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(2025, Imaging Neuroscience) argue that pipeline variability inflates false positive rates in between-group mega-analyses that \u201ccompare populations whose data were processed differently at the subject level.\u201d While such variability is a confounder in this specific and rare type of analysis, it does not confound effects of interest in more common conventional mega-analyses, which test non-zero effects across studies. Instead, pipeline differences strengthen the generalizability of discoveries and protect against idiosyncratic pipeline-induced artifacts in conventional analyses. Even in between-group mega-analyses, it is context-dependent whether pipeline-induced differences are false positives. 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