Sentence examples for false observations from inspiring English sources
Known and unknown/unmeasured risk factors are the main sources of confounding effects in observational studies and can lead to false observations of elevated protective or hazardous effects.
In order to analyze the punishment mechanism more effectively, the attackers are classified into three categories as follows: Malicious users (MUs): This type of attackers sends false observations in order to confuse other users or the FC, causing extensive DoS (denial of service) attacks making a CRN hop from band to band, severely disrupting its operation.
The ideal position would maximize the number of observations made while minimizing the number of false observations.
This highlights the potential for false observations in these types of discovery investigations and an appropriate manner in which to reduce the probability of false positives through two separate discovery studies.
The guard who was supposed to be watching over him had spent nearly four hours away from his post, later admitting he had submitted false observation reports.
This analogy between Solanka and New York fails not only because it's based on a false observation -- the city in 2000 was reeling more from a surfeit of greed and complacency than from free-floating anxiety and anger -- but also because Solanka never seems intimately connected to the events he is witnessing in America.
Spectrum sensing data falsification attacks (SSDF) Attackers send false observation information, intentionally or unintentionally, to the fusion center (FC), and let the FC make the wrong decision.
There are two objectives: (1) to investigate the likelihood of false positive observations in observational studies, (2) to propose a simulation framework for assessing epidemiologic methods which deal with confounding effects.
A small number of false positive observations were, however, observed.
Such differences may contribute to false-positive or false-negative observations and need to be factored into any overall characterization.
These are false positive observations as the apparent statistical significance is high but the true rate is zero.
