IMO the expression ‘statistical significance’ is a big part of the problem. Popular reporting of research translates a tiny but discriminable effect size into a SIGNIFICANT effect.
Changing the nomenclature to ‘statistically discriminable’ would go a long way to improving popular understanding.
> Changing the nomenclature to ‘statistically discriminable’ would go a long way to improving popular understanding.
I think you overestimate the intelligence of people prone to misunderstanding. I don't think they're likely to understand big words like "statistically" or "discriminable" when they already don't understand big words like "statistical" or "significance".
when they already don't understand big words like "statistical" or "significance".
They do understand what "significant" means, they use the world every day. The problem is that it means something different than the intuitive every day meaning when used in context of p-values. Using a word like "discriminable" might help clear things up since it's a word that doesn't have have so much meaning packed into it already.
It's like when a mathematician says that something is "almost always" true, they mean something very different than when a non-mathematician says something is almost always true.
> The problem is that it means something different than the intuitive every day meaning
Yes that's my point. Using words that are hard to understand or only understandable in the context of P-values won't solve the problem; the problem being that the general public won't understand the subtle differences in meaning
I'm not sure. I think much of the confusion comes from people instinctively applying their every day understanding of the word "significance" and falsely believing they understand what it means in the statistical context. Had it been called statistical confliburance or some other made up term then they wouldn't think they knew what it meant and might find out what it actually meant.
