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Statistical tests against systematic errors in data sets based on the equality of residual means and variances from control samples: theory and applications
Author(s) -
Henn Julian,
Meindl Kathrin
Publication year - 2015
Publication title -
acta crystallographica section a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.742
H-Index - 83
ISSN - 2053-2733
DOI - 10.1107/s2053273314027363
Subject(s) - residual , statistics , data set , mathematics , gaussian , statistical hypothesis testing , cutoff , statistical significance , systematic error , algorithm , physics , quantum mechanics
Statistical tests are applied for the detection of systematic errors in data sets from least‐squares refinements or other residual‐based reconstruction processes. Samples of the residuals of the data are tested against the hypothesis that they belong to the same distribution. For this it is necessary that they show the same mean values and variances within the limits given by statistical fluctuations. When the samples differ significantly from each other, they are not from the same distribution within the limits set by the significance level. Therefore they cannot originate from a single Gaussian function in this case. It is shown that a significance cutoff results in exactly this case. Significance cutoffs are still frequently used in charge‐density studies. The tests are applied to artificial data with and without systematic errors and to experimental data from the literature.