How to deal with an adviser that wants to force you to get his desired results? I'm doing research for my PhD thesis based on a sensitive subject related to biomedical applications. In fact, our workflow is:


Build a computational model
Verify the developed computational model based on tests and data available in the literature
Apply this developed and verified model to some other data to measure an important parameter for clinicians that people's life will depend on that.
Investigate the result of this application on that data as well as its outcome and relevance for clinicians.


My problem is the fourth stage in this workflow. First of all, nobody ever did the fourth stage in this workflow for that particular application before. There are some similar models in the literature that tried to investigate the outcome of applying a similar model for that application but their conclusions are so general and vague where a definite conclusion cannot be drawn. When we apply that developed and verified model to that data, it produces some results which may look counter-intuitive at the first place, but there are a few papers in the literature that actually confirm similar observations. These results are not bad but kinda look like a negative result. We are confident in our results because this model is validated and verified based on several independent cases.

Unfortunately, in my PhD adviser's eyes, these results are worthless cause they are not desirable and he thinks nobody will buy this results if our conclusion is something counter-intuitive (well counter-intuitive based on his thoughts at least...). Every week in our group meetings, he reminds me that these results are worthless and I should change the developed model in a certain way. He doesn't give me direction regarding what way I should change the model, but it is important that we get intuitive results right now.

I'm feeling like he is forcing me to search for his desired results. It is possible for me to do that but I believe that's cheating or could be called hiding the truth. My question: Should I change my model to get his desirable results? if no, what's the proper way to convince him that this counter-intuitive results maybe is the truth and we should live with them?
The meaning of unexpected results

It is important to be skeptical about your computational approach. However, at the same time, computational approaches are (almost) completely worthless if we just ignore them when results are unexpected (unless you already have dominant evidence that the result is not only unexpected but also simply wrong). An exception would be if your approach is in the area of generative models, where a parsimonious model is suggestive of an underlying mechanism, which is not the area of your model: you are trying to do prediction for an unknown case (extrapolation).

The art is in determining whether your initial model of the world (i.e., expectation) is wrong or if your computational model is wrong.

In a long discussion in chat, I think we came to a conclusion that in your specific case, it may be that this is an issue of extrapolation to a condition where you do not have truly comparable training data.

How to stop worrying and learn to love the model

If you want to convince your advisor, colleagues, peer reviewers, or yourself that your model should be trusted, your next steps are to test the conditions that lead to your result.

Do all the appropriate tests for model convergence in the original training. Check for input parameters that are outside the range in the training set. Use graphical representations of your model to show how input are mapping to outputs. Remove or scale variables to test the sensitivity of your model to those changes. And additionally, as your advisor suggests, figure out what it takes to make your model fit the expected result. All of these approaches will help you find if something is wrong in the model or support you if something is wrong in the prior expectations.