More about systematic errors in charge-density studies

Henn, Julian; Meindl, Kathrin

doi:10.1107/s2053273314012984

Cited by 18 publications

(18 citation statements)

References 5 publications

(11 reference statements)

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“…A similar but much stronger transformation with p 1 ¼ 0:5 was studied in Henn & Meindl (2014b). The transformation applied here is modest as the abundant weak s.u.…”

Section: Effects Of Distorted Su Valuesmentioning

confidence: 96%

Statistical tests against systematic errors in data sets based on the equality of residual means and variances from control samples: theory and applications

Henn¹,

Meindl

2015

Acta Cryst Sect A

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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.

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“…A similar but much stronger transformation with p 1 ¼ 0:5 was studied in Henn & Meindl (2014b). The transformation applied here is modest as the abundant weak s.u.…”

Section: Effects Of Distorted Su Valuesmentioning

confidence: 96%

Statistical tests against systematic errors in data sets based on the equality of residual means and variances from control samples: theory and applications

Henn¹,

Meindl

2015

Acta Cryst Sect A

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“…We chose data by Zhurov et al (2011), because in our previous studies these proved to be the most accurate data [see data sets 8-13 in Henn & Meindl (2014a), in Henn & Meindl (2015b) and in Henn & Meindl (2014b) as well as paragraph 4.4 and Fig. 4 in Henn & Meindl (2015a) and the supporting information].…”

Section: Application To High-resolution Charge-density Datamentioning

confidence: 99%

“…The parameter values a, b, f and the total number of parameters in the refinement, N par , are also given. The data set numbering scheme from Henn & Meindl (2014a) was used in column one to identify the data sets consistently with Henn & Meindl (2014b, 2015a Remaining errors -if present -originate as a consequence only from the experimental standard uncertainties. Table 2 shows the corresponding charge-density data.…”

Section: Application To High-resolution Charge-density Datamentioning

confidence: 99%

“…The ðI o Þ values determine or influence many data descriptors and data quality descriptors, for example the mean significance of the data, the goodness of fit, GoF, the purpose of which is to indicate systematic errors in the data and the correct scale of the s.u., and the wRðF 2 Þ value which describes how closely the model-derived calculated intensities approach the experimentally observed intensities. From the meansquared significance of the data, one can predict an expected wRðF 2 Þ value (Henn & Meindl, 2014b). The GoF and wRðF 2 Þ are probably the most important data descriptors in crystallographic applications.…”

Section: Introductionmentioning

confidence: 99%

“…We have shown earlier that underestimation of the s.u. 's of the strong reflections can also artificially reduce the wRðF 2 Þ value (Henn & Meindl, 2014b), and that underestimation of strong s.u. 's distorts the distribution of residuals more severely than overestimation of the strong s.u.'s.…”

Section: Introductionmentioning

confidence: 99%

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An alternative to the goodness of fit

Henn¹

2016

Acta Cryst Sect A

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An alternative measure to the goodness of fit (GoF) is developed and applied to experimental data. The alternative goodness of fit squared (aGoFs) demonstrates that the GoF regularly fails to provide evidence for the presence of systematic errors, because certain requirements are not met. These requirements are briefly discussed. It is shown that in many experimental data sets a correlation between the squared residuals and the variance of observed intensities exists. These correlations corrupt the GoF and lead to artificially reduced values in the GoF and in the numerical value of the wR(F). Remaining systematic errors in the data sets are veiled by this mechanism. In data sets where these correlations do not appear for the entire data set, they often appear for the decile of largest variances of observed intensities. Additionally, statistical errors for the squared goodness of fit, GoFs, and the aGoFs are developed and applied to experimental data. This measure shows how significantly the GoFs and aGoFs deviate from the ideal value one.

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Charge density studies of an inorganic-organic hybrid p-phenylenediammonium tetrachlorocuprate

Gajda

Woźniak

2017

Struct Chem

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More about systematic errors in charge-density studies

Cited by 18 publications

References 5 publications

Statistical tests against systematic errors in data sets based on the equality of residual means and variances from control samples: theory and applications

Statistical tests against systematic errors in data sets based on the equality of residual means and variances from control samples: theory and applications

An alternative to the goodness of fit

Charge density studies of an inorganic-organic hybrid p-phenylenediammonium tetrachlorocuprate

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