Statistics is borderline pseudoscience. Various techniques and methodologies are rigorous, but when applying them to complex real world problems, there is a necessary element of human interpretation. With that interpretation it's trivial to twist most scenarios towards either outcome.
I spent several years doing stats/BI in the medical manufacturing industry and have on many occasions done analyses that showed what my employer wanted and would stand up to scrutiny, but if my employer wanted the opposite outcome of that analysis, that would also be possible and also stand up to scrutiny.
The best way to address this is to remove any financial/business incentives form the analysis, but that's not really feasible most of the time and also requires larger/more expensive teams that are capable of internally challenging their own work.
Despite the title, it's mostly an essay on why visualization and presentation are critical to any statistical treatment.
I think it's one of those difficult skill combinations. You want someone extremely well-versed in statistics, plus someone capable of giving a damn about accurate presentation, plus someone with the UX intuition to make the correct choices to make something readable without sacrificing accuracy and important nuances. And if that "someone" is actually two people, you run the risk of telephone misunderstandings as work passes between them.
Which is a tall order.
Made taller by the (as you note) different kinds of audiences: simply ignorant, aggressively biased, rushed, etc.
I spent several years doing stats/BI in the medical manufacturing industry and have on many occasions done analyses that showed what my employer wanted and would stand up to scrutiny, but if my employer wanted the opposite outcome of that analysis, that would also be possible and also stand up to scrutiny.
The best way to address this is to remove any financial/business incentives form the analysis, but that's not really feasible most of the time and also requires larger/more expensive teams that are capable of internally challenging their own work.
https://en.wikipedia.org/wiki/Lies,_damned_lies,_and_statist...