Reply-To: | | [log in to unmask][log in to unmask]> wrote: >>> >>> Roberto, >>> >>> Sorry if this isn't addressing your points; the original poster's >>> question was a little unclear to me, because I wasn't 100% sure what >> >> she >>> >>> meant by "multi-masking." >>> >>> All I mean is that if you use a mask defined by the same functional >> >> data >>> >>> that you're applying the mask to, the significance will possibly be >>> inflated. >>> >>> As for definitions, an example which is somewhat conceptually related >> >> is >>> >>> stepwise regression. The paper at >>> http://publish.uwo.ca/~harshman/ssc2006a.pdf >>> states, "When model modifications are selected using post-hoc >>> information (e.g., in stepwise regression) standard estimates of >>> p-values become biased." Ultimately, I think my use of "bias" here is >>> correct, based on definitions given at Wikipedia. >>> >>> So, what I'm saying here is that the use of a functionally defined >> >> mask >>> >>> can lead to corrected p-values which are likely to be too small. The >>> simplest example is using a contrast to mask itself. The uncorrected >>> p-values are obviously unaffected by this procedure. And the >> >> corrected >>> >>> p-values are obviously decreased. >>> >>> My comment "the mathematics dictates that there is no bias": I mean >>> that I assume that there are situations where there's enough >>> independence (e.g, perhaps between the masking contrast and the >> >> contrast >>> >>> you're masking) that the bias either doesn't exist or is probably >>> negligible, but I haven't had time to think up rigorous examples. >>> >>> Best regards, >>> >>> S >>> >>> -----Original Message----- &gºZ|t |