: You don't want to fill your your paper with a

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Speaker 1: bunch of failures, right, But the failures are important, right.

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Speaker 1: You need to know what hasn't worked so you can

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Speaker 1: try and figure out what does work. You need to

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Speaker 1: know what's false so you can figure out what's true. Yet,

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Speaker 1: in two thousand thirteen, negative results are accounted for only

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Speaker 1: fourteen percent of published papers, and that was down from

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Speaker 1: thirty in nineteen ninety. And then in a similar vein

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Speaker 1: we see the peer of view process UM often sees

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Speaker 1: peers missing the errors in the paper. The very thing

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Speaker 1: they're supposed to do is, you know, figure out what's

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Speaker 1: what's potentially wrong with this work. So both of these

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Speaker 1: into handicap the process to a certain extent. You know,

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Speaker 1: it's interesting because my daughter's school has nine different design

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Speaker 1: principles of education, and there this is something that they

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Speaker 1: actually present to the student. So kindergarten reserve being taught

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Speaker 1: about failure and actually celebrating failure for this very reason,

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Speaker 1: because the idea, again is that you cannot have successes

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Speaker 1: without failures. And uh makes me think about Edison and

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Speaker 1: the light bulb and the hundred plus iterations of the

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Speaker 1: light bulb, all the failures that proceeded those, And yet

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Speaker 1: that's not the flashy stuff, right, that's not what necessarily

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Speaker 1: a first tier journal is going after. Like, hey, tell

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Speaker 1: me about your spectacular failure. Yeah, Like I keep thinking

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Speaker 1: about science in terms of slime mold. We did an

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Speaker 1: episode on the slime mold way back, where you would

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Speaker 1: put a slime mold in a maze, and it's solving

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Speaker 1: the maze to get to resources on the outside of

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Speaker 1: the maze. And so these tendrils of slime mold or

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Speaker 1: trailing through the mazing if they reach a dead end,

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Speaker 1: and that tendril dies and fades back and it doesn't

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Speaker 1: go down that way again. And science kind of works

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Speaker 1: the same way that you need to know which where

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Speaker 1: the dead ends are, otherwise you're just gonna keep sending

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Speaker 1: your tendils down there. Well. And then it's also this

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Speaker 1: such an elegant analogy because they're going after that sugar, right,

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Speaker 1: that that resource, and so they're eventually going to find

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Speaker 1: themselves to the success story of the resource. But then

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Speaker 1: it becomes this question of is that resource that piece

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Speaker 1: of sugar that the slime bowl is after. Is this

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Speaker 1: truth or is this money? And we'll talk a little

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Speaker 1: bit more about that later, but I thought at this

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Speaker 1: point I would go ahead and drop in a little

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Speaker 1: information about over generalization and extrapolation of results, because this

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Speaker 1: can occur in two ways. The first is applying findings

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Speaker 1: from one target group to another target group within the

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Speaker 1: same population. So an example would be you have this

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Speaker 1: new cholesterol drug and it's been tested on females age

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Speaker 1: uh it is thirty two fifty. Well, you can't make

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Speaker 1: the assumption that the drug can also do the same

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Speaker 1: thing for a different population, say women over sixty five

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Speaker 1: or men. The second fallacy is applying the survey results

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Speaker 1: to population is not living in the area in the survey.

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Speaker 1: So this is this to me was very clear cut.

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Speaker 1: Let's say that you're trying to establish the mortality rate

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Speaker 1: for a certain neighborhood within a zip code. All right,

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Speaker 1: you do the research, you do the surveying, and then

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Speaker 1: you you've got your data. Now it would be beneficial

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Speaker 1: to find out what other neighborhoods mortality rate. But you

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Speaker 1: make the assumption that just because the borders of this

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Speaker 1: other neighborhood are butting up against the one that you've

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Speaker 1: just surveyed, that they have the same mortality rate. Well,

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Speaker 1: that is erroneous thinking, because as we know and we

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Speaker 1: have seen over and over again you can have really

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Speaker 1: poor neighborhoods betting up against very prosperous ones and that

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Speaker 1: excuse the data because the very protuluh ones are going

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Speaker 1: to have a far different mortality rate than a poor neighborhood.

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Speaker 1: And yet these are some of the things that lead

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Speaker 1: can with studies and experiments. And then of course there

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Speaker 1: is conflict of interest, which is a big one. UH.

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Speaker 1: And we can date a lot of this back to

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Speaker 1: the Bio Dule Act of nineteen eighty and this came

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Speaker 1: along to encourage technology transfer from universities to industry. The

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Speaker 1: IDBA being that it would facilitate financial relationships between academic

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Speaker 1: biomedical researchers and the biotechnology industry. And you know, obviously

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Speaker 1: there's a lot of good that was going to come

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Speaker 1: out of this and has come out of this. Uh.

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Speaker 1: They leave these uh. These relationships lead to the development

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Speaker 1: of improved drugs and medical devices. UH. But on the

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Speaker 1: other hand, there's this huge financial aspect of the relationship.

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Speaker 1: Of financial relationship emerges relationships that can cause conflicts of

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Speaker 1: interest between a researchers scientific and ethical principles and that

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Speaker 1: gleam of financial gain coming background to what you said

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Speaker 1: about what is the what is the bait on the

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Speaker 1: outside of the maze? Is it? Is it knowledge and understanding?

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Speaker 1: Is it? Is it increasing our scientific understanding of a

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Speaker 1: particular ailment? Or is it mere financial gain? And of course,

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Speaker 1: financial gain for a biomedical corporation tends to boil down

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Speaker 1: to treatment, the drugs that can be thrown at a

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Speaker 1: particular ailment, the medical devices that can be thrown at

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Speaker 1: a particular ailment. And in a two thousand nine study

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Speaker 1: from Dr Rueschmajas, Assistant Professor of Radiation Oncology at the

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Speaker 1: University of Michigan Medical School, compared a thousand, five thirty

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Speaker 1: four studies involving cancer research, found that studies with with

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Speaker 1: industry funding focused on treatment again drugs, medical devices sixty

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Speaker 1: of the time compared to thirty six percent of the

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Speaker 1: time for other studies not funded by industry, and the

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Speaker 1: studies funded by industry focused on epidemiology, prevention, risk factors,

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Speaker 1: screening and other diagnostic methods only twenty percent of the

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Speaker 1: time versus forty seven for studies with no declared industry funding.

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Speaker 1: So the take home here seems to be the more

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Speaker 1: money is involved from these from the biotech industry. The

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Speaker 1: more focus there is going to be on the mere

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Speaker 1: treatment of an ailment versus uh um, you know, actually

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Speaker 1: being able to prevent it or figure out how to

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Speaker 1: screen it through looking at risk factors, which my lead

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Speaker 1: to misleading statistics or interpretation about the data. And what

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Speaker 1: I'm talking about is absolute versus relative percentages. This is

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Speaker 1: from the article bad Science, Common Problems and research Articles.

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Speaker 1: This was published on Health Readings. Quote supposed that there

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Speaker 1: was a medical problem that caused two people in one

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Speaker 1: million to have a stroke, and suppose there was a

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Speaker 1: treatment that would reduce the problem to only one person

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Speaker 1: in one million. This would be an improvement of point

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Speaker 1: zero zero zero one percent in an absolute sense, or

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Speaker 1: or as this author says, no big deal, right. However,

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Speaker 1: if it had been hoarded using relative percentages, it could

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Speaker 1: have been stated quote new medical treatment yields a rediction

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Speaker 1: and reduction and risk of stroke, and this would be

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Speaker 1: very misleading. But it's unfortunately a common practice that you

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Speaker 1: see from time to time, and so again you see

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Speaker 1: how that's it's not exactly wrong. It is a fifty

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Speaker 1: percent reduction in the two and one million people, but

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Speaker 1: it's not really accurate saying. It's just how how do

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Speaker 1: you end up using? How the effect the overall statistics

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Speaker 1: that you're dealing with. Yeah, semantics matter. Now. Another area

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Speaker 1: of concern is that of unpublished clinical trials. A two

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Speaker 1: thousand twelve study from Yale School of Medicine researchers found

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Speaker 1: that fewer than half of a sample of trials primarily

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Speaker 1: or partially funded by the National Institutes of Health were

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Speaker 1: published within thirty months of completing the clinical trial. So,

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Speaker 1: in other words, the research refindings here are not being

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Speaker 1: emanated half the time, So the scientific process is disrupted,

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Speaker 1: undermining the effort and the available material for peer of view. Now,

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Speaker 1: according to study author Dr Joseph Ross, they're probably a

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Speaker 1: number of reasons for lack of publication, such as not

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Speaker 1: getting accepted by a journal and we already hit on

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Speaker 1: the high rejection rates, or not prioritizing the dissemination of

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Speaker 1: research findings in the study. Either way, this disrupts the process.

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Speaker 1: This disrupts the strengths of the peer of view system.

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Speaker 1: Another factor is something called selective observation. Now, you've probably

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Speaker 1: experienced your own selective observation before. My example is every

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Speaker 1: time I get into the shower of my phone rings, right. Uh,

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Speaker 1: and it's a perception that is based on the annoyance

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Speaker 1: of my phone ringing and my inability to get to it.

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Speaker 1: But then I, you know, I tended to disregard all

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Speaker 1: the times that my phone didn't ring well as in

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Speaker 1: the shower, and so I was practicing confirmation biased and

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Speaker 1: oring the other data, skewing my own statistics. So selective

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Speaker 1: observation in science is essentially trying to land on a

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Speaker 1: conclusion based on an existing bias or belief. For example,

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Speaker 1: a researcher who studying obesity may have a bias that

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Speaker 1: obese people lack will power, and as a result, they

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Speaker 1: may construct an experiment that involved a plate of donuts

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Speaker 1: and a conference froom work. But if that researcher only

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Speaker 1: records data about ABS subjects and doesn't record non ABS subjects, well,

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Speaker 1: then they have a biased experiment on their hands. In

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Speaker 1: other words, Uh, if they don't go out of their

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Speaker 1: way to try to prove themselves wrong, they're not exercising

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Speaker 1: the principles of scientific method. All right, you know, let's

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Speaker 1: take a quick break and when we come back we

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Speaker 1: will discuss weird science. All right, we're back. Weird science,

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Speaker 1: weird psychology, and I'm not talking about the eighties classic

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Speaker 1: as it is. Um No, weird is a phenomenon that

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Speaker 1: plagues a lot of psychology and other social science studies.

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Speaker 1: This is when the Protestants are overwhelmingly This is where

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Speaker 1: weird comes in Western E for educated, and they're from

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Speaker 1: I for industrialized, ARE for rich, and D for democratic countries.

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Speaker 1: So weird humans are serving as the basic test subjects

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Speaker 1: in a lot of these studies. And you can also

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Speaker 1: add in that weird humans are also often college students

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Speaker 1: in the United States, participating in studies for class credit. So,

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Speaker 1: especially in the social sciences, the risk is that so

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Speaker 1: called weird populations are actually the outliers of human population

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Speaker 1: as opposed to a good standard example of human behavior.

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Speaker 1: And you know, you see this, You see shades of

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Speaker 1: this time and time again. Right. You look at a

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Speaker 1: study and it was clearly a study that was conducted

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Speaker 1: on campus with students, and in your better studies you

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Speaker 1: see them branching out from that and saying, uh um, well,

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Speaker 1: all right, when this first study we looked at students,

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Speaker 1: but then we went into an impoverished neighborhood or in

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Speaker 1: some cases, then we looked at some US participants. Then

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Speaker 1: when we also went and looked at some participants in

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Speaker 1: Hong Kong, that sort of thing. Um. And so obviously

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Speaker 1: there are a lot there's a lot to consider here

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Speaker 1: with the software of psychology, right, because there's so much

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Speaker 1: about human culture and uh and in your relations within

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Speaker 1: your particular group. But it also bleeds into the hardware

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Speaker 1: of physiology. In two thousand fourteen, Liverpool University had a

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Speaker 1: study examining rapid eye movements called cicades among groups of

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Speaker 1: mainland Chinese, British Chinese, and white British test subjects, and

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Speaker 1: he found that Chinese ethnicity was more of a factor

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Speaker 1: than culture in high cicade counts. So the mainland Chinese

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Speaker 1: groups scored high cicade numbers as did the British Chinese counterparts,

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Speaker 1: despite the many cultural differences between the two groups. So

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Speaker 1: lead author Dr Paul Knox argued, quote, the human brain

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Speaker 1: is not just amazingly complex in general, but also highly

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Speaker 1: variable across the human population. Mm hmm. And that variability

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Speaker 1: takes us to the next entry here, which is animals. Now,

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Speaker 1: we have talked about how much rodents have um contributed

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Speaker 1: to science, and they absolutely have, but we do have

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Speaker 1: problems where animal studies do not reliably predict human outcomes.

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Speaker 1: And this topic is really a complex one, but there's

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Speaker 1: a paper on the topic by Michael B. Bracken who's

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Speaker 1: from Yale University, and he writes in his paper why

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Speaker 1: animal studies are often poor predictors of human reactions to

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Speaker 1: exposure that one reason is probably because animal experiments do

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Speaker 1: not translate into replications, and human trials are into cancer

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Speaker 1: chemoprevention because as they're poorly designed, conducted, and analyzed. Now,

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Speaker 1: another possible contribution to failure to replicate the results of

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Speaker 1: animal research and humans is that reviews and summaries of

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Speaker 1: evidence from animal research are inadequate when it comes to methodology,

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Speaker 1: and one survey, only one in ten thousand Meadline records

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Speaker 1: of animal studies were tagged as being meta analysis, is

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Speaker 1: compared to one and one thousand human studies, and in

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Speaker 1: recent reports, the poor quality of research was documented by

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Speaker 1: a comprehensive search of Medline, which found only twenty five

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Speaker 1: systematic reviews of animal research. Other studies similarly found only

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Speaker 1: thirty and fifty seven systematic reviews of any type of

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Speaker 1: animal researcher so Um. The reason that Bracken points us

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Speaker 1: out is because he says these kind of deficiencies are

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Speaker 1: important because animal research often provides the rationale for hypotheses

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Speaker 1: studied by epidemiologists and clinical researchers. Moreover, if you look

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Speaker 1: at the genetics of this, it gets even more muddled.

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Speaker 1: And the reason for that is because with rodents, and

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Speaker 1: one of the reasons why we use them is because

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Speaker 1: we can change their genetic background within a couple of generations.

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Speaker 1: We can tinker with the genes. And that's great because

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Speaker 1: that can really help us to study certain conditions. However, Um,

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Speaker 1: those rodents would yield really consistent results and disease expression.

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Speaker 1: But humans, we are far more wild West when it

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Speaker 1: comes to genetics and the genetic background, and that would

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Speaker 1: factor in how the human disease is expressed, and this

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Speaker 1: would yield a mismatching results between humans and animals. It's

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Speaker 1: a layer cake of animal confusion. Indeed, it is um. Now,

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Speaker 1: on top of everything we've discussed here, there are plenty

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Speaker 1: of additional methodological pitfalls, and we're we're gonna include a

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Speaker 1: link on the landing page of this episode to a

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Speaker 1: fabulous page that has a list of about sixty of them,

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Speaker 1: and we're not going to go into all into detail

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Speaker 1: on all of them here, but just to give you

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Speaker 1: an example, this includes the likes of the cebo effect,

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Speaker 1: which we've discussed at length before, and in which the

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Speaker 1: the individual receiving the sugar pill ends up actually getting

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Speaker 1: some sort of biological benefit from from the medication or

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Speaker 1: the fake medication, uh carry over effect, where the results

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Speaker 1: of one study are are observed in a secondary study

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Speaker 1: without realizing it. And then magnitude blindness, the tendency to

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Speaker 1: become preoccupied with statistically significant results that never nevertheless have

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Speaker 1: a small magnitude on effect. I feel like that comes

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Speaker 1: into play a lot when I look at, um, some

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Speaker 1: of this stuff that's new and that's being reported in

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Speaker 1: the media. It's very exciting, right, you know, oh wow,

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Speaker 1: look at this insight, and then when you get into

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Speaker 1: the specifics of the study, it's just it's not that significant, right,

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Speaker 1: doesn't quite match up to that snappy headline. All right,

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Speaker 1: So how does science correct course? What can be done

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Speaker 1: about these problems we've discussed? Well, um, just to talk

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Speaker 1: briefly about the use of statistics and managing potential conflicts,

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Speaker 1: those financial conflicts we we mentioned earlier, conflicts of interest. Um.

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Speaker 1: The general idea that the expert put put forth is

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Speaker 1: that we need to simplify, standardized and better enforced policies

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Speaker 1: to manage financial conflicts of interest, and that science needs

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Speaker 1: to keep a better eye on statistics, by which we mean,

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Speaker 1: of course, the statistical validity and the statistical errors inherent

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Speaker 1: in the system. Another thing is to encourage replication. And

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Speaker 1: again this is from the Economist article quote. Some government

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Speaker 1: funding agencies, including America's National Institutes of Health, which dish

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Speaker 1: out thirty billion on research each year, are working out

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Speaker 1: how to best encourage replication and growing numbers of scientists,

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Speaker 1: especially young ones, understand statistics. Another area is allocating space

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Speaker 1: and journals were uninteresting studies, which which is which is

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Speaker 1: crazy because if you think about it in terms of, say, um,

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Speaker 1: you know, a literary fiction publication, you would never in

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Speaker 1: a million years have anyone suggests, hey, we should make

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Speaker 1: room in this, uh this review for bad fiction. You

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Speaker 1: know a certain amount that we're always just gonna include

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Speaker 1: bad fiction. But the idea here is that scientific journals

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Speaker 1: should allocate space for the less jazzy, the less sexy stuff,

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Speaker 1: because that too is essential. Now I'm wishing for a

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Speaker 1: journal called the humdrum Studies Journal, uninteresting Studies Journal. Now,

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Speaker 1: another solution would be to tighten peer reviews, so perhaps

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Speaker 1: dispensing with it altogether. And again that's from the Economist article.

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Speaker 1: And so if you dispense with it altogether, what would

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Speaker 1: do well? You would have post publication evaluation in the

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Speaker 1: form of appended comments. And they say that that system

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Speaker 1: has worked well in recent years in physics and mathematics.

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Speaker 1: And lastly, policymakers should ensure the institutions using public money

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Speaker 1: also aspect the rules. So picking up again to the

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Speaker 1: potholes that we had mentioned, one of them is also

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Speaker 1: skills neglect, and this is that human disposition to resist

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Speaker 1: learning new scholarly methods that may be pertinent to a

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Speaker 1: research problem. And so that would also factor into peer review.

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Speaker 1: Is just making sure that while you're reviewing something else,

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Speaker 1: but your own knowledge of the topic is up to snuff.

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Speaker 1: And finally, when it comes to weird populations, I mean,

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Speaker 1: the big thing is just to be aware of it

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Speaker 1: too when you're when you're sampling, when you're using samples

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Speaker 1: from the immediate collegiate environment to be aware of it

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Speaker 1: and maybe be less cavalier about uh saying that you

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Speaker 1: have identified something that is in you know, basic in

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Speaker 1: general human nature. Of course, we should end this episode

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Speaker 1: with the study of all studies, which is that there

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Speaker 1: are too many studies. Yes, this was I believe that

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Speaker 1: the time it was attention decay in science, um, which

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Speaker 1: is snazzy. UM. And it basically just comes down to

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00:26:06,680 --> 00:26:08,560
Speaker 1: the fact that there are just so many studies coming

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00:26:08,560 --> 00:26:11,680
Speaker 1: out now in so many journals. They've just exploded since

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00:26:11,760 --> 00:26:16,000
Speaker 1: the earlier days um, in the twentieth century. Yeah, and

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00:26:16,040 --> 00:26:18,600
Speaker 1: it's hard for everyone to keep up with the studies,

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00:26:18,640 --> 00:26:21,359
Speaker 1: and also the older studies are getting lost in the

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00:26:21,359 --> 00:26:24,359
Speaker 1: fray of new studies. So um. Of course you know

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00:26:24,400 --> 00:26:28,719
Speaker 1: that building upon knowledge is really important in this discovery

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00:26:28,760 --> 00:26:31,959
Speaker 1: of truth. Right, And it's fair to point out that

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00:26:32,000 --> 00:26:37,400
Speaker 1: this paper should awesome also be analyzed, um because it's

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00:26:37,440 --> 00:26:40,919
Speaker 1: just one single study and the researchers mainly looked at

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00:26:41,040 --> 00:26:44,680
Speaker 1: very broad fields like chemistry and medicine. Indeed, trust but

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00:26:44,920 --> 00:26:50,679
Speaker 1: verify right, it all comes back. So so again, this

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00:26:50,920 --> 00:26:54,439
Speaker 1: episode wasn't It's not about you know, doubt everything, doubt

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00:26:54,440 --> 00:26:57,480
Speaker 1: every study, that comes out, doubt every the bit of

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00:26:57,640 --> 00:27:01,440
Speaker 1: science journalism that comes across your desk, But it's it's all.

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00:27:02,560 --> 00:27:05,080
Speaker 1: It's all information that's worth keeping in mind when you

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00:27:05,160 --> 00:27:08,560
Speaker 1: do engage with these studies. Uh, and something that you

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00:27:08,600 --> 00:27:10,679
Speaker 1: know that we like to keep in mind, you know,

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00:27:10,680 --> 00:27:13,000
Speaker 1: when we look at these studies in our research. Yeah,

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00:27:13,040 --> 00:27:15,600
Speaker 1: and we thought that this was pertinent information, especially when

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00:27:15,640 --> 00:27:18,880
Speaker 1: you consider how much data we are taking in every

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00:27:18,880 --> 00:27:22,280
Speaker 1: single day and all of the headlines that are connected

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00:27:22,320 --> 00:27:24,520
Speaker 1: to these studies and where they're coming from and how

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00:27:24,520 --> 00:27:28,679
Speaker 1: they're being pursed out. Indeed, Hey, in the meantime, if

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00:27:28,680 --> 00:27:30,920
Speaker 1: you want to check out more episodes of Stuff to

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00:27:30,920 --> 00:27:34,639
Speaker 1: Blow your Mind, most of which involve scientific studies of

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00:27:34,680 --> 00:27:37,000
Speaker 1: one type or another, you can head on over to

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00:27:37,000 --> 00:27:39,280
Speaker 1: stuff to Blow your Mind dot com, where you will

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00:27:39,320 --> 00:27:42,640
Speaker 1: find all those podcast episodes, all those videos, all those

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00:27:42,640 --> 00:27:45,199
Speaker 1: blog posts, you name it. And we know some of

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00:27:45,240 --> 00:27:47,399
Speaker 1: you are out there toiling away in the fields and

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00:27:47,400 --> 00:27:51,520
Speaker 1: the labs, scientific researchers. Do you have thoughts about this?

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00:27:52,320 --> 00:27:53,879
Speaker 1: If so, we would love to hear from you, and

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00:27:53,920 --> 00:27:55,800
Speaker 1: you can email us at blow the Mind at house

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00:27:55,840 --> 00:28:01,680
Speaker 1: to courts dot com for more on this and thousands

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00:28:01,680 --> 00:28:10,000
Speaker 1: of other topics, visit how stuff works dot com.