I blame language. As with all models, the further we move from blissfully perfect abstraction the less certain we can be about our model's predictions. But ou

I blame language. As with all models, the further we move from blissfully perfect abstraction the less certain we can be about our model's predictions. But our language isn't very lenient with expressions of uncertainty. We speak about a single psychology study's findings with the many of the same words of concrete certainty that we do with the deepest mathematical theorems because that is what is most natural. We say something is true when we really mean that we're pretty sure something is true under certain conditions. We assume that everyone understands the touch of chaos underneath the words based on the context. But they don't. And we don't either. We know better but we fool ourselves more or less by accident and repetition and it shakes us when contradictions inevitably arise from messy reality. And on the other end of the spectrum, if any uncertainty at all is spoken of it seems to cast the whole statement into doubt. The language of statistics and uncertainty is cumbersome and unnatural. If I could wish for anything and have it come true for the sake of advancement of humanity it would be for a standard modifier or tense for levels of uncertainty. I would trade that increase in complication for the reduction of confusion.