A deeper cause there, as I understand it, is that the statistical analysis was incomplete/flawed but in a way that none of the technical practitioners caught an

A deeper cause there, as I understand it, is that the statistical analysis was incomplete/flawed but in a way that none of the technical practitioners caught and called out at the time. The way the data was presented was to the effect of "half of the o-ring damage incidents were in cold temp launches", which made things seem less bad. The way it could have been presented was more like "nearly every cold temp launch has resulted in damage, only like 10% of others do" (I forget the exact numbers) which is far more effective highlighting the impact of the temperature and the magnitude of the increased risk. And that would've let people make a stronger case "hey, we know temps in the 50s cause damage almost every time, and it's way colder today." There's a "system" aspect (outside of power structures and incentives) which is that prob/stats knowledge among almost all engineering disciplines (industrial engineering waves from the sidelines) is exceedingly poor and often viewed as "soft" and "less important" than calculus, linear algebra, etc. And so practitioners are ill-equipped at spotting things like that ("wait, is this the right denominator? what about frequency of incident?").