Statistical inference for false positive and false negative error rates in mastery testing

Psychometrika ◽  
1980 ◽  
Vol 45 (1) ◽  
pp. 107-120 ◽  
Author(s):  
Huynh Huynh
Keyword(s):  
Statistical Inference ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Mastery Testing ◽  
Negative Error ◽  
False Negative Error

Error Rates in Competency Testing When Test Retaking Is Permitted

1990 ◽  
Vol 15 (1) ◽  
pp. 39-52 ◽  
Author(s):  
Huynh Huynh
Keyword(s):  
Conditional Probability ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Rasch Models ◽  
Positive Error ◽  
False Positive Error ◽  
Competency Testing ◽  
Negative Error ◽  
False Negative Error

False positive and false negative error rates are studied for competency testing where examinees are permitted to retake the test if they fail to pass. Formulae are provided for the beta-binomial and Rasch models, and estimates based on these two models are compared for several typical situations. Although Rasch estimates are expected to be more accurate than beta-binomial estimates, differences among them are found not to be substantial in a number of practical situations. Under relatively general conditions and when test retaking is permitted, the probability of making a false negative error is zero. Under the same situation, and given that an examinee is a true nonmaster, the conditional probability of making a false positive error for this examinee is one.

scholarly journals High resolution analysis of cervical cells--a progress report.

1977 ◽  
Vol 25 (7) ◽  
pp. 689-695 ◽  
Author(s):  
R S Poulsen ◽  
L H Oliver ◽  
R L Cahn ◽  
C Louis ◽  
G Toussaint
Keyword(s):  
High Resolution ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Cell Segmentation ◽  
Negative Error ◽  
False Negative Error ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Cervical Cells

This paper presents preliminary results of research toward the development of a high resolution analysis stage for a dual resolution image processing-based prescreening device for cervical cytology. Experiments using both manual and automatic methods for cell segmentation are described. In both cases, 1500 cervical cells were analyzed and classified as normal or abnormal (dysplastic or malignant) using a minimum Mahalanobis distance classifier with eight subclasses of normal cells, and five subclasses of abnormal cells. With manual segmentation, false positive and false negative error rates of 2.98 and 7.73% were obtained. Similar experiments using automatic cell segmentation methods yielded false positive and false negative error rates of 3.90 and 11.56%, respectively. In both cases, independent training and testing data were used.

Errors in prostate core biopsy diagnosis in an era of specialisation and double reporting

2020 ◽  
pp. jclinpath-2020-206726
Author(s):  
Cornelia Margaret Szecsei ◽  
Jon D Oxley
Keyword(s):  
Error Rate ◽  
False Positive ◽  
Core Biopsy ◽  
False Negative ◽  
Diagnostic Error ◽  
Error Rates ◽  
False Positive Error ◽  
Biopsy Diagnosis ◽  
False Negative Error ◽  
False Positive Error Rate

AimTo examine the effects of specialist reporting on error rates in prostate core biopsy diagnosis.MethodBiopsies were reported by eight specialist uropathologists over 3 years. New cancer diagnoses were double-reported and all biopsies were reviewed for the multidisciplinary team (MDT) meeting. Diagnostic alterations were recorded in supplementary reports and error rates were compared with a decade previously.Results2600 biopsies were reported. 64.1% contained adenocarcinoma, a 19.7% increase. The false-positive error rate had reduced from 0.4% to 0.06%. The false-negative error rate had increased from 1.5% to 1.8%, but represented fewer absolute errors due to increased cancer incidence.ConclusionsSpecialisation and double-reporting have reduced false-positive errors. MDT review of negative cores continues to identify a very low number of false-negative errors. Our data represents a ‘gold standard’ for prostate biopsy diagnostic error rates. Increased use of MRI-targeted biopsies may alter error rates and their future clinical significance.

Optimizing the balance between false positive and false negative error probabilities of confirmatory methods for the detection of veterinary drug residues†

The Analyst ◽  
1999 ◽  
Vol 124 (2) ◽  
pp. 109-114 ◽  
Author(s):  
Waldo J. de Boer ◽  
Hilko van der Voet ◽  
Wil G. de Ruig ◽  
J. A. (Hans) van Rhijn ◽  
Kevin M. Cooper ◽  
...  
Keyword(s):  
False Positive ◽  
False Negative ◽  
Veterinary Drug ◽  
Veterinary Drug Residues ◽  
Drug Residues ◽  
Negative Error ◽  
False Negative Error ◽  
Error Probabilities

scholarly journals Feasibility of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging for sorting unsound chestnuts

Italus Hortus ◽  
2020 ◽  
Vol 27 ◽  
pp. 3-18
Author(s):  
Giacomo Bedini ◽  
Giorgia Bastianelli ◽  
Swathi Sirisha Nallan Chakravartula ◽  
Carmen Morales-Rodríguez ◽  
Luca Rossini ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Error Rate ◽  
False Positive ◽  
Total Error ◽  
False Negative ◽  
Nir Spectroscopy ◽  
Positive Error ◽  
False Positive Error ◽  
Negative Error ◽  
False Negative Error

Authors explored the potential use of Vis/NIR hyperspectral imaging (HSI) and Fourier-transform Near-Infrared (FT-NIR) spectroscopy to be used as in-line tools for the detection of unsound chestnut fruits (i.e. infected and/or infested) in comparison with the traditional sorting technique. For the intended purpose, a total of 720 raw fruits were collected from a local company. Chestnut fruits were preliminarily classified into sound (360 fruits) and unsound (360 fruits) batches using a proprietary floating system at the facility along with manual selection performed by expert workers. The two batches were stored at 4 ± 1 °C until use. Samples were left at ambient temperature for at least 12 h before measurements. Subsequently, fruits were subjected to non-destructive measurements (i.e. spectral analysis) immediately followed by destructive analyses (i.e. microbiological and entomological assays). Classification models were trained using the Partial Least Squares Discriminant Analysis (PLS-DA) by pairing the spectrum of each fruit with the categorical information obtained from its destructive assay (i.e., sound, Y = 0; unsound, Y = 1). Categorical data were also used to evaluate the classification performance of the traditional sorting method. The performance of each PLS-DA model was evaluated in terms of false positive error (FP), false negative error (FN) and total error (TE) rates. The best result (8% FP, 14% FN, 11% TE) was obtained using Savitzky-Golay first derivative with a 5-points window of smoothing on the dataset of raw reflectance spectra scanned from the hilum side of fruit using the Vis/NIR HSI setup. This model showed similarity in terms of False Negative error rate with the best one computed using data from the FT-NIR setup (i.e. 15% FN), which, however, had the lowest global performance (17% TE) due to the highest False Positive error rate (19%). Finally, considering that the total error rate committed by the traditional sorting system was about 14.5% with a tendency of misclassifying unsound fruits, the results indicate the feasibility of a rapid, in-line detection system based on spectroscopic measurements.

scholarly journals IMPROVING PRECISION AND REDUCING BIAS IN BIOLOGICAL SURVEYS: ESTIMATING FALSE-NEGATIVE ERROR RATES

2003 ◽  
Vol 13 (6) ◽  
pp. 1790-1801 ◽  
Author(s):  
Andrew J. Tyre ◽  
Brigitte Tenhumberg ◽  
Scott A. Field ◽  
Darren Niejalke ◽  
Kirsten Parris ◽  
...  
Keyword(s):  
False Negative ◽  
Error Rates ◽  
Biological Surveys ◽  
Negative Error ◽  
False Negative Error

Estimating false-positive and false-negative errors in analyses of hormonal pulsatility

1988 ◽  
Vol 254 (6) ◽  
pp. E786-E794 ◽  
Author(s):  
E. Van Cauter
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
False Negative ◽  
Pulse Frequency ◽  
Computer Algorithms ◽  
Pulse Detection ◽  
Negative Error ◽  
False Negative Error ◽  
Positive Rate ◽  
In Series

Previous studies evaluating computer algorithms for endocrine pulse detection have estimated the rate of false-positive pulses in series of purely random variations (i.e., “noise”) and have determined pulse-detection criteria associated with low levels of such false-positive rates. The present study investigates the relationship between the false-positive rate and the sizes of the false-positive and false-negative errors on pulse frequency for series including both pulses and noise. The algorithm used (ULTRA) proceeds by eliminating all peaks of concentration for which either the increment or the decrement does not exceed a threshold expressed in multiples of the local intra-assay coefficient of variation. A total of 336 computer-generated series was analyzed using thresholds of two and three coefficients of variation. The effects of noise level, pulse frequency, pulse amplitude, and presence of a base-line variation on the sizes of the false-positive and false-negative errors were evaluated. The false-positive rate in noise series exceeded the false-positive rate by a 4- to 10-fold factor in series including at least 8 pulses/100 samples. When pulse frequency increased, the false-positive error decreased, but the false-negative error increased. In series with more than 8 pulses/100 samples, the use of thresholds aimed at maintaining the false-positive rate in noise series below 1% resulted in a false-negative error in excess of 20%. In conclusion, for hormonal profiles that include 8 or more pulses/100 samples, the use of pulse-detection criteria tailored to minimize the false-positive rate in noise series may result in an underestimation of pulse frequency.

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