Non-classical Measurement Error with False Positives and Negatives

2020 ◽  
Author(s):  
Julio Acuna
Keyword(s):  
Measurement Error ◽  
False Positives ◽  
Classical Measurement ◽  
False Positives And Negatives

scholarly journals False Positives and Negatives from Real Traffic with Intrusion Detection/Prevention Systems

2012 ◽  
pp. 87-90 ◽  
Author(s):  
Cheng-Yuan Ho ◽  
Ying-Dar Lin ◽  
Yuan-Cheng Lai ◽  
I-Wei Chen ◽  
Fu-Yu Wang ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
False Positives ◽  
Real Traffic ◽  
False Positives And Negatives

The study of protein–protein interactions in bacteria

2012 ◽  
Vol 58 (11) ◽  
pp. 1241-1257 ◽  
Author(s):  
Roberto Velasco-García ◽  
Rocío Vargas-Martínez
Keyword(s):  
Hybrid System ◽  
High Throughput ◽  
Protein Interactions ◽  
Specific Protein ◽  
False Positives ◽  
Interaction Networks ◽  
Protein Protein Interactions ◽  
Extensive Data ◽  
False Positives And Negatives

Many of the functions fulfilled by proteins in the cell require specific protein–protein interactions (PPI). During the last decade, the use of high-throughput experimental technologies, primarily based on the yeast 2-hybrid system, generated extensive data currently located in public databases. This information has been used to build interaction networks for different species. Unfortunately, due to the nature of the yeast 2-hybrid system, these databases contain many false positives and negatives, thus they require purging. A method for confirming these PPI is to test them using a technique that operates in vivo and detects binary PPI. This article comprises an overview of the study of PPI and describes the main techniques that have been used to identify bacterial PPI, prioritizing those that can be used for their verification, and it also mentions a number of PPI that have been identified or confirmed using these methods.

scholarly journals On minimizing assignment errors and the trade-off between false positives and negatives in parentage analysis

2013 ◽  
Vol 22 (23) ◽  
pp. 5738-5742 ◽  
Author(s):  
Hugo B. Harrison ◽  
Pablo Saenz-Agudelo ◽  
Serge Planes ◽  
Geoffrey P. Jones ◽  
Michael L. Berumen
Keyword(s):  
Parentage Analysis ◽  
False Positives ◽  
Trade Off ◽  
False Positives And Negatives

scholarly journals When (Not) to Rely on the Reliable Change Index

2021 ◽  
Author(s):  
Andrew Athan McAleavey
Keyword(s):  
Measurement Error ◽  
Clinical Psychology ◽  
False Positives ◽  
Reliable Change Index ◽  
Applied Psychology ◽  
False Negatives ◽  
Statistical Tool ◽  
Reliable Change ◽  
Difference Scores

The reliable change index (RCI) is a widely used statistical tool designed to account for measurement error when evaluating difference scores. Because of its conceptual simplicity and computational ease, it persists in research and applied psychology. However, researchers have repeatedly demonstrated ways that the RCI is insufficient or invalid for various applications. This is a problem in research and clinical psychology since this common tool is potentially problematic. The aims of this manuscript are to non-technically describe the formulation and assumptions of the RCI, to offer guidance as to when the RCI is (and is not) appropriate, and to identify what is needed for proper calculation of the RCI when it is used. Several criteria are identified to help determine whether the RCI is appropriate for a specific use. It is apparent that the RCI is the best available method only in a small number of situations, is frequently miscalculated, and produces incorrect inferences more often than simple alternatives, largely because it is highly insensitive to real changes. Specific alternatives are offered which may better operationalize common inferential tasks, including when more than two observations are available and when false negatives are equally costly to false positives.

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