Flawed findings are not complete trash
Nobel Laureate Peter Doherty puts it this way,
"You know, everyone in complex science and I do immunology, is a very complex science. Everyone gets things a bit wrong sometimes. You can get it wrong or you can get it half right, and then new technology comes along or new discovery. And you realize that your data was fine, the way you interpret it wasn’t correct and so you know you really should, I think scientists really should, when, then happens they should write something short. There should be a journal called “Getting it Wrong in Immunology” or “Getting it Wrong in Biology” or “Wrong Biology” and you should, everyone should write these stories and say, this is why I got it wrong. This is how I got it wrong because… it’s useful to know that. It’s useful for the history of science, it’s useful for young people to know that. It’s useful for young people to know that if you do science and you do complex science, you are going to get it wrong some of the time. And if you are not getting it wrong you are just doing boring experiments basically. So I always, if I have graduate students and all their experiments are working, we kind of threaten them. We are going to come in and contaminate your cultures at night when you are not here. We never do it but you know just, just so they get the experience. Because you know, at every time in someone’s scientific career, things look great and then they suddenly are terrible. And you’ve got to be able to deal with that so if you are going to be a scientist, you have to be emotionally robust. "
It's okay to have the "BAD" data seen by everyone; which methodologies led to those (unfavorable) results should help understand another group of researchers from not doing that.
The problem with common people these days is that they simply don't think. "I have not failed. I've just found 10,000 ways that won't work."__Thomas A. Edison
If you knew any of those 10,000 approaches or data sets, that could have led to another discovery. We, therefore, encourage scientists and researchers to publish failed experimental graph, data sets or an abstract.
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