Here's how you can resolve conflicts between statisticians and research collaborators.

Always involve a statistician at the planning stage to allow disagreements to be aired when they are still solvable. This has the added benefit that it normally improves the project because the statistician can add their insights, and have better understanding of the constrainsts other scientists are working under. Be clear on what the goals are, and which are most important to the researchers. My experience suggests that most disagreements occur either due to fundamental misunderstanding of the others speciality, or due to costs or other constraints. Good communication, and good relationship building from an early stage can help.

In my experience leading AI and ML projects, aligning goals at the onset can significantly reduce conflict later on. Early in my career, I learned the hard way that unspoken assumptions can lead to major disagreements. Thus, I always start with a structured kick-off meeting where every participant's expectations and the overall project objectives are discussed openly. I use visual aids like flowcharts or project roadmaps to ensure clarity. This practice has not only pre-empted many potential misunderstandings but also helped in building a foundation of mutual respect and understanding.

For the researcher: Develop the specific question(s) that you would like to answer and share them with the statisticians. This will help guide the statisticians to assist you to design specific experiments to test your hypothesis/hypotheses.

One thing I have found helpful in open dialogue that it's creates confidence in all people involved either collaborators, Statisticians or non statistical collaborators. Secondly, it helps determined the needs easily during

When communication breaks down, I've found it helpful to use analogies to simplify concepts and provide examples to develop a shared understanding/mental model. From that shared understanding, you can layer-on more elaborate questions/analyses.

The number one question I receive from my statistician colleagues is: What is the question you are trying to answer? This clearly delineates our roles as researcher and statistician. The researcher must utilize their background and expertise to identify gaps in knowledge and develop appropriate questions and hypotheses to test. The statistician uses their expertise to help determine the appropriate type of analysis to conduct the test of the hypothesis.

Una minima capacitación puede ayudar a todo un equipo a interpretar los datos de forma precisa y le puede permitir a todo un equipo dialogar con otro en este area del conocimiento.