Human evaluation remains a critical step in selecting the best MT model for a job. The common approach is to have a reviewer analyze a number of segments translated by the compared models, assigning them categories and also post-editing some of them when needed. In other words, a reviewer is asked to make numerous decisions regarding very similar, out-of-context translations. It can easily result in arbitrary choices. We propose a new methodology that is centered around a real-life post-editing of a set of cohesive homogeneous texts. The homogeneity is established using a number of metrics on a set of preselected same-genre documents. The key assumption is that two or more identical in length homogeneous texts take approximately the same time and effort when edited by the same editor. Hence, if one text requires more work (edit distance, time spent), it is an indication of a relatively lower quality of machine translation used for this text. See details in the attached file.