No, it means find a result in the same direction.
Chris Green
===========

Michael Smith wrote:
>
> I'm not sure. But if the "probabilty of replication" means the same as 
> finding a statistically significant result by repeating an experiment 
> exactly as before, then the probability of such a replication is 
> exactly 50% (assuming an alpha of exactly 0.05)
>  
> --Mike
>
> */"Wuensch, Karl L" <[EMAIL PROTECTED]>/* wrote:
>
>     I don't understand why the "probability of replication" is of
>     any importance. You can "replicate" any effect given sufficient
>     power/N. N is not held constant across different calculations of
>     p-rep,
>     is it? What of value does p-rep give one that is not already in hand
>     when a confidence interval for the effect size is provided?
>
>     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>     Karl L. Wuensch, Professor and ECU Scholar/Teacher, Dept. of
>     Psychology
>     East Carolina University, Greenville NC 27858-4353, USA, Earth
>     Voice: 252-328-9420 Fax: 252-328-6283
>     [EMAIL PROTECTED]
>     http://core.ecu.edu/psyc/wuenschk/klw.htm
>
>     -----Original Message-----
>     From: Marc Carter [mailto:[EMAIL PROTECTED]
>     Sent: Friday, November 16, 2007 12:04 PM
>     To: Teaching in the Psychological Sciences (TIPS)
>     Subject: RE: [tips] p-rep, mathematical error, pure and simple < Pro
>     Bono Statistics
>
>
>     Forgive this horribly un-sourced response, but there is (and has been,
>     nearly from the get-go) a debate about whether p(rep)'s
>     assumptions are
>     correct in its original formulation. The problem has to do with (iirc)
>     the estimation of effect sizes, which are required for computing
>     p(rep).
>     (Those are the lower-case deltas, I believe, in Gat's blog post.) Gat
>     adds in a later post
>
>     onal-science/> that the error has been pointed out already.
>
>     I'm not statistician, but I suspect that this is going to work itself
>     out. The idea is fundamentally sound, and if it's possible to make
>     reasonable assumptions about effect sizes (or the distributions of
>     that
>     variable), then one *should* be able to compute the probability of a
>     replication. I'm waiting for things to calm down a bit and for people
>     far smarter than me to figure it out. Again, I'm not a statistician,
>     but I don't see immediately why that difficulty kills the idea.
>
>     I'm talking to my stats classes about it so that they'll be aware that
>     it's probably something they're going to see in the future (and if
>     they
>     read APS journals, they'll see now), but I'm not teaching it, per se.
>     It doesn't seem to me that anyone has launched a devastating
>     critique of
>     the idea of it, but rather have found problems with the computation of
>     it.
>
>     m
>
>     ------
>     "There is no power for change greater than a community discovering
>     what
>     it cares about."
>     --
>     Margaret Wheatley
>
>     -----Original Message-----
>     From: Christopher D. Green [mailto:[EMAIL PROTECTED]
>     Sent: Friday, November 16, 2007 9:28 AM
>     To: Teaching in the Psychological Sciences (TIPS)
>     Cc: Robert A. Cribbie; David Flora; Michael Friendly; Doba Goodman
>     Subject: [tips] p-rep, mathematical error, pure and simple < Pro Bono
>     Statistics
>
>
>     There has been a lot of excitement around a statistic being used
>     in APS
>     journals of late called p-rep. It was developed by Peter Killeen,
>     published in a 2005 issue of Psychological Science. The primary
>     advantage claimed for it is that it gives the average probability of
>     replicating a given effect in future studies, rather than giving the
>     probability, under the null hypothesis, of finding data at least as
>     improbable as that actually found (which is roughly what good ol'
>     p-values give).
>
>     But now I have found a blog posting by a California statistician named
>     Yoram Gat that claims that Killeen's derivation is based on a
>     "mathematical error, pure and simple." You can find the posting at:
>     http://probonostats.wordpress.com/2007/09/15/p-rep-mathematical-error-pu
>     re-and-simple/
>
>     He details what the error is, but I am not statistically sophisticated
>     enough to know whether or not he is correct. Has anyone else come
>     across
>     this? Is Gat right? And, if he is, is the error as devastating to
>     p-rep
>     as he claims?
>
>     Regards,
>     Chris Green
>     York U.
>     Toronto, Canada
>
>
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