deletions | additions       diff --git a/In_a_multisite_model_we__.tex b/In_a_multisite_model_we__.tex  index 84618fe..acba84f 100644  --- a/In_a_multisite_model_we__.tex  +++ b/In_a_multisite_model_we__.tex 
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In a multisite model, we are sampling effect sizes from our set of sites, and then taking an average of those samples and testing whether or not this average effect is significantly different from 0. Becuase of this, its really important to have enough site-level samples to estimate a mean effect. The plots don't go down to the single site case because the power curves would not apply there - the model would be different. In a single site case, one simply needs to power a two sample T-test, given an effect size, number of subjects, false positive rate. If similar parameters are taken to a single site case (effect size=0.2, alpha=0.002, power = 80\%), one would need 1550 subjects, all acquired at one site, to power this. However, it takes a really long time to acquire that many subjects for one site, and it is likely the scanner will go through upgrades, or protocols will change in the meantime. The n cutoff (number subjects per site for our 20 sites) that was chosen for this plot was 150 subjects per site, which is the maximum number we would ask our consortium to collect. Ideally this would be even lower, especially if researchers wanted to study very rare diseases. At a certain point, even with 0 variability from MRI, there are not enough sites foran  effect sizes that are so small (which is the case with genetics) and this is why the # of sites do not go below 10 for this particular effect size ($<10$ samples is not enough for 80\% power, even with no bias).