In academic papers, is it really a bad thing to not report the relatively not good results? I am going to submit a computer science paper to a conference. So the thing is fairly simple. On some datasets with 1:1 ratio, our model achieves really good performance. On datasets with 1:5 or 1:10 ratio, our models only reaches mediocre results.
I just want to report the results of datasets with 1:1 ratio in this case since they are pretty good. Putting a mediocre result (on the 1:5 or 1:10 ratio datasets) may extremely lead to a whole paper getting rejected. However, my PhD advisor told me that we have to put the mediocre result as well.
Meanwhile, the PhD advisor seems really angry about this behavior and disappointed about me, and I feel really sad because I didn't cheat, and I just not want to include the relatively mediocre results in my paper.
I think many other computer science papers do something like that, not reporting their results on some datasets if the result is bad, and reporting their results on some datasets if their result is good.
I don't get why my PhD advisor is so angry about that. Could someone tell me? I am really appreciated it.
Currently, I feel wrongly judged by my PhD advisor and afraid of the whole paper getting rejected because of this since we are going to submit to a top-tier conference.
Research is a search for truth, not for happy talk. If you have evidence of some behavior then you should be honest and report it just as it is. Don't try to dress it up.
Giving only the &quot;best&quot; results will sometimes mislead people and will cause them, perhaps, to waste time and effort.
You are currently the best source to give an honest and complete review of the meaning of your results. I strongly suggest you do that. At some level, not doing that is malfeasance.
I suggest that the reviewers themselves probably think like I do and won't reject a paper that is honest and complete, other things being equal. They aren't looking for happy talk either.
But you may also have an opportunity to investigate why you get different outcomes. Is it something about your model, or something special about the data? Inquiring minds want to know.