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Web surveys are rapidly becoming standard issue in many researchers' toolkits; however, measurement error has been shown to affect web surveys to a greater extent than paper-and-pencil surveys (Couper, 2000; Manfreda & Vehovar, 2002).
Although there is often collinearity between variables, in general DEM error has been shown to increase with increasing canopy stem density, age (Reutebuch et al. 2003; Naesset 2002; Naesset and Okland 2002), and levels of undergrowth (Hyyppa et al. 2005).
In addition, sequencing error has been shown to have important effects in comparative genomics, particularly in genomic scans for positive selection [20], [21].
Where cause of death is miscoded, the error has been shown to be correlated with race (Noymer, Penner, and Saperstein 2011).
Exposure measurement error has been shown to introduce bias and random error in environmental epidemiological studies (Thomas et al. 1993), and the quality of exposure data has been identified as a major determinant of the validity of environmental epidemiology studies (Baker and Niewenhuijsen 2008; Rothman et al. 2008).
In time series studies, use of measurements from ambient monitors, even in absence of any instrumental error, has been shown to introduce both a Berkson error component, a result of using aggregated instead of individual exposure data, and a classical error component, a result of the difference between the aggregated exposure data and the true ambient PM2.5 concentrations [ 5].
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It should be noted that the formulas used to estimate this standard error have been shown to be inconsistent in certain settings [ 23].
Two learning signals that are derived from this algorithm, the predictive value and the prediction error, have been shown to explain changes in neural activity and behavior during learning across species.
A reduction in the number of cutting errors has been shown to occur when a navigation system is used for TKR surgery [10].
Test-retest reliability for the SOPT total errors score has been shown to be high while stability of other SOPT indices, including perseverative errors, has been shown to be rather poor [ 47].
As an association between interruptions and medication errors has been shown [ 32], understanding the reasons why interruptions are pervasive in certain environments is a prerequisite to improving patient safety.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com