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Comment by hannob | Hacker News Reader

> I can't blame researchers for tailoring their research to help their own careers but this should help prevent that.

It regularly astonishes me how easily people are willing to accept scientific malpractice with such excuses.

While I agree that mechanisms like registered reports are the way to fix these things at the core, I do think that these mentalities ("it's just the system, I can't do anything but cheat with my scientific publications, my career!") are a big part of the problem.

Good read on the issue: http://www.talyarkoni.org/blog/2018/10/02/no-its-not-the-inc...

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> It regularly astonishes me how easily people are willing to accept scientific malpractice with such excuses.

I get that. But the other side is that we pump up a lot of kids about science, then filter for very ambitious, very dedicated people and shove them into a resource-constrained environment where some kinds of results are much more strongly rewarded than others. It's absurd to set up a system like that and expect that science will be done to the level of frankness that you (and I) want.

I know some really smart, hardworking, very honest PhDs who have shifted to the tech industry because the roll of the research dice didn't come up right for them early in their careers. They are far from happy about that. I wish they could have stayed in science.

And I'd note that the person you're replying to is not "accepting scientific malpractice". They're saying they can't blame people for trying to survive in the system that they're caught in. If you want to be mad at somebody, be mad at the people who have set up the system, who could change it but don't.

It's unscientific and illogical to expect humans to sacrifice their lives on the altar of your belief: statistically, that's not what they're like. Especially in a system that filters out the most honest, least career-focused people early on.

I think one of the problems is that it often doesn't feel like cheating. You have a hypothesis, but you don't preregister it. It lives only in your head. You do an experiment, you look at the results. They fail to confirm the hypothesis. But if you tweak the hypothesis just a little, the data suddenly confirm it. So why not publish the tweaked version?

This is a malpractice that effectively invalidates the research. But it doesn't feel so. It feels more like a thought-crime.

> You do an experiment, you look at the results. They fail to confirm the hypothesis. But if you tweak the hypothesis just a little, the data suddenly confirm it. So why not publish the tweaked version?

If between tweaking the hypothesis and publishing it, you add the step "you perform another experiment which tests the tweaked hypothesis", you have just described the scientific method.

I'm sure the budget for this new experiment will be given straight away, no questions asked.

Though of course, there's a difference between "drug A doesn't work for condition B but seems to work slightly for C" and "drug A doesn't work for condition B but 10 of 12 individuals with condition C have shown significant improvement"

> I'm sure the budget for this new experiment will be given straight away, no questions asked.

Most of the cases: yes, although your example of clinical trials is slightly different, and in that case, I do think the data should be publicly available to other researchers even in the case of a null.

If the original experiment was large enough [1], you could almost always find some C such that "drug A doesn't work for condition B but 90% [2] of individuals with condition C showed significant improvement".

In fact, you could replace [2] by a number arbitrarily close to 100% by increasing [1] accordingly.

If the original experiment was large enough to do that, then somebody was given way too much money for the original experiment. So I'd expect that's a very rare case.

>> But if you tweak the hypothesis just a little, the data suddenly confirm it

This is 'data mining' right? And I've occasionally wondered about this, since I don't work in a scientific field but did once make use of the scientific method for some research I did. And yes the findings weren't especially conclusive but I'm not sure I could've tweaked the hypothesis to make it work.

So, had I found something really interesting that didn't fit the hypothesis, is the 'right way' to conduct a new experiment from scratch? So say I did that, and used the 'tweaked' hypothesis, of course I'd find something interesting, because it's already there.

In this new 'pre-registration' framework, how can I correct the problem and pursue the interesting idea but keep the science in-tact? Because, if I used some sort of cross-validation at the outset and I have all the data available I presumably can't change the sample, so the hypothesis presumably has to change.

Refining an experiment is not wrong. What is wrong, like you say, is going on a fishing expedition until you find a result you like.

There are methods to account for follow-up experiments. Bonfaroni correction [1], for instance, requires you to increase your significance level with each new test.

[1] https://en.m.wikipedia.org/wiki/Bonferroni_correction

You can also split the dataset into two parts. Use first part to form a hypothesis. Register it. Then use the second part to confirm/disprove it.

It's harder than you might think to control for multiple comparisons. The Bonfaroni correction assumes that each experiment is independent, and so penalises correlated experiments unnecessarily harshly.

On the other hand, other tests typically require the researcher to make explicit assumptions on the correlation structure of the experiments despite the fact that it is not directly observable.

You are probably thinking of Sidak correction when you state independence is needed. Bonferroni correction does not need independence. You are absolutely right about Bonferroni being a severely conservative correction though -- at least the 'first order' one that uses only the first term of the Bonferroni inequality. One can take more terms to be less conservative but those aren't as easy to apply as you need to know the joint distributions over larger and larger tuples of events.

Another more recent technique for 'exploratory' yet correct technique is to exploit differential privacy and dithering.

>This is 'data mining' right?

That would be datamining done wrong. Its perfectly fine to look at data to provoke new hypothesis. But you should not be using the same data to confirm the hypothesis that it provoked. Either use fresh data or make sure that you still ensure correctness if you are reusing the data.

It can be much more sneaky than this.

You have an hypothesis, do an experiment, it fails. You mark the hypothesis false and move on, never putting the work into publishing it (why would you?).

At the same time, 19 other researchers have the same idea. Some 18 of them do the same as you, but one does the experiment and get a success. He will publish his work (why wouldn't he?), and it will be the only piece of literature available about the subject.

Where on this narrative did anybody do anything even remotely unethical?

> "It regularly astonishes me how easily people are willing to accept scientific malpractice with such excuses."

Agreed in theory. In practice such don't even qualify - by definition - as science. Using the word science in such contexts gives such things far more credit and legitimacy than they deserve.

Whether you blame researchers or not is inconsequential. People respond to incentives with a spectrum of responses from incredibly principled to entirely unprincipled. If the unprincipled are rewarded and the principled are penalized, the principled will leave the field for something else. Moralize all you like but the incentives define the field.

It regularly astonishes me how easily people are willing to accept scientific malpractice with such excuses.

Likewise. The BBC reports that 2/3 of scientific research cannot be reproduced. Let’s be honest about what that means: 2/3 of scientists are fraudulent or incompetent.

https://www.bbc.com/news/science-environment-39054778

If we want to give people the benefit of the doubt, it could also mean that scientific research is difficult and even competent individuals often make mistakes.

This is incorrect and unhelpful. Science in fields with lots of complexity, such as neuroscience and psychology, suffer from hard problems such as unmeasured causal variables and divergent sample characteristics. You might chalk this up to incompetence, but there are are in fact real concerns of feasibility. You cannot hope to measure or control all relevant cognitive factors, you just try to balance your sample groups on the more obvious measures, increase your sample size, then hope that other differences average out with random assignment.

To put it otherwise: for some problems, none of the 7 billions humans alive is competent enough right now. For some of them, we'll collectively eventually get there, if we don't stop trying.

> Let’s be honest about what that means: 2/3 of scientists are fraudulent or incompetent.

No, it means that 2/3rds of the attempts to reproduce research fails.

When you work on the very edge of science, the lab you're working on it probably either the only one, of one of a very small number who are even able to perform the research that you're doing, due to a combination of highly specialised equipment, subject knowledge and just plain experience. Without that combination of factors, it's quite easy to mess up an experiment and therefore fail to reproduce the research.

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