Support for the “biotic resistance hypothesis,” that species-rich communities are more successful at resisting invasion by exotic species than are species-poor communities, has long been debated. It has been argued that native-exotic richness relationships (NERR) are negative at small spatial scales and positive at large scales, but evidence for the role of spatial scale on NERR has been contradictory. However, no formal quantitative synthesis has previously examined whether NERR is scale-dependent across multiple studies, and previous studies on NERR have not distinguished spatial grain and extent, which may drive very different ecological processes. We used a global systematic review and hierarchical mixed-effects meta-analysis to provide a comprehensive quantitative assessment of the patterns of NERR over a range of spatial grain sizes and spatial extents, based on 204 individual cases of observational (non-experimental) NERRs from 101 publications. We show that NERR was indeed highly scale-dependent across studies and increased with the log of grain size. However, mean NERR was not negative at any grain size, although there was high heterogeneity at small grain sizes. We found no clear patterns of NERR across different spatial extents, suggesting that extent plays a less important role in determining NERR than does grain, although there was a complex interaction between extent and grain size. Almost all studies on NERR were conducted in North America, western Europe, and a few other regions, with little information on tropical or Arctic regions. We did find that NERR increased northward in temperate regions and also varied with longitude. We discuss possible explanations for the patterns we found, and caution that our results do not show that invasive species are benign or have no negative consequences for biodiversity preservation. This study represents the first global quantitative analysis of scale-based NERR and casts doubt on the existence of an "invasion paradox" of negative NERR at small scales and positive correlations at large scales in non-experimental studies.