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Seconded.
I'd be interested in hearing about this, too. It
seems to me that the computation of power has to make assumptions about the
shape of the distribution of the dependent variable (power is essentially a
measure of area of the distribution of the variable -- under the
alternative hypothesis -- above the criterion), and so if we cannot make
assumptions about the character of that distribution (that's why they're called
"distribution-free stats"), I'm at a loss to figure how we'd compute its
area.
I'm wondering if there's some way to bootstrap a
distribution based on the data, generate a function to describe it, and then get
about integrating it.
But, as often happens, I could be wrong and would really
like to know.
m
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- [tips] RE: Nonparametric Effect Size and Post-Hoc Pow... Marc Carter
- [tips] RE: Nonparametric Effect Size and Post-Ho... Christopher D. Green
- [tips] Re: Nonparametric Effect Size and Post-Ho... Blaine Peden
- [tips] RE: Nonparametric Effect Size and Post-Ho... Marc Carter
- [tips] RE: Nonparametric Effect Size and Post-Ho... Michael Scoles
