A persistent and underexamined problem in EEG functional connectivity analysis is that identical data can yield opposite interpretations depending on the analytic metric. Metrics such as weighted phase lag index (wPLI) and magnitude-squared coherence (MSC) derive from the same cross-spectral density (CSD) matrix yet capture different coupling properties, and this choice alone can reverse the reported direction of a group difference, a phenomenon we term directional fragility. We introduce Metric-T, a permutation-based diagnostic that quantifies it through a unit-erasure principle: direction consistency (DC) converts each metric into the percentage of channel-pair × frequency-band features for which one group exceeds the other, enabling cross-metric comparison on a common scale (T = DC(wPLI) − DC(MSC)). In a public dementia EEG dataset (OpenNeuro ds004504; N = 88: Alzheimer's n = 36, frontotemporal dementia n = 23, controls n = 29), reversals occurred in 3 of 12 band × comparison conditions (25%), each exceeding 20 percentage points, the largest being frontotemporal dementia vs. control in theta (T = +41.5 pp). None survived multiple-comparison correction (all corrected p > 0.10; Westfall–Young max-T minimum p = 0.234), indicating that connectivity differences in this heterogeneous cohort are directionally fragile and statistically unstable. A 700-condition simulation confirmed that reversal reflects coupling-mode composition rather than noise (p < 0.001). Metric-T requires only the CSD matrix already computed in any pipeline, is implementable in Python, and is fully reproducible. Rather than asserting a biomarker, it flags when metric choice, not neurobiology, may drive a reported group effect.