"Replication crisis" in science

OleBoiii

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I know that Veritasium has been criticized for his sponsored videos recently, but this is a good watch:

 
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Revan

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Definitely true in the field I work (Machine Learning / Computer Vision).

A lot of papers don't have code at all, which makes them non-reproducible by default. Many others have code, but good luck getting the results they claim in them. And then from the remaining, many are reproducible, but the results are not because of the main idea of the paper, but because of a lot of engineering to push the results past state-of-the-art. I know that I have done this for a couple of my papers, cause if not state-of-the-art means reject, and everybody is doing so, which makes it the only way to actually get your idea published. I think this is a syndrome of a broken (to some degree system), people are incentivised to publish as much as they can, not necessarily to really push the science forward. Postdoc positions and tenure-track positions are primarily based on the number of top-venue papers, as are the offers from the big tech companies. Getting meaningful research is a distant second.

Nevertheless, I think that the field has progressed massively because when there are so many top-venue papers each year, some of them are meant to be good. After all, the standards to publish in top venues are very high, and even if 99% of them in grand scheme of things don't do anything, the 1% is gonna push the science forward. There has also been a push to at least publish the code, which makes reproducibility better, and cheating in results more difficult (hard to cheat in results where your code is online). Nowadays, around 80% of top-venue papers have code online, just a few years ago, most of them didn't.

Saying that despite that I think this is a big problem in my field, it is far better than in the others. In medicine or social studies, some of the experiments are completely not reproducible. The venues not being double-blinded also means that at least some of the decision is based on the author's reputation, not on the science itself. With the results not being properly reproducible, it also gets easy to cheat. And at least in neuroscience (I also work a bit in the intersection between ML and neuroscience) the quality of some top-venue papers I read is completely appalling, Nature papers that I would have not accepted as Master thesis.

It is much better in theoretical fields though. For example, in rare occasions I check theoretical physics papers, they are top-notch (as far as I can understand read them). But then, I guess by definition, they cannot have reproducibility issues.

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TLDR: Most of the research there, despite being peer-reviewed and top-tier venue (in general, every field has just a few journals/conferences who are top-tier, the rest is somewhere between junk and not worth it to read), most of it is probably non-reproducible and generally useless.
 

VorZakone

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Definitely true in the field I work (Machine Learning / Computer Vision).

A lot of papers don't have code at all, which makes them non-reproducible by default. Many others have code, but good luck getting the results they claim in them. And then from the remaining, many are reproducible, but the results are not because of the main idea of the paper, but because of a lot of engineering to push the results past state-of-the-art. I know that I have done this for a couple of my papers, cause if not state-of-the-art means reject, and everybody is doing so, which makes it the only way to actually get your idea published. I think this is a syndrome of a broken (to some degree system), people are incentivised to publish as much as they can, not necessarily to really push the science forward. Postdoc positions and tenure-track positions are primarily based on the number of top-venue papers, as are the offers from the big tech companies. Getting meaningful research is a distant second.

Nevertheless, I think that the field has progressed massively because when there are so many top-venue papers each year, some of them are meant to be good. After all, the standards to publish in top venues are very high, and even if 99% of them in grand scheme of things don't do anything, the 1% is gonna push the science forward. There has also been a push to at least publish the code, which makes reproducibility better, and cheating in results more difficult (hard to cheat in results where your code is online). Nowadays, around 80% of top-venue papers have code online, just a few years ago, most of them didn't.

Saying that despite that I think this is a big problem in my field, it is far better than in the others. In medicine or social studies, some of the experiments are completely not reproducible. The venues not being double-blinded also means that at least some of the decision is based on the author's reputation, not on the science itself. With the results not being properly reproducible, it also gets easy to cheat. And at least in neuroscience (I also work a bit in the intersection between ML and neuroscience) the quality of some top-venue papers I read is completely appalling, Nature papers that I would have not accepted as Master thesis.

It is much better in theoretical fields though. For example, in rare occasions I check theoretical physics papers, they are top-notch (as far as I can understand read them). But then, I guess by definition, they cannot have reproducibility issues.

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TLDR: Most of the research there, despite being peer-reviewed and top-tier venue (in general, every field has just a few journals/conferences who are top-tier, the rest is somewhere between junk and not worth it to read), most of it is probably non-reproducible and generally useless.
Why are the incentives the way they are? Why value quantity over quality?
 

Revan

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I know that Veritasium has been criticized for his sponsored videos recently, but he explains the replication crisis well here:

Why is he getting criticised? He is absolutely brilliant, one of my favorites. And despite that he is a physicist, he seems to have a wonderful knowledge about most natural sciences and makes awesome videos in completely different topics.
 

OleBoiii

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Why is he getting criticised? He is absolutely brilliant, one of my favorites. And despite that he is a physicist, he seems to have a wonderful knowledge about most natural sciences and makes awesome videos in completely different topics.
I don't want to derail the thread, so I'll spoiler it:

This is basically where it started:


TLDR; his video about self-driving cars is poorly researched and comes across as a long commercial for the company in question.

I also like Veritasium a lot, but I also agree that the video on self-driving cars is a bad look.
 

Revan

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Why are the incentives the way they are? Why value quantity over quality?
Talking exclusively for my field (but it is likely similar for many others):

- As an undergraduate student: you need 1-2 first author top-venue papers to go to a top university (MIT, CMU, Stanford, Berkeley, Toronto etc) for PhD. For second-tier ones, you can get away without that, if you have done a very good thesis within that group, but gets harder if you want to go to some other unis (for example, going from ETH to Cambridge).

- As a PhD student: you need papers in top-venue to have a realistic chance of getting internships in FAANG companies.

- As a postdoc: you need multiple top-venue papers to get a position in any of the top 20-50 or so universities. Also, if you then want to go to FAANG, you need several papers.

- As a tenure-track professor: you need a shitload of top-venue papers to be able to get such a position, in case of the very top unis, probably 20 or so. And then when you get that position, you need a shitload more to get tenure. For example, I just checked my supervisor's papers, and she has over 30 top-venue papers but still she has not received tenure in a top (but not MIT/Stanford-top) university.

- As a professor: the more top-tier papers you have, the more chances you have to get grants. For example, in ERC start grant (starting grant of the elite ERC grants), professors can apply only within 7 years of their PhD. In general, they have somewhere between 20-40 top-tier papers, which is kinda crazy cause we are talking about young people at the beginning of their careers.

- As senior professors: reputation, more funding, organizing conferences, being editor in chief of top journals etc.

Why value quantity over quality?
Because getting extremely qualitative research is incredibly hard. Also, because most of the research is done by PhD students, who typically need 3 top tier first author papers to get their PhD. And there are thousands of them.

Don't get me wrong, it is not that the research is shit. After all, it is still interesting, and very professional. But in the grand scheme of things, it does not mean anything. There are a few papers who study similar idea and get roughly the same results. There is too much incremental research. In general, there are probably 10 or so papers for each year who really push the field forward, the others just make tiny incremental research, to the point that if you delete them from existence, nothing happens. But then, maybe without having this massive number of them, you won't get the top 5 or 10 who really make a massive impact. And maybe a lot of the engineering papers (who extend an idea in an incremental way using some engineering tricks) actually improve things, which in turn bring more success, funding etc, which in turn increase the chances of one of those stellar papers happening.
 

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When working in biology I was always amazed that the generally very high quality of biological papers (including containing enough information the methods section to be repeatable) wasn't always the norm elsewhere. Medical research can be very bad for this from what I can tell with confounding, lack of proper control and small sample sizes being frequent issues. Part of this often isn't avoidable due to ethical considerations but some is just sloppy science.
 

Revan

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I don't want to derail the thread, so I'll spoiler it:

This is basically where it started:


TLDR; his video about self-driving cars is poorly researched and comes across as a long commercial for the company in question.

I also like Veritasium a lot, but I also agree that the video on self-driving cars is a bad look.
Thanks. I haven't watched that video, cause usually I tend to watch the science popularizers in fields I am not working on.

I found some of his math/physics videos really inspiring, and also his video in longevity with David Sinclair was great.
 

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Talking exclusively for my field (but it is likely similar for many others):

- As an undergraduate student: you need 1-2 first author top-venue papers to go to a top university (MIT, CMU, Stanford, Berkeley, Toronto etc) for PhD. For second-tier ones, you can get away without that, if you have done a very good thesis within that group, but gets harder if you want to go to some other unis (for example, going from ETZ to Oxford).

- As a PhD student: you need papers in top-venue to have a realistic chance of getting internships in FAANG companies.

- As a postdoc: you need multiple top-venue papers to get a position in any of the top 20-50 or so universities. Also, if you then want to go to FAANG, you need several papers.

- As a tenure-track professor: you need a shitload of top-venue papers to be able to get such a position, in case of the very top unis, probably 20 or so. And then when you get that position, you need a shitload more to get tenure. For example, I just checked my supervisor's papers, and she has over 30 top-venue papers but still she has not received tenure in a top (but not MIT/Stanford-top) university.

- As a professor: the more top-tier papers you have, the more chances you have to get grants. For example, in ERC start grant (starting grant of the elite ERC grants), professors can apply only within 7 years of their PhD. In general, they have somewhere between 20-40 top-tier papers, which is kinda crazy cause we are talking about young people at the beginning of their careers.

- As senior professors: reputation, more funding, organizing conferences, being editor in chief of top journals etc.



Because getting extremely qualitative research is incredibly hard. Also, because most of the research is done by PhD students, who typically need 3 top tier first author papers to get their PhD. And there are thousands of them.

Don't get me wrong, it is not that the research is shit. After all, it is still interesting, and very professional. But in the grand scheme of things, it does not mean anything. There are a few papers who study similar idea and get roughly the same results. There is too much incremental research. In general, there are probably 10 or so papers for each year who really push the field forward, the others just make tiny incremental research, to the point that if you delete them from existence, nothing happens. But then, maybe without having this massive number of them, you won't get the top 5 or 10 who really make a massive impact. And maybe a lot of the engineering papers (who extend an idea in an incremental way using some engineering tricks) actually improve things, which in turn bring more success, funding etc, which in turn increase the chances of one of those stellar papers happening.
Publish or perish. Why I got out of academia. I saw the way my friends and colleagues lived and it never ever ends until you retire or leave.
 

berbatrick

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Anecdote from my project:
The person working on my project before me failed to reproduce a published finding. She tried over a dozen times. Her attempts are listed in her thesis, but not in a published paper.

With access to better technology, I can question that published finding in other ways (not directly contradicting it, but suggesting alternative explanations for what they saw). Another, unrelated lab, has also cast doubt on their interpretations. Both of our doubts will be published as small paragraphs within our papers, but we're not directly replying to the original work.

But even though we're all skeptical of that paper's results and especially its conclusions, we don't think it's fudged. May be some luck which isn't getting repeated. May be carelessness. (In my opinion) something funny with their controls. May be different reagents, methods, moon signs... who the feck knows. Definitely over-interpretation in their conclusions, which should have been caught by reviewers. But for the result itself, nobody knows.

Apart from my project, I know 1-2 more findings from within our lab, that we've had trouble repeating, from student to student. Current student thinks previous one was careless, etc. If it isn't published yet, it's going to take years to sort out. Maybe it will never go forward. Biological systems can be stupid like that.


My lab is molecular bio/genetics, which should be a harder, more reproducible field that anything to do with psych, which is the focus of the wikipedia article. It's also very far from medical or engineering applications, which takes away big money, attention, and some of the pressure to have "good" results.
In general, I'm confident that our expensive experiments will be reproducible. We use 3+ replicates and are pretty strict with what we drop between replicates. Strain re-verification (which destroyed a year of my work and 1/3rd of the thesis). My prof is a bit of a lunatic in terms of code and data transparency, so in theory anybody could check all our analysis. (In practice, as a reviewer of others' work, no way.) Apart from the example in the spoiler, we haven't seen cases where we're noticing the opposite of what other labs are seeing.

But there's published work from respected labs in our field using 1-2 replicates that maybe has a question mark over it. Clinical work - much more important than ours!- could mean less than 10 mice.


Also disagree strongly with @Revan on journals. At least in my field. The elite journals (C, N, S) simply will not take any of the work I or most people I know do, not because it's shoddy, but because it isn't exciting enough or broadly relevant - rejected before review. In fact Science seems more like a glossy 3-page write-up of the sexiest findings rather than a useful scientific paper for someone who wants to repeat/build on what has been published.
On the other hand, sure, there are dozens of shoddy journals, those are easier to identify rather than restricting it to top 3.
 

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First off Revan, great and interesting post. Second, you're right, it is known to be poor in social sciences, a well known problem in academia that there aren't enough follow up studies, especially for qualitative projects.

I think it's especially important in this field too because being a more human-oriented it's prone to more possible biases. You'd ideally like any scientific method to be held to scrutiny as much as possible and for results to be contrasted to find a truth in-between, so to speak. Anything else runs the risk of being dogmatic.

Edit - I'm not in academia. This mostly came from conversations I had with lecturers during my master's.
 

Flying high

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Why are the incentives the way they are? Why value quantity over quality?
It sounds like yet another problem which capitalism exacerbates and is unfit to resolve.

I'm sure collaboration is good in some areas of research(space, for example?), but with so much money at stake it seems to me that any potential gains made from competition, are likely to be outweighed by excessive duplication of effort from some of the smartest scientific minds.
 

OleBoiii

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Publish or perish.
This is why business majors shouldn't be put in charge. I'm pretty sure that all academics know that it shouldn't be this way, but at the end of the day money talks.
 

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This is why business majors shouldn't be put in charge. I'm pretty sure that all academics know that it shouldn't be this way, but at the end of the day money talks.
Part of the problem is that promotion/tenure in academia is usually tied to your publication record so you have little choice. Some Universities are now creating both research and teaching pathways for academics but it is still reasonably rare.
 

berbatrick

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Even aside from capitalism, there is the question of time vs resources.

Say 0.2% of your taxes were going to fund basic research. Someone wants to find out why some plants in mangroves on some island have longer roots than other plants in the same mangrove on that same island. Would you rather it go into proving/disproving what X said about this 20 years ago, or a new approach to the problem that starts with X's results and promises a clearer answer?
Or would you be willing to spend 0.4%, so that all such (seemingly useless, possible dead-end) questions are answered with both approaches?
 

OleBoiii

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Part of the problem is that promotion/tenure in academia is usually tied to your publication record so you have little choice. Some Universities are now creating both research and teaching pathways for academics but it is still reasonably rare.
That sounds really stupid.

I know that some insane American companies monitor the amount of keyboard clicks and lines of code you write as if that is what good programming is all about. Looking at the quantity of publications reminds me of that.
 

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There's a lot of focus on psychology on this topic, but I think the evidence for that is pretty shoddy. I think it's mostly a consequence of it being the origin, combined with the popular narrative about "soft" sciences. The reason the replication crisis started in psych is because psychologists were the ones to really look into it, and that should be a point in favour rather than a black mark.

I'd liken it a bit to the perception of professional cycling being a sport with a uniquely high amount of cheaters. No doubt doping is rife in the peloton, and that's a real problem if the goal is a clean sport, but one of the reasons a comparatively high amount of people test positive is that cycling is one of the sports with the most rigorous testing. In a lot of sports, especially including the big money sports, the protocols are frankly pathetic so of course people rarely get caught.
 

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It's interesting what you lot are saying about loads of academics starting to become really switched on about publishing their codes now. The first person to build the AI that pulls in all these diverse research codes and goes Skynet on them will be the progenitor of true AI :cool:
 

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It certainly happens more often than you'd like in my field (Nanomaterials/Material Chemistry). From my experiences as a Ph.D. and Post-Doc, research areas are extremely monopolized and it becomes difficult to switch for a research group into a new area in which they don't have prior publications. To get into the top journals, you need a big name fellow in the field as a co-author and they are not too pleased if you have results that might contradict their existing publications.

The dependency on models, especially in physical sciences, is quite frightening in the sense that scientists only want to publish results that reinforce their existing models, even if the empirical data disproves it. The issue of research grants is also highly politicized in the sense that it's the better presentation and potential commercial value (no matter how unrealistic) that is likely to get funding as opposed to research proposals that aim to actually tackle the why and the hows.

Working in scientific research over the past few years has certainly been a disillusioning experience for me, and I'm switching over to sales and market development soon, and frankly, I can't wait to move away from this world.
 

Revan

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It certainly happens more often than you'd like in my field (Nanomaterials/Material Chemistry). From my experiences as a Ph.D. and Post-Doc, research areas are extremely monopolized and it becomes difficult to switch for a research group into a new area in which they don't have prior publications. To get into the top journals, you need a big name fellow in the field as a co-author and they are not too pleased if you have results that might contradict their existing publications.

The dependency on models, especially in physical sciences, is quite frightening in the sense that scientists only want to publish results that reinforce their existing models, even if the empirical data disproves it. The issue of research grants is also highly politicized in the sense that it's the better presentation and potential commercial value (no matter how unrealistic) that is likely to get funding as opposed to research proposals that aim to actually tackle the why and the hows.

Working in scientific research over the past few years has certainly been a disillusioning experience for me, and I'm switching over to sales and market development soon, and frankly, I can't wait to move away from this world.
I think it is absurd that paper reviewing is not done in a double-blinded system. I am happy that in my field, the top venues* are all double-blinded (the reviewers and area chairs/associate editors don't know the authors and vice versa).
 

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There's a lot of focus on psychology on this topic, but I think the evidence for that is pretty shoddy. I think it's mostly a consequence of it being the origin, combined with the popular narrative about "soft" sciences. The reason the replication crisis started in psych is because psychologists were the ones to really look into it, and that should be a point in favour rather than a black mark.

I'd liken it a bit to the perception of professional cycling being a sport with a uniquely high amount of cheaters. No doubt doping is rife in the peloton, and that's a real problem if the goal is a clean sport, but one of the reasons a comparatively high amount of people test positive is that cycling is one of the sports with the most rigorous testing. In a lot of sports, especially including the big money sports, the protocols are frankly pathetic so of course people rarely get caught.
I find the WEIRD issue tends to come up more often. Basically how most research is conducted using white, educated, heterosexual, non-neurodiverse participants, and how those findings are then applied to different groups.

https://www.psychologytoday.com/gb/blog/non-weird-science/202004/psychologys-weird-problem

It's related to the replication crisis but, you know, slightly different.
 
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There is a lot of work underway among research funders to battle this issue. They have huge influence, as a lot of research depends on their funding, so the conditions they attach to grants can really change things - just like there already is a big and growing push from funders towards open science (i.e., publishing articles in a that they are openly accessible, and not behind a paywall). Something like requiring data and code to be included with all grant-funded publications will likely soon be common practice - if it isn't already. (I worked for a science funder, but I wasn't in this area of the organization.) That and other measures should help a lot.
Publish or perish. Why I got out of academia. I saw the way my friends and colleagues lived and it never ever ends until you retire or leave.
Exactly, it's a big issue - to the point where there is fraud happening just for people to be able to produce papers that will improve their job prospects. This stuff is particularly egrarious:
Scammers impersonate guest editors to get sham papers published
Hundreds of junk-science papers have been retracted from reputable journals after fraudsters used ‘special issues’ to manipulate the publication process. And the problem is growing.
Full article: https://www.nature.com/articles/d41586-021-03035-y?WT.ec_id=NATURE-20211118

As a consequence, institutions are currently reconsidering how to evaluate their researchers. Utrecht University in the Netherlands has recently made a big step in this process. It's not perfect yet, but likely the future will look something like this across the board:
Impact factor abandoned by Dutch university in hiring and promotion decisions
Faculty and staff members at Utrecht University will be evaluated by their commitment to open science.
Full article:https://www.nature.com/articles/d41586-021-01759-5
 

King7Eric

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I think it is absurd that paper reviewing is not done in a double-blinded system. I am happy that in my field, the top venues* are all double-blinded (the reviewers and area chairs/associate editors don't know the authors and vice versa).
There are so many absurd things with the entire system. I have worked as a researcher in 4 separate countries and the core issues remain the same everywhere. The absolute power that tenured professors have is gonna lead to a massive crisis at the miro and macro-level over the next few decades in scientific research. Thankfully, I start my new job in January, so i am done with this shite. :lol:
 

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I find the WEIRD issue tends to come up more often in psychology circles. Basically how most research is conducted using white, education, heterosexual, non-neurodiverse participants, and how the findings are often applied to other groups.

https://www.psychologytoday.com/gb/blog/non-weird-science/202004/psychologys-weird-problem

It's related but slightly different.
That's because test subjects are often university students or people who are otherwise more in touch with the researchers conducting the study. That's a reproductivity issue to some extent, as people working with a different demographic might have trouble reproducing the findings even if the original study was well conducted otherwise; but you will never be able to deal with that entirely. I mean, you can assemble the most diverse set of Norwegians possible for your study, but that might still not mean much for people in Chile. You can't get rid of that entirely, you will basically always need complementing studies to see how a certain effect plays out in different parts of the world.

To get back to @NotThatSoph's point, I think another issue with psychology's image is that a couple of high-profile fraud cases have come from there (like Diederik Stapel at the University of Tilburg). Still, wouldn't you think that cause and effect are harder to pintpoint in psychology than in some of the STEM sciences? Although that shouldn't really affect reproducibility, it should just mean that statistical standards are adjusted accordingly (which is often the real issue with psychological research: statistically unsound methods).
 

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That's because test subjects are often university students or people who are otherwise more in touch with the researchers conducting the study. That's a reproductivity issue to some extent, as people working with a different demographic might have trouble reproducing the findings even if the original study was well conducted otherwise; but you will never be able to deal with that entirely. I mean, you can assemble the most diverse set of Norwegians possible for your study, but that might still not mean much for people in Chile. You can't get rid of that entirely, you will basically always need complementing studies to see how a certain effect plays out in different parts of the world.

To get back to @NotThatSoph's point, I think another issue with psychology's image is that a couple of high-profile fraud cases have come from there (like Diederik Stapel at the University of Tilburg). Still, wouldn't you think that cause and effect are harder to pintpoint in psychology than in some of the STEM sciences? Although that shouldn't really affect reproducibility, it should just mean that statistical standards are adjusted accordingly (which is often the real issue with psychological research: statistically unsound methods).
For sure it's a flaw and I suppose any findings should be taken with a pinch of salt as exactitude is always going to be a problem with any study involving people/ human behaviour. In recent years people have often used that as a stick to beat social sciences but if you frame it differently, you're basically reaching a conclusion until a point where a better conclusion can be drawn, which might include using better, more exact methods. That's no issue in my eyes except if the researchers/ organisation has the sense to point out such flaws. Edit - I'm basically outlining why the replication crisis exists in a roundabout way :lol:
 

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For sure it's a flaw and I suppose any findings should be taken with a pinch of salt as exactitude is always going to be a problem with any study involving people/ human behaviour. In recent years people have often used that as a stick to beat social sciences but if you frame it differently, you're basically reaching a conclusion until a point where a better conclusion can be drawn, which might include using better, more exact methods. That's no issue in my eyes except if the researchers/ organisation has the sense to point out such flaws. Edit - I'm basically outlining why the replication crisis exists in a roundabout way :lol:
Actually, I think you're lining out the scientific process - Popper's principle of falsificiation. (If it's impossible to falsify research findings, then it's probably not proper or not properly conducted science.)

I anyway don't see the problem with what you're describing. Going back to my example, say the Norwegian researchers find something very interesting using their very diverse set of Norwegians, and the Chilean researchers can't replicate that with a very diverse set of Chileans. Now we have cause for follow-up research. Let's redo it with a different group of Norwegians. Still the same result? So what's the difference between these Norwegians and these Chileans? And so on. Cause as long as the original study was transparent and statistically sound, this is not an issue of reproducibility, but a question of context, and actually an opportunity to deepen the findings.
 

Vidyoyo

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Actually, I think you're lining out the scientific process - Popper's principle of falsificiation. (If it's impossible to falsify research findings, then it's probably not proper or not properly conducted science.)

I anyway don't see the problem with what you're describing. Going back to my example, say the Norwegian researchers find something very interesting using their very diverse set of Norwegians, and the Chilean researchers can't replicate that with a very diverse set of Chileans. Now we have cause for follow-up research. Let's redo it with a different group of Norwegians. Still the same result? So what's the difference between these Norwegians and these Chileans? And so on. Cause as long as the original study was transparent and statistically sound, this is not an issue of reproducibility, but a question of context, and actually an opportunity to deepen the findings.
Yep agree that's the ideal. Obviously it's dependent on whether they think the study is worth replicating but that might be nit-picking...

Probably is Popper related! The idea came from a podcast series I've been listening to recently and an episode last night was about post-enlightenment thinking, about how the scientific method needs to account for limits of human observation.

https://www.philosophizethis.org/podcast/consequences-of-reason

Not saying you should listen but I really like the show :)
 

Cheimoon

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Yep agree that's the ideal. Obviously it's dependent on whether they think the study is worth replicating but that might be nit-picking...

Probably is Popper related! The idea came from a podcast series I've been listening to recently and an episode last night was about post-enlightenment thinking, about how the scientific method needs to account for limits of human observation.

https://www.philosophizethis.org/podcast/consequences-of-reason

Not saying you should listen but I really like the show :)
Thanks - I'll check it out!

My background is in the humanities, not STEM or social sciences, but theory was important to my work and I have always been very interested in the scientific process and in the history and philosophy of science. So I'm fairly well informed (for a lay person) on those subjects - and always interested in learning more.
 

Vitro

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There's a lot of focus on psychology on this topic, but I think the evidence for that is pretty shoddy. I think it's mostly a consequence of it being the origin, combined with the popular narrative about "soft" sciences. The reason the replication crisis started in psych is because psychologists were the ones to really look into it, and that should be a point in favour rather than a black mark.

I'd liken it a bit to the perception of professional cycling being a sport with a uniquely high amount of cheaters. No doubt doping is rife in the peloton, and that's a real problem if the goal is a clean sport, but one of the reasons a comparatively high amount of people test positive is that cycling is one of the sports with the most rigorous testing. In a lot of sports, especially including the big money sports, the protocols are frankly pathetic so of course people rarely get caught.
The problem with replication in (social) psychology is that a lot of the foundational work, then built upon by top researchers in the field over the next several decades are not only non-replicable, but even possibly fraudulent. It’s cast doubt on a lot of current thinking within the field. Some of the most famous experiments (Standford prison experiment being one) performed by some of the most eminent psychologists (Zimbardo, Milgram) are partially, if not completely misleading. These experiments and their implications span multiple chapters in dozens of academic text books. Of course this doesn’t speak to the frequency of non replicable research but it has somewhat set a narrative for social psychology. Perhaps psychologists do revisit previous studies more often but if for example Crick and Watson’s or Sanger’s research on DNA was found to be most probably fraudulent/non-replicable I’d imagine there would be similar uproar within Biology.

Edit: Though of course if the research on DNA structure or sequencing had been non-replicable I imagine it would have been discovered quite quickly.
 

VorZakone

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Just read that the famous "Thinking, Fast and Slow" book by Kahneman may also be suffering from replication issues. How valid are these pop-book insights really?
 

PedroMendez

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Just read that the famous "Thinking, Fast and Slow" book by Kahneman may also be suffering from replication issues. How valid are these pop-book insights really?
here a good blog post about the replicability of the book:
A Meta-Scientific Perspective on “Thinking: Fast and Slow | Replicability-Index (replicationindex.com)

here is a comment from Kahneman:
Reconstruction of a Train Wreck: How Priming Research Went off the Rails | Replicability-Index (replicationindex.com)

here a general op-ed(ish) article related to the topic:
http://www.stat.columbia.edu/~gelman/research/published/failure_of.pdf


I am highly skeptical of any findings that reports large effects, especially if these effects don't confirm common sense. On the flip side, small effects, while potentially interesting, are rarely relevant and have substantial error bars. To properly identify small effects, one needs very good and relatively large data and this kind of data rarely exists.

I am more willing to trust people, who are open about the uncertainty, limitations, problems and alternative explanations than those who present their findings as facts. The problem is, there are few incentives to do that in science and even more so in pop-science publications.
 

VorZakone

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Question: why in academia is it so much more important to prove the existence of a causal relationship? I'd be just as interested in research that isn't able to prove a causal relationship, at least to clear up misconceptions or bust some pop myth.
 

hobbers

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Question: why in academia is it so much more important to prove the existence of a causal relationship? I'd be just as interested in research that isn't able to prove a causal relationship, at least to clear up misconceptions or bust some pop myth.
Because everything is weighted towards getting publications, nothing matters if you cant get it published, and the editors of the major journals only care about headline discoveries.
 

Cheimoon

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Question: why in academia is it so much more important to prove the existence of a causal relationship? I'd be just as interested in research that isn't able to prove a causal relationship, at least to clear up misconceptions or bust some pop myth.
Because everything is weighted towards getting publications, nothing matters if you cant get it published, and the editors of the major journals only care about headline discoveries.
Exactly. Scientists are largely judged, both by their peers and employers, by the prestige of their work (i.e., publications in high-ranking journals that get cited a lot in other articles). Articles that disprove theories or links, or report on not having achieved a result when looking for something, don't get accepted in those journals (except if something major is being disproven), and so for many scientists aren't worth the effort. In fact, if a scientist has finished a project and didn't get what they were hoping, they will usually start searching the data for anything unexpected that might be interesting, rather than reporting on the 'non-result'.

It's a sorry state of affairs and will require a major overhaul of performance measurement in science. That's widely acknowledged and slowly underway - but really only very slowly.