Because their goal is to determine if some sort of treatment has an effect. If the null is false for other reasons, then statistical significance can't be used to support the existence of a treatment effect. So these would be pointless, pedantic calculations.
@nonbel: Are you saying that an fMRI, when taken of a subject at rest, twice - then what the data here show - is that this is likely to be interpreted as a subject in two different states? And the researchers are ignoring the fact, rather insisting that we should be able to tell that these two states are the same (and then, perhaps tell them from other states)?
I see a few ways this could come about: perhaps the way we record and model activity doesn't conform to the distribution we assume (I'm not sure if they assume a normal distribution here - or if that even makes sense given the nature of the data) -- or perhaps the issue is with taking 3d/4d data and "turning it into" an easy-to-model statistical model (like the normal distribution)?
At any rate, it does seem that they're saying we can't tell that one individual at rest, measured twice, is in the same (rest) state both times? Hence, they're null hypothesis is bunk?
>"Are you saying that an fMRI, when taken of a subject at rest, twice - then what the data here show - is that this is likely to be interpreted as a subject in two different states?"
Yes, that is what they seem to be saying. I didn't read the code, or even the paper very closely. However, from what I quoted, they seem to be saying there is some assumption about autocorrelation that introduces what they call "false positives".
I am saying they have mischaracterized the problem. These are true positives.
Because their goal is to determine if some sort of treatment has an effect. If the null is false for other reasons, then statistical significance can't be used to support the existence of a treatment effect. So these would be pointless, pedantic calculations.