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Statistical inference for false positive and false negative error rates in mastery testing
Authors:Huynh Huynh
Affiliation:(1) College of Education, University of South Carolina, 29208 Columbia, South Carolina
Abstract:This paper describes an asymptotic inferential procedure for the estimates of the false positive and false negative error rates. Formulas and tables are described for the computations of the standard errors. A simulation study indicates that the asymptotic standard errors may be used even with samples of 25 cases as long as the Kuder-Richardson Formula 21 reliability is reasonably large. Otherwise, a large sample would be required.This work was performed pursuant to Grant No NIE-G-78-0087 with the National Institute of Education, Department of Health, Education and Welfare, Huynh Huynh, Principal Investigator. Points of view or opinions stated do not necessarily reflect NIE position or policy and no official endorsement should be inferred. The editorial assistance of Joseph C. Saunders is gratefully acknowledged.
Keywords:mastery testing  errors in classifications  decision errors  decision accuracy  beta-binomial model  testing for selection  error rate inference
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