“…In summary, the data set that will yield the best model will be highly redundant, extend to a high-resolution limit characterized by a CC 1/2 statistic in the 0.1-0.2 range, and will be nearly complete in all resolution ranges with only randomly missing reflections. In other words, (1) merging R factors are of little value, especially in determining the high-resolution limit of a data set, [136,139,147,149] (2) strict signal-to-noise criteria discard useful data, degrading data quality and, consequently, model quality, [136,139,143,144,147,148] (3) highly redundant data sets are better than low redundancy sets, [147,149] (4) the CC 1/2 statistic is a better high-resolution limit indicator than previously used statistics, that is, merging R factors and I/σ I , [136,139,145,147] and (5) completeness in all resolution ranges is important, especially for structure solution, although incompleteness in the highest resolution shells only reduces the effective resolution of the data. [138,145,147] In the words of Evans and Murshudov, [136] 'There is no reason to suppose that cutting back the resolution of the data will improve the model.…”