Re:Scientists rise up against statistical significance
by kgwgk on Thursday March 21, 2019 @11:30AM
(#19453455)
> I did not mean to imply that each value in the interval is equally likely (and looking over my comments, I do not think I did imply that).
When you said that "the values at the boundary of your interval are as credible as in the center" you kind of implied that, which is why I asked.
I won't defend the article being discussed, but you opposed their statement that "the values in the center are more compatible than the values at the boundary" with an equally ill-defined "the values at the boundary are as credible as in the center".
Re:Scientists rise up against statistical significance
by BeetleB on Thursday March 21, 2019 @12:11PM
(#19453992)
I do not read my statement to imply uniform distribution.
What I meant was "there is no reason to prefer values at the center more than values at the boundary" based on the CI (there may be external reasons, though). To me, this is equivalent to your:
>there is nothing that can be said about the likelihood of each value
Re:Scientists rise up against statistical significance
by kgwgk on Thursday March 21, 2019 @12:25PM
(#19454157)
Ok, we agree. "As credible as" implies uniform "credibility". "More compatible than" implies non-uniform "compatibility". Without any clear definition of "credibility" or "compatibility" it's impossible to interpret precisely what are those claims supossed to mean.
Yes - sorry if I wasn't clear. I did not mean to imply that each value in the interval is equally likely (and looking over my comments, I do not think I did imply that).
The complaint is that the article is stating otherwise as fact.
>One practical way to do so is to rename confidence intervals as ‘compatibility intervals’
>The point estimate is the most compatible, and values near it are more compatible than those near the limits.
They simply are not in a frequentist model (which is the model most social scientists uses). I agree with the main thrust of the article in that there are many problems with P values. But I am surprised that a journal like Nature is allowing clearly problematic statements like these.
I don't know enough about the Bayesian world to be able to state if his statement is wrong there as well, but if it is correct there, it is problematic that the authors did not state clearly that they are referring to the Bayesian model and not the frequentist one.
(Not to get into a B vs F war here, but I remember a nice joke amongst statisticians. There are 2 types of statisticians: Those who practice Bayesian statistics, and those who practice both).