This is about the latter. This is the kind of corruption that spreads. It's a very bad thing.
It is related to the former problem of "not replicable," which is about application of the "scientific method." On this, my worldview has evolved a lot over the last decade. I think we've been applying overly Popperian methods, and formalisations of the scientific method where they don't work well.
Say you are studying the effects of a breakfast program, 2 hr recess or push-up regiment on high schoolers. The study is well constructed, faithfully conducted, etc. You find interesting, potentially actionable ways of reducing obesity, improving psych health or something.
Do/Should we even expect such a study to replicate in 1990s Tokyo, 2000s Miami, School A, School B, etc.
Does that mean this study should not be conducted? Does it mean we can't generate knowledge and understanding of things this way? Is there any daylight between "anecdote" and a result that qualifies for publication in Physics?
A lot of articles published in many fields feel like they are being hammered into a Popperian mold they don't fit into. Everyone knows it, but no one is willing to say "this method doesn't work here. we're actually doing a different thing." Instead, they sort of eye roll the article into the frame and let readers unscramble it for themselves.
However, a big chunk of this statistic is not actually fraud. It's just false empiricism. If you look at a lot of the actual papers, I think you'll find that a lot of them just aren't trying to do empiricism... at least not strict empiricism. They're forced into that structure by a convention.
Either (1) "Not Science" becomes OK and extends to economics, some areas of ecology, and such. (2) The Scientific Method gets renamed "Method A," and the concept of scientific pursuit is more flexible, depending on what you are studying or (3) We force these poor bastards doing economics, psychology and such to pretend that they're doing a method that they're not... or publish in the prestigious "Unscientific Economics & Psychology" journal.
> Despite its shortcomings for interpreting current data, power can be useful for designing studies and for understanding why replication of ‘‘statistical significance’’ will often fail even under ideal conditions. Studies are often designed or claimed to have 80 % power against a key alternative when using a 0.05 significance level, although in execution often have less power due to unanticipated problems such as low subject recruitment. Thus, if the alternative is correct and the actual power of two studies is 80 %, the chance that the studies will both show P ≤ 0.05 will at best be only 0.80(0.80) = 64 %; furthermore, the chance that one study shows P ≤ 0.05 and the other does not (and thus will be misinterpreted as showing conflicting results) is 2(0.80)0.20 = 32 % or about 1 chance in 3. Similar calculations taking account of typical problems suggest that one could anticipate a ‘‘replication crisis’’ even if there were no publication or reporting bias, simply because current design and testing conventions treat individual study results as dichotomous outputs of ‘‘significant’’/‘‘nonsignificant’’ or ‘‘reject’’/‘‘accept.’’
Nothing about "this study applies to this specific situation" is counter to Popper. However, if it fails to replicate in other situations, it tends to suggest at the very least "might be too specific to this situation to have practical application".
This happens in software as well. If my system works on my specific laptop but anyone with a slightly different version of the programming language, imported libraries, or operating system will be unlikely to get it to work, then it's just not good software. The solution is not (usually) to put something in the README that says "will only work with python 3.6.7 on Ubuntu 18.04", the solution is to change the software so it is more generally applicable. And it's on me the programmer, to make that happen. If the scientific phenomenon is specific to 1990's Tokyo but not 2000s Miami, then some major variable is being left out, and the science requires finding out what that is.
I agree with your basic point that there is a big difference between non-replicable and fraud, though.
Maybe not technically, but it does imply that "this specific situation" should be defined specifically, hence controls. I agree that broad applicability, and replicability are strong indicators of truth value. That said, there are pursuits of rational knowledge outside of Popperian ideals. That's not represented in the way Science, as an institutions works.
We found that students participating in the "2hr active recess program" have meaningfully better health outcomes, psychological health outcomes, and better grades. It was tested faithfully in 25 schools over 10 years.
It expands to other places. Different countries. Different decades. Different people run it in different ways. The kids are different. In some cases, the kids are already so active that the extra activity is detrimental. In some places, it's really hot. Kids tend to read, or do more organized indoor activities.
Basically, the world is not a laboratory. You're not predicting the trajectory of a projectile, where unknowables can be quantified as a function. It's all 3 body problems all the time.
Yet, we as humans do manage to make sense of the world. We run schools, companies. We learn more about how to do those things. Formal studies are an important part of learning about how to do those things. Scientific mindset and methodologies are useful to this. It's a lot better than just philosophizing the whole thing. The type of knowledge we gain, OTOH, isn't like F=MA. It never will be.
The value of the above study does relate/imply to it containing knowledge. It's a partial knowledge of a general problem. It is not complete knowledge of a particular problem, like you would get in a lab. Neither lab experiments or wild studies tend to replicate in one another's world.
No matter how good the study, how many times it replicated, or how well it worked throughout the 80s and 90s... there's no guarantee that your program is working well or a guaranteed explanation for why it isn't.
Elucidating confounding variables and controlling for them is the entire fucking job of a scientist.
To your example, if they publish a paper that explicitly highlights that it worked well for sedentary kids with typical diets of X, Y, and Z in region Q, then it failing to be a found in other environments isn’t a problem.
It’s a problem when these clowns do a test on one specific group of 20 people and then generalize to, “doing X leads to Y in humans”.
The answer is absolutely not loosening up the constraints. It’s to keep lambasting people who generalize before anything has been reproduced widely.
Popper’s science is fine. Morons who don’t realize what they measured are the problem.
Many times the first or first few teams won’t know what the confounding variables are and subsequent research is needed to find them. They should still report their initial “hmm, that’s novel” findings even if it later turns out that it’s explained by confounding variables.
*-No true Scotsman
No. This is methodology. The job of a scientist is to advance scientific knowledge. This is as vague as it needs to be.
Popper didn't do science. He did philosophy. He also did a fine job of formalising methods, and abstracting the methods scientists were using into a Scientific Method. This scientific method is a great achievement and a wonderful, multipurpose probe. That does not mean it fits in all orifices. The job of a scientist is not to continue hammering until the probe fits.
I think it has to to be held out as a possibility that there is actually no science behind how we run schools and companies. One way this could happen is if non-scientific ideas simply overwhelm the ones that are grounded in good science.
The fact that it isn't like F=MA also means it will never advance beyond producing collections of amusing factoids that are unrelated to one another and potentially false.
It’s actionable by trying it in a new circumstance (set of parameters) and observing whether it fails - over and over. Failure doesn’t mean the claim is wrong/untrue, it means you’ve changed a parameter that happens to be important: now find it!
I agree that this is a generalization problem. I just don't believe that this particular way of describing it is helpful. If you'll allow me an apocryphal example, when Newton saw the apple fall, if he had generalized to "all things from apple trees fall at the same rate", he would have been correct for a while, then ludicrously incorrect once the leaves started blowing off in Autumn. If at that point he had tried to figure out exactly what kind of tree produces this gravity thing, or which parts of trees, we wouldn't be any further along than we were before.
Generalizations work along multiple axes. With some degree of imprecision, every experiment can be a success (or failure). This is why we have the scientific method in the first place.
Now, perhaps you're making the argument that this is somehow, for lack of a better term, half-science. It's not right, but it's not wrong either. It's truthy. The logical follow-up would be: ok, where is the second half? In this and many other cases there is no second half. Papers were published, people received awards, wrote books, became famous, and lots of other folks used their work. So even if somehow this is better than nothing, in practice I'm not seeing it.
I think framing it in this lens is helpful because it points toward a solution: capturing more of the parameters more explicitly and varying them more deliberately. If we just say, “oh x y z studies aren’t replicable, field [x y z] is bullshit,” we are ending our inquiry prematurely. We are ending it at the falsification of Newton’s initial theory and saying that because leaves fall at a different rate, that gravity doesn’t exist.
If you can't control parameters so that the results you are claiming are reproducible you are just publishing noise ?
Should a journal paper never be published until the field has established all the fundamental properties? How could it discover those fundamental properties without the publications?
Surely there is one, but it's maddeningly hard to pin down. Especially when the experiments are hard to perform and control properly, which applies both to psychology and astronomy.
Remember that scientists existed before philosophy of science, including formalisations of The Scientific Method. Darwin wasn't following the scientific method, the method followed him.
You're not going to find the parameter which makes a high school nutrition program deterministic. It shouldn't be the goal.
If you can’t find the parameters that make it reproducible, what value does it have in the first place?
Look at emotional intelligence (EQ) as an example. One of the early books on the subject written by a journalist which was based on a shaky foundation and then added a bunch more sugar. It might as well be a self-help movement which could spawn coaches, authors courses and certifications.
Sometimes things are even more complicated than you already believe them to be. And it may take more than just a replication study to say a study is wrong or fraudulent.
If it doesn’t replicate, it should be relegated to “less than observational” level evidence. Why would you believe that the assumed intervention had the claimed effect, if it doesn’t have it when you try again?
If it fails to replicate certain statistical outcomes, things are more complicated.
The amount of times I read about spectacular counter-intuitive study that "changes everything" and later find out it was fraudulent or failed to replicate makes me really doubt all of my knowledge I have from reading about psychology.
Reproducibility crisis is real and it sucks for all the people that read individual books and don't know about reproducibility. I feel people that write those books should also put themself into higher standards and stop writing narrative books based on individual nonreplicated studies.
You can read about reproducibility crisis here: https://www.theatlantic.com/science/archive/2018/11/psycholo...