Relevant bibliographies by topics / Error type II

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Error type II'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Error type II"

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Weinstein, Gerald S. "Type II Statistical Error." Annals of Thoracic Surgery 83, no. 2 (2007): 724. http://dx.doi.org/10.1016/j.athoracsur.2006.07.070.

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Walker, J. Bryan, Robert M. Klein, and Sook-Ling Yee. "Type II Error and Antidepressants." Journal of Clinical Psychiatry 62, no. 5 (2001): 373–74. http://dx.doi.org/10.4088/jcp.v62n0512b.

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Resch, K. L., and E. Ernst. "The ethical type II error." Lancet 347, no. 8993 (1996): 62–63. http://dx.doi.org/10.1016/s0140-6736(96)91598-6.

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Pushap, Agar Chander, Srishty Sudershan, and Amrit Sudershan. "Type of Error in Statistics: A Review." Haya: The Saudi Journal of Life Sciences 8, no. 03 (2023): 39–43. http://dx.doi.org/10.36348/sjls.2023.v08i03.001.

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Background: Making appropriate decisions and drawing valid conclusions from the data requires the use of statistics in both scientific and non-scientific contexts. But errors are usually made during the formation of the result of the collected data which are obtained from a diverse and big population. Allowing errors is harmful and unavoidable, therefore, we need to control or limit the maximum level of error using statistics. Aim: Therefore, in the present review we aimed to provide brief information about the statistical test, the type of errors, and how to minimize the type of errors. Metho
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Chanoknath, Sutanapong, and Paul Louangrath. "Descriptive and Inferential Statistics." Inter. J. Res. Methodol. Soc. Sci 1, no. 1 (2018): 22–35. https://doi.org/10.5281/zenodo.1320727.

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This paper introduces two basic concepts in statistics: (i) descriptive statistics and (ii) inferential statistics. Descriptive statistics is the statistical description of the data set. Common description include: mean, median, mode, variance, and standard deviation. Inferential statistics is the drawing of inferences or conclusion based on a set of observations. These observations had been described by the descriptive statistics. From these descriptive statistics, an inference is made subject to a predefined limit or error or confidence interval. The error in concluding the inference is call
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Sato, Toru. "Type I and Type II Error in Multiple Comparisons." Journal of Psychology 130, no. 3 (1996): 293–302. http://dx.doi.org/10.1080/00223980.1996.9915010.

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Susilowati, Puji Lestari, and Novisita Ratu. "ANALISIS KESALAHAN SISWA BERDASARKAN TAHAPAN NEWMAN DAN SCAFFOLDING PADA MATERI ARITMATIKA SOSIAL." Mosharafa: Jurnal Pendidikan Matematika 7, no. 1 (2018): 13–24. http://dx.doi.org/10.31980/mosharafa.v7i1.337.

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AbstrakTujuan penelitian ini adalah untuk mengidentifikasi kesalahan siswa berdasarkan Tahapan Newman dan scaffolding pada materi aritmatika sosial. Penelitian ini merupakan penelitian tindakan. Subjek penelitian ini adalah siswa SMP Pangudi Luhur Salatiga kelas VII. Teknik pengumpulan data yang digunakan adalah tes dan wawancara klinis. Instrumen penelitian dalam penelitian ini adalah peneliti sendiri dan dibantu oleh instrumen pendukung berupa soal tes dan pedoman wawancara. Teknik analisis data yang digunakan mencakup transkrip hasil wawancara, reduksi data, analisis, dan triangulasi. Hasil
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Maarial, Arbi, Cory Pitoy, and Jorry Ferry Monoarfa. "Analisis Kesalahan Siswa Kelas IX SMP Negeri 3 Tondano Dalam Menyelesaikan Soal Cerita Pokok Bahasan Kekongruenan dan Kesebangunan." Journal on Education 6, no. 4 (2024): 19724–37. http://dx.doi.org/10.31004/joe.v6i4.6000.

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The study aims to analyze the mistakes of students in solving stories related to the material of congruence and construction in the 9th grade of the 3rd Tondano State High School. This research is a qualitative descriptive research with research subjects being 32 students and selected 6 students as samples of interviews. Data is collected through observations, written tests, and interviews. Data analysis is done through data summaries, data presentation, and interpretation. There are three types of errors observed, i.e. I misunderstood the problem, II mismade the mathematical model, III misled
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Shrestha, Shristi, and Khem Raj Kaini. "Refractive errors in type II diabetic patients." Journal of Universal College of Medical Sciences 3, no. 3 (2015): 17–21. http://dx.doi.org/10.3126/jucms.v3i3.24243.

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INTRODUCTION: The prevalence of diabetes is increasing rapidly. Refractive error in the diabetic population is considered a main cause of visual impairment. So this study was conducted to find out the pattern of refractive errors in patients with type II diabetes mellitus in Western Nepal.
 MATERIAL AND METHODS: It was a hospital-based cross-sectional study. Three hundred forty eyes of one hundred seventy patients with type II diabetes underwent comprehensive eye examination including subjective and objective refraction and serum biochemistry.
 RESULTS: The mean refraction was -0.132
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Sánchez-Espigares, José A., Pere Grima, and Lluís Marco-Almagro. "Visualizing Type II Error in Normality Tests." American Statistician 72, no. 2 (2018): 158–62. http://dx.doi.org/10.1080/00031305.2016.1278035.

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Dissertations / Theses on the topic "Error type II"

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Dalile, Boushra. "Is the High Probability of Type II Error an Issue in Error Awareness ERP Studies?" Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12628.

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When researchers began addressing the electrophysiology of conscious error awareness more than a decade ago, the role of the error-related negativity (ERN), alongside the subsequently occurring error positivity (Pe), was an obvious locus of attention given the fact that they are taken as indices of cortical error processing. In contrast to the clear-cut findings that link the amplitude of the Pe to error awareness, the association between the ERN amplitude and error awareness is vastly unclear, with a range of studies reporting significant differences in the ERN amplitude with respect to error
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Evans, Lauren B., and L. Lee Glenn. "Type II Error and Evaluation of Kangaroo Care in Tertiary Neonatal Units." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etsu-works/7461.

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Oduol, Vitalice K. (Vitalice Kalecha). "A generalised type-II hybrid ARQ scheme with soft-decision decoding /." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66095.

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Dai, Xiaogang. "Score Test and Likelihood Ratio Test for Zero-Inflated Binomial Distribution and Geometric Distribution." TopSCHOLAR®, 2018. https://digitalcommons.wku.edu/theses/2447.

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The main purpose of this thesis is to compare the performance of the score test and the likelihood ratio test by computing type I errors and type II errors when the tests are applied to the geometric distribution and inflated binomial distribution. We first derive test statistics of the score test and the likelihood ratio test for both distributions. We then use the software package R to perform a simulation to study the behavior of the two tests. We derive the R codes to calculate the two types of error for each distribution. We create lots of samples to approximate the likelihood of type I
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Shu, Xiaohua. "BLOCK DESIGNS UNDER AUTOCORRELATED ERRORS." Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/154927.

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Statistics<br>Ph.D.<br>This research work is focused on the balanced and partially balanced incomplete block designs when observations within blocks are correlated. The topic for this dissertation was motivated by a problem in pharmaceutical research, when several treatments are allocated to individuals, and repeated measurements are taken on each individual. In that case, there is correlation among the observations taken on the same individual. Typically, it is reasonable to assume that the observations within individual close to each other are highly correlated than observations that are far
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Powers-Prather, Bonnie Ann. "The Analysis of the Accumulation of Type II Error in Multiple Comparisons for Specified Levels of Power to Violation of Normality with the Dunn-Bonferroni Procedure: a Monte Carlo Study." Thesis, University of North Texas, 1989. https://digital.library.unt.edu/ark:/67531/metadc331385/.

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The study seeks to determine the degree of accumulation of Type II error rates, while violating the assumptions of normality, for different specified levels of power among sample means. The study employs a Monte Carlo simulation procedure with three different specified levels of power, methodologies, and population distributions. On the basis of the comparisons of actual and observed error rates, the following conclusions appear to be appropriate. 1. Under the strict criteria for evaluation of the hypotheses, Type II experimentwise error does accumulate at a rate that the probability of accept
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Li, Wei. "Analyzing Gene Expression Data in Terms of Gene Sets: Gene Set Enrichment Analysis." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/79.

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The DNA microarray biotechnology simultaneously monitors the expression of thousands of genes and aims to identify genes that are differently expressed under different conditions. From the statistical point of view, it can be restated as identify genes strongly associated with the response or covariant of interest. The Gene Set Enrichment Analysis (GSEA) method is one method which focuses the analysis at the functional related gene sets level instead of single genes. It helps biologists to interpret the DNA microarray data by their previous biological knowledge of the genes in a gene set. GSEA
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Vaz, Marcos André Braz. "PODER ESTATÍSTICO E COEFICIENTE DE VARIAÇÃO EM EXPERIMENTOS COM BOVINOS DE CORTE." Universidade Federal de Santa Maria, 2016. http://repositorio.ufsm.br/handle/1/4386.

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This study was conducted to estimate power of tests in Analysis of Variance (ANOVA) in beef cattle experiments, determine sample size and classify the coefficient of variation. Data was collected from thesis and dissertations of the Program of Post-Graduation in Animal Science of Federal University of Santa Maria (PPGZ-UFSM) among the years of 1991 to 2012, in beef cattle production area using ANOVA. Power was estimated by assumption of non central F distribution to the alternative hypothesis in ANOVA. The number of replications for treatments was estimated by power of 80% and interval of [0,4
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Slack, Sean Edward. "Essays on categorical and universal welfare provision : design, optimal taxation and enforcement issues." Thesis, University of St Andrews, 2016. http://hdl.handle.net/10023/15659.

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Part I comprises three chapters (2-4) that analyse the optimal combination of a universal benefit (B≥0) and categorical benefit (C≥0) for an economy where individuals differ in both their ability to work and, if able to work, their productivity. C is ex-ante conditioned on applicants being unable to work, and ex-post conditioned on recipients not working. In Chapter 2 the benefit budget is fixed but the test awarding C makes Type I and Type II errors. Type I errors guarantee B > 0 at the optimum to ensure all unable individuals have positive consumption. The analysis with Type II errors depend
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Crespi, Júnior Hugo. "Gerenciamento do ponto de corte para a concessão de crédito no varejo brasileiro." Universidade Presbiteriana Mackenzie, 2014. http://tede.mackenzie.br/jspui/handle/tede/940.

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Made available in DSpace on 2016-03-15T19:32:48Z (GMT). No. of bitstreams: 1 Hugo Crespi Junior.pdf: 1552051 bytes, checksum: 34b1ca02581c6a8240a8bd4702d15a71 (MD5) Previous issue date: 2014-02-14<br>One of the most important ways to finance consumers in the Brazilian market is the consumer credit offered in store. Provided by independent or captive finance companies, the consumer credit is normally granted or denied without taking into account its effect on the retailer s profitability. Denying credit to finance a high profit margin product is more damaging to the companies profits than if
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Books on the topic "Error type II"

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Anand, Geeta. The Cure. HarperCollins, 2009.

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Gerhart, Barry. Modeling HRM and Performance Linkages. Edited by Peter Boxall, John Purcell, and Patrick M. Wright. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199547029.003.0027.

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This article focuses on methodology in the literature on human resources (HR) management and performance. Whenever a theoretical model of HR and performance is tested and estimated using empirical data, a binary decision regarding whether the model is supported (yes or no) is typically made. If support is found, it is either because the model is correct or the method is wrong (i.e. a false positive, or Type I, error of inference). If support is not found, it is either because the model is wrong or the method is wrong (i.e. a false negative, or Type II, error of inference). The article hopes to
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Buchaklian, Adam H., and David P. Dimmock. Citrin Deficiency. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199972135.003.0018.

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Citrin deficiency is an inborn error of metabolism that appears to affect both the urea cycle and energy generation. Worldwide, citrin deficiency is likely one of the commonest inborn errors of metabolism, 60,000–80,000 individuals are estimated to have citrin deficiency in China alone. Children typically present with an abnormal newborn screen or neonatal intrahepatic cholestasis with citrin deficiency (NICCD). Adults typically present clinically with neuropsychiatric symptoms, hepatic dysfunction and hyperammonemia. As plasma amino acids typically show an elevated citrulline at the time of c
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Merry, Alan F. Errors and Violations. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199366149.003.0003.

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Errors and violations are major contributors to iatrogenic harm in patients. Anesthesia is a complex process within the complex system that is healthcare. In complex systems, decisions need to be reviewed frequently and iteratively with reset baseline data. Errors are unintentional and cannot be avoided simply by trying harder or deterred by the threat of punishment; violations, on the other hand, are deliberate deviations from safe practice. Types of error are statistically predictable, and can be understood in relation to the cognitive processes involved in their generation: either System I
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McCleary, Richard, David McDowall, and Bradley J. Bartos. Statistical Conclusion Validity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0006.

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Chapter 6 addresses the sub-category of internal validity defined by Shadish et al., as statistical conclusion validity, or “validity of inferences about the correlation (covariance) between treatment and outcome.” The common threats to statistical conclusion validity can arise, or become plausible through either model misspecification or through hypothesis testing. The risk of a serious model misspecification is inversely proportional to the length of the time series, for example, and so is the risk of mistating the Type I and Type II error rates. Threats to statistical conclusion validity ar
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Zarowin, Paul. Estimation of Discretionary Accruals and the Detection of Earnings Management. Edited by Michael A. Hitt, Susan E. Jackson, Salvador Carmona, Leonard Bierman, Christina E. Shalley, and Douglas Michael Wright. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780190650230.013.20.

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This article reviews recent research on the estimation of discretionary accruals and the detection of earnings management. There has been an explosive growth in research on accrual earnings management over the past twenty years, and almost all has used the Jones (1991) model or one of its close derivatives. Nevertheless, a growing literature has addressed the model’s problems and attempted to improve its estimation of discretionary accruals. The model’s incomplete characterization of how nondiscretionary accruals are determined by the firm’s operations can cause either Type I or Type II errors
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Anand, Geeta. The Cure: How a Father Raised $100 Million--And Bucked the Medical Establishment--In a Quest to Save His Children. William Morrow, 2006.

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Anand, Geeta. The Cure: How a Father Raised $100 Million--And Bucked the Medical Establishment--In a Quest to Save His Children. William Morrow, 2006.

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Robertson, Simon. Nietzsche and Contemporary Ethics. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198722212.001.0001.

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Nietzsche is one of the most subversive ethical thinkers of the Western canon. This book offers a critical assessment of his ethical thought and its significance for contemporary moral philosophy. It develops a charitable but critical reading of his thought, pushing some claims and arguments as far as seems fruitful while rejecting others. But it also uses Nietzsche in dialogue with, so to contribute to, a range of long-standing issues within normative ethics, metaethics, value theory, practical reason, and moral psychology. The book is divided into three principal parts. Part I examines Nietz
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Anand, Geeta. Cure: How a Father Raised $100 Million--and Bucked the Medical Establishment--in a Quest to Save His Children. HarperCollins Publishers, 2009.

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Book chapters on the topic "Error type II"

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Gooch, Jan W. "Type II Error." In Encyclopedic Dictionary of Polymers. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15415.

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Speer, Tod W., Rene Rubin, Iris Rusu, et al. "Type II Error." In Encyclopedia of Radiation Oncology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-540-85516-3_655.

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Nahler, Gerhard. "type II error." In Dictionary of Pharmaceutical Medicine. Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_1427.

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Carrier, Martin. "Error of Type I and Type II." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_943.

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Betsumiya, Koichi, Masaaki Harada, and Akihiro Munemasa. "Type II Codes over IF2r." In Applied Algebra, Algebraic Algorithms and Error-Correcting Codes. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45624-4_11.

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Cruz, Ricardo, Kelwin Fernandes, Joaquim F. Pinto Costa, and Jaime S. Cardoso. "Constraining Type II Error: Building Intentionally Biased Classifiers." In Advances in Computational Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59147-6_47.

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Anastassiou, George A. "Voronovskaya Type Asymptotic Expansions for Error Function Based Quasi-interpolation Neural Networks." In Intelligent Systems II: Complete Approximation by Neural Network Operators. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20505-2_19.

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Piedmont, Ralph L. "Type II Errors." In Encyclopedia of Quality of Life and Well-Being Research. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_3073.

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Piedmont, Ralph L. "Type II Errors." In Encyclopedia of Quality of Life and Well-Being Research. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17299-1_3073.

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Guo, Kai-zhong, Ran Li, and Jian-xin Li. "Study of the Solvability of the Fuzzy Error Matrix Set Equation in Equality Form of Type II 4." In Fuzzy Systems & Operations Research and Management. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19105-8_34.

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Conference papers on the topic "Error type II"

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Hao, Yiyan, Megan T. Jones, Simon Vandekar, Russell T. Shinohara, and Brian R. White. "Addressing Missing Data With Multiple Imputation in Optical Neuroimaging." In Bio-Optics: Design and Application. Optica Publishing Group, 2025. https://doi.org/10.1364/boda.2025.jm4a.8.

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In optical neuroimaging, the field-of-view invariably differs across subjects. Here, we evaluate the use of techniques to address missingness, including multiple imputation, with an emphasis on their effects on Type I and Type II error.
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Ragab, Hany, Sidney Givigi, and Aboelmagd Noureldin. "Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS." In 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS). IEEE, 2025. https://doi.org/10.1109/plans61210.2025.11028310.

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Gundogdu, Fatma Kutlu, Esra Ilbahar, Ihsan Kaya, Ali Karasan, Elifnaz Olgac, and Kubra Yazir. "Monitoring Packaging Defects in a Logistic Company for the Determination of Type-II Errors." In 2024 International Conference on Decision Aid Sciences and Applications (DASA). IEEE, 2024. https://doi.org/10.1109/dasa63652.2024.10836353.

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Gruzin, Andrey. "Type II error exponent for identity test of Markov processes." In 2008 IEEE Information Theory Workshop (ITW). IEEE, 2008. http://dx.doi.org/10.1109/itw.2008.4578671.

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Dinu, Alexandru, and Adriana Vlad. "Romanian printed language, statistical independence and the type II statistical error." In 2021 International Conference on Speech Technology and Human-Computer Dialogue (SpeD). IEEE, 2021. http://dx.doi.org/10.1109/sped53181.2021.9587397.

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Wright, Geraldine A., Douglas B. Leviton, and Charles M. Fleetwood. "Vacuum ultraviolet performance of a Wolter Type II telescope: theoretical and experimental results." In Space Optics for Astrophysics and Earth and Planetary Remote Sensing. Optica Publishing Group, 1991. http://dx.doi.org/10.1364/soa.1991.mf11.

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Researchers at Goddard Space Flight Center have been developing fabrication and testing techniques for glancing incidence mirrors for many years.1,2 We have also been developing analytical tools to predict performance of such mirrors from their measured surface data.3 The ability to predict imaging from surface metrology would allow us to set error budgets on the surface quality of glancing incidence mirrors to obtain any imaging requirement requested. Efforts have been ongoing to verify our imaging calculations by comparison with actual imaging tests. We have succeeded in verifying our calcul
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Singh, Jaspreet, Mahesh V. Shitole, Omprakash Dewangan, and Akanksha Mishra. "Type-I and Type-II Diabetes Disease Prediction by Handling Trade-Off Between Hyperplane Margin and Classification Error In Support Vector Machine." In 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI). IEEE, 2023. http://dx.doi.org/10.1109/icaiihi57871.2023.10489216.

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Peng, Aoran, and Scarlett R. Miller. "Hit, Miss, or Error? Predicting Errors in Design Decision Making for Radically Innovative Ideas Using Individual Attributes." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-89708.

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Abstract Researchers and practitioners alike agree that for companies to survive and thrive they must develop and support radical innovation. However, these ideas are complex and risky, and not all succeed. Because of this, decision makers are often left to make hard decisions in terms of which ideas move on and which are abandoned. The goal of this paper was to provide pilot evidence to identify the impact of individual attributes on the effectiveness of decision making for radical innovation. Specifically, we sought to identify how individual personality and preferences for creativity impact
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Kloareg, Maela, and David Causeur. "Improving type II error rates of multiple testing procedures by use of auxiliary variables Application to microarray data." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0076.

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Chowdhury, Souma, Ali Mehmani, and Achille Messac. "Concurrent Surrogate Model Selection (COSMOS) Based on Predictive Estimation of Model Fidelity." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35358.

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One of the primary drawbacks plaguing wider acceptance of surrogate models is their low fidelity in general. This issue can be in a large part attributed to the lack of automated model selection techniques, particularly ones that do not make limiting assumptions regarding the choice of model types and kernel types. A novel model selection technique was recently developed to perform optimal model search concurrently at three levels: (i) optimal model type (e.g., RBF), (ii) optimal kernel type (e.g., multiquadric), and (iii) optimal values of hyper-parameters (e.g., shape parameter) that are con
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Reports on the topic "Error type II"

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Verrill, Steve, and Mark Durst. The decline and fall of Type II error rates. U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 2005. http://dx.doi.org/10.2737/fpl-rp-628.

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Orbán, Levente. Lecture Short Notes: Introduction to Probability, Effect Size, and Hypothesis Testing. Orban Foundation, 2025. https://doi.org/10.69642/4593.

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This lecture introduces fundamental statistical concepts related to probability, effect size, and hypothesis testing. We explore the role of probability in inferential statistics, discuss the trade-offs between Type I and Type II errors, and demonstrate calculations using R programming. The importance of effect size in interpreting statistical significance is also highlighted. This document refines and expands upon the original lecture transcript to enhance coherence and depth.
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Hawley. PR-015-11707-R01 Test Diagnostic Methods for Turbine Gas Meters. Pipeline Research Council International, Inc. (PRCI), 2013. http://dx.doi.org/10.55274/r0010671.

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Similar to most metering technologies, turbine meters are known to be affected by abnormal flow or abnormal mechanical conditions which can cause bias in flow measurement. These types of flow conditions include blockage at the flow meter or straightening vanes, grime or liquid contamination on the internal meter components, damage to the internal meter components, and pulsation in the flow. With the introduction of ultrasonic and Coriolis meters for gas applications, the natural gas industry has embraced the concept of meters with embedded diagnostic capabilities. These capabilities allow the
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