This is a malpractice that effectively invalidates the research. But it doesn't feel so. It feels more like a thought-crime.
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.
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.
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.
Another more recent technique for 'exploratory' yet correct technique is to exploit differential privacy and dithering.
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.
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.
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"
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.
In fact, you could replace [2] by a number arbitrarily close to 100% by increasing [1] accordingly.
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?