Hi @akubicki,
Such a result makes perfect sense. We often say something like “FDC is a combined metric of FD and FC”, when we formally mean FDC = FD × FC (these are the original metrics). Of course, that doesn’t mean that the stats results (or significant fixels) are “additive”: to put it simple, the stats also bring the variance of the metrics in the populations into play. In your case, seeing a result for FD and FDC, but not for FC, can mean that there was genuinely no difference in the FC (or the variance was too high, and the study underpowered to detect it). The fact that you still see a result for FDC, must mean that the power to detect FD was high enough that it could potentially cope with the added variance of FC still… or that there was some difference in FC, yet too much variance to bring it out on its own in the FC result, but there was a clear difference in FD in similar areas; so both effects “reinforced” each other in the FDC metric.
But in any way, in your case you can only safely (statistically) say that there is a difference in FD. While there is a difference in FDC, this does not imply a difference in FC, since FDC is not specific to either FD or FC (it literally factors in both).
Does this answer your question?
Cheers,
Thijs