Robert,
Everybody? Not me. I'd teach everything in terms of effect sizes. Then we get to
argue over what are large/small effect sizes.
rick
--- You wrote:
> let's say that today ... we as the statistical community decided, by
> democratic vote, that the concept of 'hypothesis testing' ... which has
> essentially dominated statistical work for as long as i can remember
> (which, .... er um ... is a LOT of years!) ... is relegated to the 'we
USED
> to do this stuff' category
> just THINK about this ....
>
> what would the vast majority of folks who either do inferential work
and/or
> teach it ... DO????
> what analyses would they be doing? what would they be teaching?
Everybody would use Bayesian inference. Of course:
* students would be told in their compulsory intro stats that
"a posterior probability of 95% or greater is called
"statistically significant", and we say 'we believe
the hypothesis'. Anything less than that is called
"not statistically significant", and we say 'we disbelieve
the hypothesis'".
* most elementary textbooks would start out by saying, for a chapter or so
"Use a prior probability of 50%." Then the better ones would point out that
one can use other priors, and set exercises running roughly as follows:
"A researcher believes that the prior probability of
a certain food causing cancer is 61%. She feeds the
food to a group of 10 mice, ..."
Then they would go back to using 50% in the next chapter.
* Editors would similarly use a Bayesian posterior probability of 95% as
a criterion for publication. 94% doesn't cut it - see above.
* Statisticians trying to tell colleagues in other departments that "you
can't just make up a prior to give the right result" would be told "that's
how we've always done it in Necrophiliac Studies, it's a long-standing
convention".
-Robert Dawson
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