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Survey-based occurrence data are often clustered in geographic space, and models built with such data are prone to produce erroneous results due to spatial autocorrelation.
But teachers' unions have fought to make sure evaluations do not rely too heavily on testing data, contending that the data are prone to errors.
Given that most previous throughfall studies relied on method-of-moments variogram estimation and sample sizes ≪200, currently available data are prone to large uncertainties.
Studies using observational data are prone to selection bias, with the treatment effect potentially being confounded with individual, provider or other characteristics [3].
While this does work most of the time, the encrypted data are prone to prying security thieves, who could decipher sensitive information like the patients' insurance service provider, medication history, etc. Steganography provides an extremely effective alternative to this problem, hiding the very existence of sensitive data by concealing the data in a "carrier".
Bioinformatics approaches, microarray experiments, as well as other high-throughput data are prone to false-positives.
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b) The data is prone to a variety of selection biases and confounding effects.
Furthermore, purely algorithmic extraction of rules from data is prone to creating spurious connections, such as false correlations.
But due to the nature of the aforementioned sensors, this localization data is prone to drift.
The monthly data is prone to distortions because of bad weather or quirks of the calendar (even an oddly timed holiday can skew the numbers).
The data worth implied by the surface soil water content data and groundwater level data is prone to corruption by a deviated initial guess.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com