I've found this to be the rule, not the exception. Pointing out extremely-obvious (to me? Maybe I'm just unusually good at it? I don't even have much formal sci

I've found this to be the rule, not the exception. Pointing out extremely-obvious (to me? Maybe I'm just unusually good at it? I don't even have much formal science training, and hell, barely any math training by the standards of HN folks, though) damning errors in experimental construction that should invalidate the whole thing won't earn you any friends, even if you do it before the work is undertaken, and even if you're telling the person who's claiming to want good data and useful results. Everyone seems to just want a veneer of science to what they're doing, not actually good efforts at it. As long as you have a paper-thin layer of justification that falls apart if anyone looks at it long enough, that's considered good enough and people will sit around in meetings nodding along. Of course, in many situations the business totally lacks what it needs to correctly do the "data-driven" stuff they want to, and it'd take a good deal of up-front effort by competent people to get it, amounting to entire new projects or deep modification of existing projects. So, given the choice between: going without that stuff and acknowledging that a lot of what they're doing is guesswork and gut decision making, or simply arbitrary; putting a smaller but still-large amount of work into finding out what they can glean from what's available; spending the time and money to collect what they need, the right way, to do the data-driven decision making they claim to want to do; and insisting they're doing things "data driven" but having all their data hopelessly ruined by e.g. selection bias and comically-bad experimental construction that can't possibly be yielding reliable results, so they can cheap out and get no actual "data-driven" benefits aside from falsely claiming that's what they're doing—they tend to go with that last option, nearly every time!