It's unfortunate that this is getting downvoted. Perhaps it comes across as a little boastful, but it matches my experience in academia. When I was doing my Ph

It's unfortunate that this is getting downvoted. Perhaps it comes across as a little boastful, but it matches my experience in academia. When I was doing my PhD (Statistics) I spent several weeks running simulation studies to compare our new statistical model to 2-3 existing models that did similar things, across a wide range of data sets both real and generated with varying parameters. It was an unsupervised learning problem so there wasn't a "correct" answer to compare to on the real data sets, but on the simulated data we outperformed the existing models over about 60% of the parameter space that we tested, including the part of the parameter space that I thought was most useful/likely in practice. Rather than present all the simulation results and have a nuanced discussion of where we did better and where we did worse, my advisor had me remove the 40% of the parameter space where we did worse from the results, so it looked like we always did as good or better. The scientific contribution would undoubtably have been better if we had presented the results where we did worse and discussed why we believed that to be the case, but getting the paper past reviewers and into a top journal (as we eventually did) was considered more important.