Rather than wanting to confirm priors, I believe this usually is a problem with neither the PM nor the data scientist ensuring that the problem formulation is good enough before diving in. I.e., what data would be needed to actually test the hypothesis? Do we have that data or not? Is the hypothesis even formulated in a way to be falsified in theory?
I've seen so many analysis tasks where data scientists without questioning went away for a few weeks to crunch data and come back with some random graphs and statistics that are completely useless as decision support.
You're overthinking it. Executives and managers quite literally want to see data that confirms their existing convictions and beliefs so they can act on those beliefs under the guise of it being "data-driven".
I've seen so many analysis tasks where data scientists without questioning went away for a few weeks to crunch data and come back with some random graphs and statistics that are completely useless as decision support.