Need some advice. We are doing a series of tests looking for correlations
among age-sensitive variables in a population of mice. We will have about
600 mice in all, and it will take 3 years to test each mouse at about 200
mice tested each year.
We are considering three strategies:
A) Wait 3 years until all the data are in; then do the analyses.
B) Analyze the data on the first 300 mice, and publish anything that looks
exciting and meets conventional significance criteria. When the second set
of mice is finished, we can use these second 300 animals as a replicate
samples to (try to) confirm the significant findings we reported on the first
set. And we can also pool all 600 mice to obtain higher statistical power
than we had for the initial analysis with N = 300.
Of course this represents testing some hypotheses twice, and thus increases
the Type I error rate. I suspect that there are theoretically justified
methods for adjusting significance criteria to "adjust" for taking two looks
at the data, but I don't know how to do this. Anyone have a recipe, or a
reference to get me started?
Thanks.
Rich Miller
University of Michigan
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