Detecting outliers in non-redundant diffraction data

Outliers are observations which are very unlikely to be correct, as judged by independent observations or other prior information. Such unexpected obs ervations are treated, effectively, as being more informative about possibl e models, so they can seriously impede the course of structure determinatio n and refinement. The best way to detect and eliminate outliers is to colle ct highly redundant data, but it is not always possible to make multiple me asurements of every reflection. For Iron-redundant data, the prior expectat ion given either by a Wilson distribution of intensities or model-based str ucture-factor probability distributions can be used to detect outliers. Thi s captures mostly the excessively strong reflections, which dominate the fe atures of electron-density maps or, even more so, Patterson maps. The outli er rejection tests have been implemented in a program, Outliar.