Many years ago, I read that when estimating standard deviations in power
analysis of the two-sample t-test, one should take a larger value for the >treatment group than the control group (which can usually be found in the >literature) because in biological experiments intervention often disturbs
the steady state or, for observational data, the sick group can be expected >to be less homogeneous.
I can no longer locate that reference and wonder whether anyone here can
help me by directing me either to an appropriate source for such a claim or >to actual published data.
Any help would be greatly appreciated.
On Sat, 9 Jul 2016 13:51:45 +0300, Norman B. Grover
<norman@md.huji.ac.il> wrote:
Many years ago, I read that when estimating standard deviations in power
analysis of the two-sample t-test, one should take a larger value for the >treatment group than the control group (which can usually be found in the >literature) because in biological experiments intervention often disturbs >the steady state or, for observational data, the sick group can be expected >to be less homogeneous.
I can no longer locate that reference and wonder whether anyone here can
help me by directing me either to an appropriate source for such a claim or >to actual published data.
Any help would be greatly appreciated.
Well, Jacob Cohen wrote the book on power analysis for the
social sciences. That would be the first place that I would look.
Cohen does mention practical considerations, so that might be
one of them. The consequence of knowing that variances are
unequal is that you have reason to depart from the usual practice
of sampling equal Ns in the two groups.
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