We present a practical approach to inter-compare a range of candidate digital elevation models (DEMs) based on pre-defined criteria and statistically sound ranking approach. The presented approach integrates the randomized complete block design (RCBD) into a novel framework for DEMs comparison. The method presented provides a flexible, statistically sound and customizable tool for evaluating the quality of any raster - in this case a DEM - by means of a ranking approach, which takes into account a confidence level, and can use both quantitative and qualitative criteria. The users can design their own criteria for the quality evaluation in relation to their specific needs. The application of the RCBD method to rank six 1" global DEMs, considering a wide set of study sites, covering different morphological and landcover settings, highlights the potentialities of the approach. We used a suite of criteria relating to the differences in the elevation, slope, and roughness distributions compared to reference DEMs aggregated from 1-5 m lidar-derived DEMs. Results confirmed significant superiority of CopDEM 1" and its derivative FABDEM as the overall best 1" global DEMs. They are slightly better than ALOS, and clearly outperform NASADEM and SRTM, which are in turn much better than ASTER.