I've often been called upon to do a t-test with 5 animals in one group
and 4 animals in the other. The power is abysmally low and rarely do I
get a p less than 0.05. One of the difficulties that medical researcher
have is with the notion of power and concomitant sample size. I make it
a point of calculating power especially where Ho has not been rejected.
It gives the researcher some comfort in that his therapy may indeed be
effective. All he needs for 0.8 power is 28,141 rats per group.
[EMAIL PROTECTED] wrote:
>
> In article <[EMAIL PROTECTED]>,
> [EMAIL PROTECTED] (dennis roberts) wrote:
>
> >
> > thus, the idea is that 5% and/or 1% were "chosen" due to the tables
> that
> > were available and not, some logical reasoning for these values?
> >
> > i don't see any logic to the notion that 5% and/or 1% ... have any
> special
> > nor simplification properties compared to say ... 9% or 3%
> >
> > given that it appears that these same values apply today ... that is,
> we
> > have been in a "stuck" mode for all these years ... is not very
> comforting
> > given that 5% and/or 1% were opted for because someone had worked out
> these
> > columns in a table
>
> I agree, and I think perhaps that although the original work focused on
> the 5% and 1% levels for practical reasons, the tradition persists b/c
> it provides a convenient criterion for journal editors in deciding
> between 'important' and 'unimportant' findings. Consequently, to
> increase the chances of being published, researchers sometimes resort to
> terms like "highly significant" in referring to low p values, which is
> really a quite nebulous statement (if not completely misleading- I shall
> leave that determination to the experts). To me, it seems that less
> emphasis on p values per se and more emphasis on power and effect size
> would increase the general quality and replicability of published data.
>
> Chris
>
> Sent via Deja.com http://www.deja.com/
> Before you buy.
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