How many medical studies are faked or flawed?
web.archive.org
web.archive.org
So, 70% fake/flawed. The finding falls in line with other large scale replication studies in medicine, which have had replication success rates ranging from 11 to 44%. [1] It's quite difficult to imagine why studies where a positive outcome is a gateway to billions of dollars in profits, while a negative outcome would result in substantial losses, might end up being somehow less than accurate.
[1] - https://en.wikipedia.org/wiki/Replication_crisis#In_medicine
... into the multi-billion dollar companies GP is talking about.
That doesn't mean you're a bad scientist, just an unlucky one. But it does mean you can't get tenure.
So it's easy to understand why people fake results to secure a career.
That sounds like a really easy problem to solve. Just treat valid science as important regardless of the results. The results shouldn't matter unless they've been replicated and verified anyway.
We should reward quality work, not simply the number of research papers (since it's easy to churn out trash) or what the results are (because until they are verified they could be faked).
Actually implementing it across the academic world seems much harder.
1. No, there is minimal or no numerical matching between populations of neurons in retina (ganglion cells) and populations of principal neurons in their CNS target (the thalamus). That demolished the plausible/attractive numerical matching hypothesis. I was trying valiantly to support it ;-)
https://pubmed.ncbi.nlm.nih.gov/14657177/
2. No, there is no strong coupling of volumes of different brain regions due to “developmental constraints” in brain growth patterns. https://pubmed.ncbi.nlm.nih.gov/23011133/
That idea just struck me as silly from an evolutionary and comparative perspective. We were happy to call it into doubt.
I suspect many of the comments are being made by damn fine programmers who know right from wrong ;-) a la Dijkstra. But in biology and clinical research, defining right and wrong is an ill-defined problem with lots of barely tangible and invisible confounders.
We should still demand well designed, implemented, and analyzed experimental or observational data sets.
However, that alone is not nearly enough to ensure meaningful and generalizable results. The meta-analyses were supposed to help at this level for clinical trials but have been gamed by bad actors with career objective that don’t consider patient outcomes even a bit.
Highlighting the problem is a huge step forward and it looks like AI may provide some near-future help along with more complete data release requirements.
If you have done biology—- Hot. Wet. Mess. But beautiful.
Do you think CocaCola and the Sacklers had their own unique ideas shared by no one else? That we've filtered all scrupulous people out of industry?
Scruples are an abstraction at that scale.
[0]: Politico "Coca-Cola tried to influence CDC on research and policy, new report states" [https://www.politico.com/story/2019/01/29/coke-obesity-sugar...]
[1]: "Evaluating Coca-Cola’s attempts to influence public health ‘in their own words’: analysis of Coca-Cola emails with public health academics leading the Global Energy Balance Network" https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10200649/
[2]: Forbes: "Emails Reveal How Coca-Cola Shaped The Anti-Obesity Global Energy Balance Network" https://www.forbes.com/sites/nancyhuehnergarth/2015/11/24/em...
This is a huge problem and in my opinion is mostly due to bad incentive structures and bad statistical/methodological education. I'm sure there are plenty of cases where there is intentional or at least known malpractice, but I would argue that most bad research is done in good faith.
When I was working on a PhD in biostatistics with a focus on causal inference among other things, I frequently helped out friends in other departments with data analysis. More often than not, people were working with sample sizes that are too small to provide enough power to answer their questions, or questions that simply could not be answered by their study design. (e.g. answering causal questions from observational data*).
In once instance, a friend in an environmental science program had data from an experiment she conducted where she failed to find evidence to support her primary hypothesis. It's nearly impossible to publish null results, and she didn't have funding to collect more data and had to get a paper out of it.
She wound up doing textbook p-hacking; testing a ton of post-hoc hypotheses on subsets of data. I tried to reel things back but I couldn't convince her to not continue because "that's how they do things" in her field. In reality she didn't really have a choice if she wanted to make progress towards her degree. She was a very smart person, and p-hacking is conceptually not hard to understand, but she was incentivized to not understand it or to not look at her research in that way.
* Research in causal inference is mostly about rigorously defining the (untestable) causal assumptions you must make and developing methods to answer causal questions from observational data. Even if an argument can be made that you can make those assumptions in a particular case, there is another layer of modeling assumptions you'll end up making depending on the method you're using. In my experience it's pretty rare that you can really have much confidence that your conclusions about a causal question if you can't run a real experiment.
These numbers seem almost wholly unrelated. A perfectly good study may be extremely difficult to replicate (or even the original purpose of replication - the experiment as described in the paper may simply not be sufficient); and an attempt at replication (or refutation), successful or not, is under the same pressure to be faked or flawed as the original paper.
I think you miss read that section? Only were 44% fake or flawed acording to this study.
The 26% that were very flawed is a subset of the 44% that were flawed in general. So those precentages should not be added together.
And then there's cultural differences in which people sometimes see a negative result as a "failure", don't publish it as a result, and instead skew the data and lie their asses off in order to gain prestige in their career. As long as nobody double checks you, you're good.
Academia seems like the idea place for this. Why not require a certain number of replicated studies in order to get a degree? Universities could then be constantly churning out replication studies.
More importantly, why do we bother taking anything that hasn't been replicated seriously? Anyone who publishes a paper that hasn't been verified shouldn't get any kind of meaningful recognition or "credit" for their discovery until it's been independently confirmed.
Since anyone can publish trash, having your work validated should be the only means of gaining prestige in your career.
And that's when I believe people do have a somewhat best effort to maximize profits. There are plenty of people that only care about career progression and think they can get away with lying and cheating their way to the top. They wouldn't believe that if it didn't work sometimes.
These medical studies are also run mainly to maximize profits, also by some career climbers. They are not run virtuously for the betterment of society.
So I would be astounded if they are as reliable as people might like to believe.
Maybe I'm just being grossly skeptical. Actually, I'd feel better if someone could convince me I'm completely unfounded here.
I do think there is an even worse issue - which is funding. The money incentive means you can fund studies that support whatever you want.
Nope. I actually think that if you do scientific research as a company (profit) it may make you less bad/less likely to do fraud compared to academia (non-profit).
Reason is that there are more ways to punish you, employees, board, investors, etc in a profit seeking vehicle, and as a profit seeking vehicle being caught must be part of the profit seeking calculation – in the end, the world of reality/physics will weigh your contribution.
I believe there is evidence that there is more fraudulent scientific research happening in non-profit vehicles/academia. Take for example an area where there are fewer profit seeking companies participating - social sciences. It's dominated by academia. Now look at the replication rate of social sciences.
You can fake everything except a well designed A/B test. At FAANG scale, a statistically significant A/B test requirement will stop the worst fraud before it hits the user.
Seriously though, as a person who has built related systems at FAANG, yes this problem exists there. Your beautiful cathedral of an A/B testing framework is covered in knobs that are just perfect for p-hacking.
We saw flaws in the data collection - basically the people tasked to collect data were being lazy and some were making stuff up. We know the made up stuff when we see it. Outliers are fine and some groups do better than expected, but an entire group from one data collecter shouldn't be 100% outliers.
But we had to enter the data anyway. We were told to smooth the bad data to what was expected. So the outliers that were low were smoothed high, the high outliers were left alone because they seemed right. But those of us who were spending hundreds of hours on data entry had an intuitive feel of what an outlier looked like.
IMO everything should have been entered as is and the computer data would just be filtered out if it was deemed from a corrupt source. But the data in the computer was biased to match what the research wanted to prove.
So I agree that non-profits can be corrupt too, just because of the incentives each part of the way. We were being paid about half a cent per column of data. So some assistants were lazy and filling in data that could be right, or skimming on fields like address which are longer and less likely to be flagged.
So many people have the opinion that private research must be flawed because of the profit motive, but the profit motive ensures that someone will be motivated to take oversight seriously, and have the power to punish misbehavior.
Free markets, as ugly as they are sometimes, are still the best way we have of ensuring that incentives align with outcomes.
War also works, to a certain extent, as a source of truth to align incentives with outcomes. But it's horribly expensive even in terms of economics alone, not to mention the human tragedy.
Luckily free markets work as a backstop, too. Add in free movement of people (who often want to come to better run places), and free movement of capital, and you have a winning combination.
Another stroke of luck: even if you only implement a very partial version of 'free', you still get partial benefits. Slightly freer markets are typically slightly more efficient. It's not an all or nothing proposition.
Granted, they are mostly interested in a very small sliver of social science: 'how can you get people to directly or indirectly spend more time online and look at more ads'; but they are very, very interested in getting robust results that replicate well. They are also interested in figuring out how the results vary between different cultures and over time.
In academia, you essentially have the student who does the work, the professor, and the person who funded the grant, and that's essentially the sum total of people supervising the data. There's not a lot of people to call you out on fudged (or outright faked) data, and all of them are likely to be very invested in the success of the research.
Turn to industry, and you have a similar set of people--the worker, the manager, and the head of the research department--except maybe a few more levels of manager (depending on the scale of the project). But since the goal is usually productization in industrial research, you usually have to turn to the product divisions and convince their executive chain as well of the merits of your research. And unlike everybody else mentioned so far, this group of people isn't invested in the success of the research. You might even be competing against other research teams that have different alternatives, and those people are going to be actively invested in the failure of your research so that their research makes it instead.
The 'product' of academic research is a published paper. The product of industrial research is an actual product.
(This does not apply when your research is about eg effectiveness of a new drug. The product people can sell that drug on the strength of that research. Whether that research replicates or not is only of indirect concern in that case.)
Fwiw, I do not know of any data in my realm which have been molded, cherry picked, intentionally misrepresented, falsified, or otherwise fake or flawed. I don’t work with clinical trial data, so if that were happening, it wouldn’t be on my desk.
The biggest difference I saw was that universities were very short-term focused, while we were more long-term focused.
The universities had a constant churn of personnel, as new PhD candidates appeared and old ones left, whereas our own researchers and technicians stuck around for far longer.
Additionally, they were so hyperfocused on grants and papers that they tended to not put as much effort into replication, since that didn't pay their bills. By comparison, we typically repeated our experiments ad nauseum; it was common for us to perform the same experiment twice a year (once in the northern hemisphere and then again in the southern hemisphere) for a decade or more, gradually iterating and refining our processes along the way.
Even if we initially got negative results, we'd beat that dead horse for a few years to make sure. Occasionally it turned out not to be so dead after all.
It seems like destroying the reputation and career of people who fake science would be a great start. If you're willing to fake data and lie to get results, there will always be an industry who'd love to hire you no matter how tarnished your reputation is. We need a better means to hold researchers accountable and we need to stop putting any amount of faith in any research that hasn't been independently verified through replication.
Today the lobby for orange juice manufactures can pay a scientist to fake research which shows that drinking orange juice makes you more attractive, and then pay publications to broadcast that headline to the world to increase sales. We should have some means to hold publications responsible for this as well.
When so many reports are faulty and fraudulent, that might instead be the great start of destroying the careers of those who would have revealed the fraudulent research?
I wonder what'd happen if researchers got compensated and funding based on other things, unrelated to papers published. But what would that be
See what they do in the parts of industry where they need their research to work.
Eg how do battery manufacturers compensate and incentives their researchers that are aiming to improve various characteristics of batteries? How do steel mills manage and reward their metallurgists? How does Intel's research work?
But that's different from a PhD student -- they're not embedded in any organization that would notice if the research works or not?
Maybe if the universities partnered somehow with different companies, and the researchers got extra compensation if a company decided to make real world use of the research?
(On top of some base salary)
But who would determine if a company had made use of a certain research paper? What would the company gain, by keeping track and reporting back? Maybe more good research
(but I'd guess few companies would be that much forward-looking?)
I don't know. Is that speculation on your part, or something you figured out?
> But who would determine if a company had made use of a certain research paper? What would the company gain, by keeping track and reporting back? Maybe more good research
> (but I'd guess few companies would be that much forward-looking?)
That's why I am saying we should look what real companies are actually doing already in reality. We might have to leave our armchairs for that.
That's just normal monthly wages, how things usually work.
> look what real companies are actually doing already
But you can't look at what companies are doing now, to find out if new research is useful? The companies can't yet have started doing the things that any new & good research enables (since it wasn't known before).
Could take years until they make use of the research
(But maybe you meant something else)
Look at what processes worked (and didn't work!). Not at what specific inventions worked.
(Research by Google about psychological safety comes to my mind.)
Unfortunately, your skepticism is not unfounded. Those in the industry conclude the same. Take, for instance, the editor in chief of The Lancet:
>The case against science is straightforward: much of the scientific literature, perhaps half, may simply be untrue. Afflicted by studies with small sample sizes, tiny effects, invalid exploratory analyses, and flagrant conflicts of interest, together with an obsession for pursuing fashionable trends of dubious importance, science has taken a turn towards darkness.
https://www.thelancet.com/journals/lancet/article/PIIS0140-6...
You quickly learn not to be the guy pointing out the problem that means we’ll need several people and months or years to gather and analyze data that would allow them to (maybe) support or disprove their conclusion, though. Nobody wants to hear it… because they don’t actually care, they just want to present themselves as doing data-driven decision making, for reasons of ego or for (personal, or company) marketing. It’s all gut feelings and big personalities pushing companies this way and that, once you cut through the pretend-science shit.
“Yeah, that graph looks great (soul dies a little) let’s do it”
Oh, that hits home
Absolutely insane to watch.
I'm not in the industry so my question might have an obvious answer to those of you who are: How would one go about getting IPD if you wanted to run your own analysis of trial data or other data-driven research?
Most of the studies that were problematic came from China and Egypt.
In other words, nothing new here.
[1] https://associationofanaesthetists-publications.onlinelibrar...
"It is simply no longer possible to believe much of the clinical research that is published, or to rely on the judgment of trusted physicians or authoritative medical guidelines. I take no pleasure in this conclusion, which I reached slowly and reluctantly over my two decades as an editor of the New England Journal of Medicine." -Marcia Angell
"If the image of medicine I have conveyed is one wherein medicine lurches along, riven by internal professional power struggles, impelled this way and that by arbitrary economic and sociopolitical forces, and sustained by bodies of myth and rhetoric that are elaborated in response to major threats to its survival, then that is the image supported by this study." -Evelleen Richards, "Vitamin C and Cancer: Medicine or Politics?"
File under: Follow the money
I think skepticism is healthy to a degree, but ironically, the more adversarial the general public is to an area of study the more rigorous it usually is.
The vaccine was tested against the original virus but Omicron was almost a different disease with much more immune escape. We're lucky the vaccine held up as well as it did. This is also why the alternative doctors that wanted everyone to catch covid to build up herd immunity were wrong, but nobody seems to bring them up. These discussions just devolve into the usual polarized political talking points.
It's only as this very obviously failed to be the case that the metric was completely shifted to hospitalization/death. I'd also add this is about the time that the 'public messaging' swapped from talking about efficacy and other topics to outright vitriol and attacks on unvaccinated individuals, which is probably where the politicization of the topic began.
Trust the politics, blame the science?
"There were 11 COVID‑19 cases in the Moderna COVID‑19 Vaccine group and 185 cases in the placebo group, with a vaccine efficacy of 94.1% (95% confidence interval of 89.3% to 96.8%)."
They were claiming it outright prevented COVID, as vaccines generally do. So "the politics" and "the science" were in lockstep on this one. As were they when they seamlessly dropped this narrative and swapped over to hospitalization/death.
[1] - https://web.archive.org/web/20210202223626/https://www.moder...
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745181/
Sample size was 43k people split in treatment and control group. Adding it here for context.
That's the-moon-landing-was-fake tier denial-ism.
You cannot compare the moon landing to the COVID circus.
What does "aren't outright bullshit" translate into in quantitative (%) terms?
Even if all that were not intentional, it would certainly raise the question why this should be the only case of fabricating data for studies. Since the RNA-vaccines are obviously not as positively effective as claimed, what else is there that might be not talked about? And lastly why is the topic still that controversial when the safety and effectivity claims have not stood up to the real world test?
Increasing seems like peer review is a waste of everyone's time, and it's be better to just publishing when you think your ready on the likes of arXiv, and let everyone examine and criticize it.
Run it up the flagpole, see if it gets saluted or shot down. Seems like there's be much more of an incentive to run confirmation studies.
Why? Because its just cargo cult science at the end of the day. Once you incentivize clinical trials to be used as a tool to unlock massive profits through an arbitrary process with a single regulator as judge it WILL be hacked. At every possible part of the process including regulators.
It's worse. First, there's a selection bias in what trials the author can get data for. Second, the 44% + 26% are just those where the author can detect problems from reading their data alone. If someone convincingly fakes their data, the author can't detect that.
They've always known so there hasn't actually been any new information from which to spur action. In the academic circles I've run in there has always been a strong mistrust of reported results and procedures based on past difficulties with internal efforts replicating results. Basically a right of passage for a grad student to be tasked with replicating work from an impossible paper.
Many papers aren't even "looks legit but doesn't replicate", they are self-evidently wrong just from reading them or the associated data. Just peer review done properly would solve that, but the problem is widespread because there's no real incentive to rock the boat. Once a bad technique or invalid approach gets through to publication a few times it becomes a new standard within the field because it lets people publish more papers.
There are only two ways to fix that, as far as I can see:
1. Have highly technical, scientifically trained and skeptical politicians who police academia and ensure research money is well spent.
2. Stop governments funding research in the first place.
(1) just doesn't seem feasible. There are so many problems there. (2) is feasible. So I suspect you're going to start seeing calls from the right to defund universities over the next ten years or so. The underlying motivation may be that universities are strongholds of the left, but the stated justification will be the high levels of research fraud.
Attempting to fix this within the framework of government would be incredibly distracting for the state, as it'd lead to endless fights and debates. For example, a reasonable person appointed to run the NSF might conclude they should just defund whole subfields of the social sciences, because there's no way to make them scientific at all, but good luck lasting in the job if you do that. A big part of the problem is that scientists are in hoc to a specific political ideology, so they will have powerful friends in Washington who want to see them stick around producing useful propaganda.
Huge funding for replication studies is commonly suggested, but it won't work. I'm basing that view on experience of having read and reported invalid papers across several different fields, spent years following this story and its various ins and outs, and have written extensively about the problems with pseudo-science coming out of official institutions.
Replication is the polite society way to talk about a whole range of problems. People say there's a replication crisis because it sounds a lot better than saying there's a large scale fraud and incompetence crisis. But it's not like there are papers out there that abstractly don't replicate, nobody could have known, and when someone tries and fails then there's suddenly a whole process that's followed to root cause why it failed and fix it. There isn't anything even close to that. What actually happens is that very obvious problems are ignored or kicked into the long grass as long as possible, often indefinitely, and sometimes scientists even argue they're being victimized by those reporting problems.
This article is a reasonable place to start, if you're new to the problem space:
https://fantasticanachronism.com/2020/09/11/whats-wrong-with...
It raises points that are often overlooked due to the "replication crisis" framing, points like:
• Many papers that can be replicated are only replicable because they're of little value (e.g. poor children have worse exam results). We don't want to be flooded with highly replicable science that tells us only what everyone already knew.
• Many papers are replicable but their claims are still invalid because their logic or methodology is wrong. This category covers significant amounts of COVID science, for example.
• It's often unclear exactly what the definition of replicable is.
But zooming out from replicability for a moment, I wrote an article with about some of the more egregious problems here:
https://blog.plan99.net/fake-science-part-i-7e9764571422
We're talking about a scientific culture that can't even stop obviously machine-generated text from being published on a massive scale. No other part of society has problems like this. Even talking about replication seems like a distraction whilst you have papers being published every day that contain obviously Photoshopped images, mathematically impossible numbers and AI generated text, yet nobody cares and getting even one case "resolved" (paper retracted) requires months or years of external pressure.
Think about how big the trust problems are with journalism. How to fix trust in journalism is talked about a lot on the conference circuit, but it's a hard problem. And yet newspapers don't publish garbled gibberish and faked images on a daily basis! That's the scale of the challenge faced with science reform.
And even if you somehow manage to drain that swamp, the first step before full replication is attempted is to get the original data, so the analysis steps can be replicated using the original numbers. Whole swathes of science fail here because academics refuse to reveal their data. This happens even when they've been made to sign a statement agreeing that they'll provide it on request. Again, their employers do nothing, so where's the pressure point? Only massive fiscal punishment could cause culture change here but the NSF has a single institutional goal of dishing out as much money as possible. To quote Alvaro de Menard,
Why is the Replication Markets project funded by the Department of Defense? If you look at the NSF's 2019 Performance Highlights, you'll find items such as "Foster a culture of inclusion through change management efforts" (Status: "Achieved") and "Inform applicants whether their proposals have been declined or recommended for funding in a timely manner" (Status: "Not Achieved"). Pusillanimous reports repeat tired clichés about "training", "transparency", and a "culture of openness" while downplaying the scale of the problem and ignoring the incentives. No serious actions have followed from their recommendations.
It's not that they're trying and failing—they appear to be completely oblivious. We're talking about an organization with an 8 billion dollar budget that is responsible for a huge part of social science funding, and they can't manage to inform people that their grant was declined! These are the people we must depend on to fix everything.
There’s no replication crisis for academics because they have a meatspace social network of academics; they go to conferences together and know each other. You can just ignore a paper if you know the author is an idiot.
If medical studies are faked, is it a problem? Presumable regulatory agencies are using these studies or something, right? Looks like the FDA and NSF need to fund some more replication studies.
This is putting it very mildly.
What can they do? It's an incredibly hard problem to solve. It's like asking why the buisness community has done nothing to adress the housing crisis.
Large scale culture changes, or the entire structure of the way science is conducted and funded, would be the only solutions
* working groups "red teams" whose whole job it is to find weaknesses in papers published at the university
* post mortems after finding papers with serious flaws exposing the problem and coming up with constructive corrective actions
* funding / forcing researchers to devote a certain amount of time to replicating significant results
* working groups of experts in statistics and study design available to consult
* systems to register studies, methodologies, data sets, etc. with the aim of eliminating error and preventing post-hoc fishing expeditions for results
The whole-ass purpose of a university is seeking knowledge. They are fully capable of doing a better job of it but they don't because what they actually focus on are things like fundraising, professional sports teams, constructing attractive buildings, and advancing ranking.
Most universities would be better off just firing and not replacing 90% of their administration.
That's because universities are in the business of teaching. Apart from a few rare exceptions, universities don't have the money to hire redundant people. Instead of hiring many experts in the same topic, they prefer hiring a wider range of expertise, in order to provide better learning opportunities for the students.
Statisticians can find faults in many studies. Universities even employ statisticians to act as consultants for research in other departments, as that raises the quality of the institution's publications.
Make peer review public. Weight replication studies more. Make conducted peer reviews and replications an important metric for tenure and grants. Publish data and code alongside papers.
It doesn't help. It just means outsiders get to read bad peer reviews and bad code, but the right people within the system don't actually care. There's no way for the public to hold anyone to account, science is a law unto itself because politicians are generally not scientifically minded. They can be easily bullied with technobabble. The few that aren't susceptible to this don't tend to rise to the top.
There's literally thousands of different things they could do.
But why do anything when the real business of academia is in the tax-free hedge funds they operate and the government-subsidized international student gravy train? There's no short-term incentive to change anything.
Or how many studies are useless, period? It's like publishing a memoir to Amazon. You can now say "author" on your resume, or when you're introduced or at cocktail parties but nobody finds any value in what you have to say. You can also use ChatGPT because people might not notice.
You might think that publishing about the replication crisis itself would be great for your career, but perhaps not. Maybe the incentives to be able to bullshit your way to a professorship are so great that no one wants to rock the boat.
Our whole economy is fueled by people bullshitting each other.
Proving something is false is less valued than proving it right. It's silly because if we valued the quality of science it should be the exact opposite.
Sociology/Psych/Economics are almost all junk. Their conclusions may or may not be correct.
Medical studies are mostly junk. There's way too much financial incentive to show marginal improvement. Theraflu and anti-depressents come to mind. Both show a small effect in studies and launched billion dollar businesses.
Hard science stuff tends to be pretty good. Mostly just outright fraud and they usually end up getting caught.
Also a lot of stuff is tough to classify. Are epidemiology or climatology "hard" sciences?
Dude. The scientific community created the replication crisis.
We are not ignoring it, I promise you. The reaction is nuanced by no one who matters is ignoring it.
But maybe that's a good thing? I can't actually say a reason I think it'd be that terrible except for the profs doing novel research that would lose their some of their student workforce.
Bachelors < Masters < PhD
Which, of course, not not really the case. A PhD in <field> is a specialization in creating novel research in <field>. In terms of actually applying <field>, a Masters ought to be as prestigious or whatever as a PhD. That it isn’t thought of that way seems to indicate, I dunno, maybe we need a new type of super-masters degree (one that gives you a cool title I guess).
Or, this will get me killed by some academics, but let’s just align with with the general public seems to think anyway: make a super-masters degree, give it the Dr title, make it the thing that indicates total mastery of a specific field (which is what the general public’s favorite Doctors, MDs, have, anyway) (to the extent to which a degree can even indicate that sort of thing, which is to say, not really, but it is as good as we’ve got). Then when PhDs can have a new title, Philosopher of <field>, haha.
This is not necessarily bad, as there is a lot of drudge work that needs doing in science. The problem is the pressure to over-promote everything you do. The reality is that very few people, even among PhD's, have the capacity to do really original work. There was a study many years ago on the physics community that found that less than 10% of physicists published even one thing in their career that someone else found worth citing.
I would expect 26% or more studies have these flaws but faked data is a different thing entirely and 26% fake would be incredibly worrying
Really, though, how/why would we expect otherwise? There's nothing in the system to prevent it, and plenty to incentivize it. There's really no good reason to expect (under the current system) that it would not happen (a lot). Without systemic change, it will not get better.
After a moment, I thought better, and decided that we should actually offer incentive for fraudulent papers! Liars will always have incentive to lie. When their lies are accepted by those who ought to be more skeptical, it's an indictment of the system, which should be more robust in detecting fraud.
Lots of "interesting" psychology experiments in the days of yore turns out to have lots of damaging confounding variables and we can't just redo the experiment because, well, we shouldn't.
Stream or record your experiment. If a 12 year old has the ability to stream his gaming life on twitch, scientists should be able to record what they are doing in more detail.
You could start a new journal with higher level of prestige that only publishes experiments that adhere to more modern methods of proof.
If the answers were easy I'm sure someone would've implemented it already, it turns out those kind of things are hard.
I'd recommend checking out the following article. https://slatestarcodex.com/2014/04/28/the-control-group-is-o...
Critical details and oversights can be lost in translation between reality and LaTeX that could be easily pointed out by third party observers.
And what scientist is going to be happy with constant surveillance that's intended to be shared (so no picking your nose, losing your temper, or making mistakes)?
[0] Literally, thousands. I just finished a paper about a project that we started three years ago. This was the main project for two people, plus a tech: 2000 hrs/yr * 3 * 80% effort = 4800 hours of footage.
The footage is there to support inquiries into how you did your experiment, just as you would skim a youtube video about repairing an appliance to get to the part you need to see.
And yeah, it would have to be a new level of prestige backing this way of documenting your methods. Obviously people prefer being trusted, but p-hacking and replication problems have proven that we can't have nice things.
That is overly cynical. We are already well past that standard.
We require words on a piece of paper that were written by somebody from a recognizable institution, or by an AI operated by somebody from a recognizable institution.
For example, say you have a new (patentable) drug that you're trying to get approved and replace an old (generic, cheap) drug. You need to prove that the new drug is at least as safe as the old one and/or more effective than the old one. Which sounds reasonable.
Let's say that the old drug is super safe and that the new one may not be. What you can do is set up your RCT so that you surreptitiously underdose the control arm so the safe drug looks less effective. And then you can say that your new drug is more effective than the old one.
Peer review doesn't notice this because you can easily hide it in the narrative of the methods section. I've seen it a couple of times.
So you can easily have "gold standard RCTs" that get the result you want just by subtle changes in the study design.
Sometimes this is true. One example is when the people verifying the pre-registration work for a regulator. The introduction of pre-registration seems to be at least partly behind the collapse in pharma productivity:
https://journals.plos.org/plosone/article?id=10.1371/journal...
17 of 30 studies (57%) published prior to 2000 showed a significant benefit of intervention on the primary outcome in comparison to only 2 among the 25 (8%) trials published after 2000 (χ2=12.2,df= 1, p=0.0005). There has been no change in the proportion of trials that compared treatment to placebo versus active comparator. Industry co-sponsorship was unrelated to the probability of reporting a significant benefit. Pre-registration in clinicaltrials.gov was strongly associated with the trend toward null findings.
But when journals do this the results seem to be worse. They aren't strongly incentivized to improve anything, so you can get studies that claim to be pre-registered but do something different to what the pre-registration said they'd do.
1. Evidence of widespread error and fraud in science in general?
2. Evaluations of many sub-fields of science like we do for drug studies showing how true or false they are?
An even lower percentage.
This species and its cultures are suffering an accelerating cognitive landslide.
Coupled with the many other accelerating descents, it's a shame, really.
I think we almost made it.
Almost.
If published studies that have been replicated were the highest incentive instead, maybe it would reduce the risk of faked studies.
Why couldn't a bad actor just fake the raw data? Isn't that what Climategate was all about?
Edit: See the Dan Ariely drama for an example.
"How many medical studies of medical studies are faked or flawed? A meta report on meta studies."
Can you send a link to it?
Well… yeah? All medications have a list of possible side effects in the leaflet. It’s just that the disease tends to be worse.
Say you have prostate cancer. Should you risk the low chance of dying of prostate cancer 10 years from now, or the higher chance of incontinence, and medical complications from the treatment you undertake now.
Here is a simple tool that can help exploring sample sizes:
With everything we've been seeing in recent years on HN, about science reproducibility and fraud, and the complaints about commonplace fudging and fraud that you might hear privately from talking with PhDs/students in various fields... I wonder whether science has developed a similar alignment problem.
How many people in science careers are doing trustworthy science? And when they aren't, why not?
Well furthering wealth is the reason why tech companies exist. The incentives in academia are completely different. You might be right, but i see no reason to expect similar behavior across such drastically different situations.
Look in the medical literature and it seems outright spammed by reports on the positive effects of caffeine and negative reports on any of the harmful effects one would expect.
Similarly the main active ingredient of red wine (alcohol) is harmful, red wine in particular causes a lot of discomfort, dispepsia, hangovers and other unpleasant effects if you get a bad vintage but look the literature and it is like it will transport you to a blue zone and you will love forever.
And you find those kind of papers spammed in “real” journals, not MDPI or “Frontiers” journals.
Coffee prevents headaches for me so I'll always drink it. And no it's not related to physical dependence although at this point the withdrawal will guarantee a headache.
Got a mild flu/Covid/cold couple years ago. Better in a week. But, during the illness and since, the slightest bit of caffeine would make be incredibly wired to the point of panic attacks. Had to quit cold turkey. I’ve tried a cup now and again, and it’s the same thing: 6 hours of overwhelming anxiety.
Wierd. It’s like I became hypersensitive to caffeine. Oddly, though, nicotine doesn’t have that effect, and I always figured the two stimulants were similar.
I frequently switch between that and coffee (coffee has a much more pronounced effect and sometimes you have to grind)
Dose size matters.
I have the same issue w/ cannabis. Right now I have a few plants (legal) in the garden and also a bag that is going to a friend and I don't care. If I had a little puff though the next day I would want another little puff and another and in a week or so I would be like the guy in the Bob Marley song
Nothing is more natural and free range than the occasional murder between animals.
What I've been told is that opossums are much worse than foxes in the sense that a fox will usually eat a chicken or two to survive but opossums seem to freak out and will kill all the hens in a henhouse in one go.
I've often wished I could talk with my cats but more than ever I wish I could ask them what they knew about the fox. There is this lady
https://www.youtube.com/@debs3289
who meets them in the street, has them come to her door, and feeds them chicken (!) Secretly I imagine that the fox is really a
https://en.wikipedia.org/wiki/Kitsune
and my wife is always reminding me that it has just one tail, not nine.
Mostly we've had people around, either tenants or neighbors, who keep chickens so we don't have to. I'll say the eggs from a small scale chicken operation taste a lot better than commercial eggs.
I definitely thought about trying to draw in the fox but as much as that British lady makes it look easy on Youtube Shorts the legends are that foxes can cause a lot of trouble.
Am I out of place if I say I also like coyotes? (Remember: city boy, so nothing is at stake for me here. I also like wolves!)
And yeah, Japanese folklore has taught me that it's best to avoid kitsune. Though they sometimes turn into magical women who help you?
I don't tell the SPCA, however, that I am getting a cat there because it's a replacement for one lost to predators. They die of old age, get hyperthyroidism, high blood pressure, become blind, walk around mindlessly and crash into things, and one morning you find them dead at the bottom of the stairs. I think they would find dying at the claws of a predator an honorable death.
As for kitsune they are the Japanese version of a myth that is widespread in East and South Asia, for instance Daji
https://en.wikipedia.org/wiki/Daji
is probably isomorphic to Tamamo-no-mae
https://en.wikipedia.org/wiki/Tamamo-no-Mae
and is sometimes believed to be the same entity. I was into anime for a long time (Urusei Yatsura was a watershed but it really goes back to seeing Star Blazers on TV) but lately I have been into Chinese pop culture like Three Kingdoms, Nezha and Wolf Warrior 2 and that's gotten me reading about Chinese mythology and one clear thing is a lot of Japanese mythology comes from China, for instance it seems there is a "world tree" in almost every JRPG that they climb to get to heaven and even if they call it Yggdrasil it is not from Norse mythology it is from Chinese mythology. (Turns out also a lot of Chinese mythology as well as neopagan ideas comes from India as well.)
The Japanese do derive a lot of their culture from the Chinese, don't they? I would like to one day read some of the stuff around the Three Kingdoms. Alas! So much to read and do, and so little time!
Anyway, thanks for this conversation, I enjoy it.
The old adage applies: "everything nice is either illegal or bad for your health". Or both, I would add.
And is known to cause cancer in the State of California.
Never the middle ground, it's always a shocking new finding "by science" (spoiler: scientists seldom say the things newspapers and pop-science/nutrition & health articles claim they say).
I think peeing more lowers your uric acid.
Oh, before I forget! Red meat and saturated fat is terrible for you! Wait, no, it's actually sugar that's evil. Vegetables are great for you! Oh wait, most vegetables contain oxalates and other defense, mechanisms and compounds that are actually bad for you overtime.
Do you think well-funded RCTs (like those that support vaccine safety) are just as weak as any old observational study?
But my question to the person saying it's problematic to defend vaccine studies and attack food results is: isn't it possible that you feel the research procedures used in one are superior to those used in another?
For example: vaccine safety study looks at 200,000 people and randomly assigns them to use or not use the vaccine. Coffee/red wine study looks at 30 people and surveys them about how they felt last week after drinking coffee/red wine. Looking at these two, I think it's fair to put more trust in the vaccine study.
Something being in a category, such as "a study", doesn't tell you much about a thing. If you read multiple studies on vaccine safety critically and reason about them and what experts are saying about them, IMO most functional human being are going to reach the same general conclusion about vaccine safety. If you do the same thing on studies about seed oils or aspartame you're also going to come to the conclusion that they're safe! If you're not reaching these same results it doesn't necessary mean you're the one who is malfunctioning but you should seriously consider it and try again to learn what you might not know.
Red meat is a little bit bad for longevity. The majority of the reported effect is correlative.
> it's actually sugar that's evil.
Sugar is bad but mostly because it's easy to overeat, and obesity is all around terrible for health.
> Vegetables are great for you! Oh wait, most vegetables contain oxalates and other defense, mechanisms and compounds that are actually bad for you
Cooking removes most oxalates (tho vitamins too, to be fair). But the overall effect of oxalates is relatively minor, except in extreme cases.
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Every food source has advantages and disadvantages.
Not being obese is 75% of the health battle.
Skepticism is reasonable.
The only way (imo) to stay on firm ground is to acknowledge that someone published a thing saying xyz, and maybe that you are x% convinced by it. Can't get too far out over your skis going that route.