... 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?
Note that "studies" generally means "clinical studies" and this is also true for the Wikipedia reference. That's only one category of scientific publication.
Today we see people trying to cast doubt on all scientific research based on examination of clinical or pre-clinical studies. Then we have people who want to weaken the powers FDA. Would that increase or decrease the amount of fake/flawed data. No doubt the US could be the world leader in Snake Oil if we just got rid of all those burdensome FDA requirements.
Sometimes when a company is developing a therapeutic, a paper is published by an academic that in some way casts doubt on the future potential of the therapeutic. The company's management may become quite distressed; they may try to sign an agreement with the author with confidentiality provisions in order to silence him. The company may try to portray the author as a "maverik" in the scientific community, but the paper is never assumed to be flawed/fake. It is taken seriously.
The point is that even if there is a preponderance of fake/flawed data in scientific publications, we still have to separate the wheat from the chaff. We have to do that work. The "30%" cannot be dismissed or ignored. We cannot just assume all, or even most, papers are untrustworthy. (Or vice versa.)
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?"
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?
File under: Follow the money
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 effect 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.
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