Sentence examples for multiple sources of error from inspiring English sources

Unfortunately, the operational weather radar measurements were affected by multiple sources of error and only 30% of the precipitation observed by rain gauges was estimated.

The present study used a complex design with multiple examiners with the aim of assessing the reliability of intraoral QST taking account of multiple sources of error simultaneously.

For example, G theory does not require parallel forms (i.e., equal means, variances, and covariances), can handle unbalanced designs, and provides a single reliability estimate for designs with multiple sources of error.

All sample surveys and polls are subject to multiple sources of error including, but not limited to sampling error, coverage error, and measurement error.

All sample surveys and polls, whether or not they use probability sampling, are subject to multiple sources of error which are most often not possible to quantify or estimate, including sampling error, coverage error, error associated with nonresponse, error associated with question wording and response options, and post-survey weighting and adjustments.

However, self-reported exposures are subject to multiple sources of error resulting in misclassification.

However, because maps represent ordered lists of continuous values, this extension is not straightforward, particularly when multiple sources of mapping errors and their high error rates are taken into account [ 19].

These findings will have a broad interest to ecologists wishing to account for multiple sources of misclassification error in capture-mark-recapture studies, with the caveat that the specifics of the approach are dependent on species, transmitter types and other aspects of experimental design which may or may not be amenable to the misclassification framework.

The technique of depressing a syringe plunger as the cannula is withdrawn from the tissue is associated with potentially multiple sources of human error.

The dosimetry model was based on inputs with multiple sources of measurement error including release estimates, data on environmental dispersion, dietary recall of usual milk consumption from mothers, and assumptions about bioavailability and biokinetics.

Our second contribution is the design of a robust and fast statistical evaluation approach that includes multiple sources of mapping errors in the alignment score and evaluates the significance of the best alignment using all identified feasible solutions.