<p>Seismic quiescence, commonly defined as a transient decrease in earthquake occurrence rate, has long been discussed as a potential precursor to large earthquakes. However, its statistical detectability at the global scale remains controversial due to finite-sample variability, clustering, and the implicit multiple-testing problem associated with scanning long seismicity records. Here we quantitatively assess the statistical limits of detecting global seismic quiescence using a multiscale, null-hypothesis–driven framework applied to modern global earthquake catalogs. We analyze global seismicity from 1973 to 2026 using centered sliding windows spanning sub-annual to decadal timescales and estimate scale-dependent earthquake rates. Rate fluctuations are standardized within each scale and evaluated against analytically specified stationary baseline models, including both a homogeneous Poisson process and an overdispersed Negative Binomial null calibrated to the empirical mean–variance relationship of sliding-window counts. Statistical significance of apparent rate deficits is assessed using one-sided analytical p-values derived under each null model and controlled for multiple testing through scale-wise and pooled false discovery rate procedures. Results show that variability in the global earthquake rate is strongly scale dependent and statistically indistinguishable from fluctuations expected under stationary null models once empirical overdispersion and multiplicity are explicitly accounted for. While numerous apparent rate deficits emerge when uncorrected thresholds are applied, none survive multiplicity control at conventional levels under either null specification. This conclusion is robust across temporal scales, magnitude thresholds, and null model formulations. These findings delineate fundamental statistical limits on detecting global seismic quiescence within a sliding-window framework under explicit variance modeling and error control.</p>