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Artykuły w czasopismach na temat „Error type II”

Autor: Grafiati

Data publikacji: 5 czerwca 2025

Data aktualizacji: 2 sierpnia 2025

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1

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Bodington, Jeffrey C. "Wine, Women, Men, and Type II Error." Journal of Wine Economics 12, no. 2 (2017): 161–72. http://dx.doi.org/10.1017/jwe.2017.8.

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AbstractMore than forty published works show that women and men differ in their taste preferences for sweet, salt, sour, bitter, fruit, and other flavors. Despite those differences, dozens of state fair and other wine competitions determine winners' ribbons, medals, scores, and ranks by pooling the opinions of female and male judges. This article examines twenty-three blind wine tastings during which female and male judges scored more than nine hundred wines. Two-sample t-test results show that the gender-specific distributions of scores do have similar means and standard deviations. Exact p-v

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12

Ueki, Chikara, and Genichi Sakaguchi. "Importance of Awareness of Type II Error." Annals of Thoracic Surgery 105, no. 1 (2018): 333. http://dx.doi.org/10.1016/j.athoracsur.2017.03.062.

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13

Iwan, Mohamad. "BANKRUPTCY PREDICTION MODEL WITH ZETAc OPTIMAL CUT-OFF SCORE TO CORRECT TYPE I ERRORS." Gadjah Mada International Journal of Business 7, no. 1 (2005): 41. http://dx.doi.org/10.22146/gamaijb.5563.

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This research examines financial ratios that distinguish between bankrupt and non-bankrupt companies and make use of those distinguishing ratios to build a one-year prior to bankruptcy prediction model. This research also calculates how many times the type I error is more costly compared to the type II error. The costs of type I and type II errors (cost of misclassification errors) in conjunction to the calculation of prior probabilities of bankruptcy and non-bankruptcy are used in the calculation of the ZETAc optimal cut-off score. The bankruptcy prediction result using ZETAc optimal cut-off

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14

Wacholder, S. "Population Stratification in Genetic Association Studies: Bias, Type I error and Type II Error." American Journal of Epidemiology 163, suppl_11 (2006): S84. http://dx.doi.org/10.1093/aje/163.suppl_11.s84-b.

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15

Valencia, Adrian, Thomas J. Smith, and James Ang. "The Effect of Noisy Fair Value Measures on Bank Capital Adequacy Ratios." Accounting Horizons 27, no. 4 (2013): 693–710. http://dx.doi.org/10.2308/acch-50517.

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SYNOPSIS Fair value accounting has been a hotly debated topic during the recent financial crisis. Supporters argue that fair values are more relevant to investors, while detractors point to the measurement error in the estimation of the reported fair values to attack its reliability. This study examines how noise in reported fair values impacts bank capital adequacy ratios. If measurement error causes reported capital levels to deviate from fundamental levels, then regulators could misidentify a financially healthy bank as troubled (type I error) or a financially troubled bank as safe (type II

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16

Fuadi, Irham, and Sutriyono Sutriyono. "ANALISIS KESALAHAN SISWA DALAM MENYELESAIKAN SOAL CERITA MATERI SISTEM PERSAMAAN LINEAR DUA VARIABEL KELAS VIII SMP NEGERI 7 SALATIGA." Pendekar : Jurnal Pendidikan Berkarakter 1, no. 1 (2018): 66. http://dx.doi.org/10.31764/pendekar.v1i1.282.

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Abstrak: Penelitian ini bertujuan untuk menganalisis kesalahan siswa dalam menyelesaikan soal cerita materi sistem persamaan linear dua variable. Metode yang digunakan dalam penelitian ini adalah deskiptif kualitatif. Sampel yang diambil adalah siswa kelas VIII H SMP Negeri 7 Salatiga dengan jumlah subjek sebanyak 3 siswa. Hasil penelitian ini menunjukan siswa masih melakukan kesalahan pada type I (reading error) sebesar 0%, kesalahan pada type II (comprehension error) sebesar 19%, kesalahan pada type III (transformasion error) sebesar 14%, kesalahan pada type IV (process skill error) sebesar

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17

Lochner, Heather V., Mohit Bhandari, and Paul Tornetta. "Type-II Error Rates (Beta Errors) of Randomized Trials in Orthopaedic Trauma." Journal of Bone and Joint Surgery-American Volume 83, no. 11 (2001): 1650–55. http://dx.doi.org/10.2106/00004623-200111000-00005.

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18

Matsuda, Hiroyuki. "The Importance of Type II Error and Falsifiability." Human and Ecological Risk Assessment: An International Journal 11, no. 1 (2005): 189–200. http://dx.doi.org/10.1080/10807030590920015.

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19

Bailey, Christopher S., Charles G. Fisher, and Marcel F. Dvorak. "Type II Error in the Spine Surgical Literature." Spine 29, no. 10 (2004): 1146–49. http://dx.doi.org/10.1097/00007632-200405150-00018.

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20

Maclean, A. D. W., T. J. Coats, C. J. C. Kirk, and F. W. Cross. "Design of study predisposed to type II error." BMJ 311, no. 7013 (1995): 1165. http://dx.doi.org/10.1136/bmj.311.7013.1165.

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21

GROSSMAN, R. "The problem of the type ii statistical error." Obstetrics & Gynecology 87, no. 4 (1996): 640. http://dx.doi.org/10.1016/s0029-7844(96)80292-4.

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22

MITTENDORF, R., V. ARUN, and A. SAPUGAY. "The problem of the type II statistical error." Obstetrics & Gynecology 86, no. 5 (1995): 857–59. http://dx.doi.org/10.1016/0029-7844(95)00251-l.

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23

Pomerantsev, Alexey L., and Oxana Ye Rodionova. "On the type II error in SIMCA method." Journal of Chemometrics 28, no. 6 (2014): 518–22. http://dx.doi.org/10.1002/cem.2610.

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24

MAURO, ROBERT D. "It's Time We Eschew the Error, Type II." Archives of Pediatrics & Adolescent Medicine 144, no. 6 (1990): 620. http://dx.doi.org/10.1001/archpedi.1990.02150300014010.

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25

Byers, Steven N. "Testing type II error rates in biological anthropology." American Journal of Physical Anthropology 111, no. 2 (2000): 283–89. http://dx.doi.org/10.1002/(sici)1096-8644(200002)111:2<283::aid-ajpa11>3.0.co;2-1.

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Koretz, Ronald L. "JPEN Journal Club 33. The Type II Error." Journal of Parenteral and Enteral Nutrition 42, no. 3 (2018): 661–64. http://dx.doi.org/10.1002/jpen.1140.

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27

Vasudevan, Senthilvel. "Formation, Testing of Hypothesis and Confidence Interval in Medical Research." International Journal of Medical Sciences and Nursing Research 2, no. 3 (2022): 22–27. https://doi.org/10.55349/ijmsnr.2022232227.

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<strong>Abstract:</strong> <strong>Background:</strong>  Statistics which help us in arriving at the criterion for such decisions in any research.  Hypothesis means assumptions.  It is an important activity of pharmacy or medical fields and its related research. <strong>Materials and Methods:</strong>  Statistical inferences play an important role in biological statistical tests and arriving at some conclusion.  Some suitable examples were also workout in this section. <strong>Results:</strong>  Confidence intervals provide a method of stating the precision or clo

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28

Jiménez-Gamero, Inmaculada, and Mohamed Analla. "The Importance of Type II Error in Hypothesis Testing." International Journal of Statistics and Probability 12, no. 2 (2023): 42. http://dx.doi.org/10.5539/ijsp.v12n2p42.

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Statistical tests of significance theoretically deal with two mutually exclusive hypotheses: the null and the alternative. However, at least in biomedical assays, only the null hypothesis is taken into account through type I error evaluation. But, basing these tests solely on type I error has two drawbacks: first, the probability limits (5%, 1% and 0.1%) arbitrarily set to the significance levels have no scientific justification. Second, acceptation of the null hypothesis is just a matter of chance, as it is mainly conditioned by the sample size due to its direct effect on the power of the tes

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29

Verrill, Steve, and Mark Durst. "The Decline and Fall of Type II Error Rates." American Statistician 59, no. 4 (2005): 287–91. http://dx.doi.org/10.1198/000313005x70353.

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Pike, Jeffrey, and Jordan Leith. "Type II error in the shoulder and elbow literature." Journal of Shoulder and Elbow Surgery 18, no. 1 (2009): 44–51. http://dx.doi.org/10.1016/j.jse.2008.08.001.

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HAYDEN, GREGORY F. "It's Time We Eschew the Error, Type II-Reply." Archives of Pediatrics & Adolescent Medicine 144, no. 6 (1990): 622. http://dx.doi.org/10.1001/archpedi.1990.02150300016011.

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Dyer, Frank J. "Avoiding Type II error in assessing lead toxicity plaintiffs." Behavioral Sciences & the Law 16, no. 1 (1998): 131–45. http://dx.doi.org/10.1002/(sici)1099-0798(199824)16:1<131::aid-bsl297>3.0.co;2-p.

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Geiger, Marshall A., and Dasaratha V. Rama. "Audit Firm Size and Going-Concern Reporting Accuracy." Accounting Horizons 20, no. 1 (2006): 1–17. http://dx.doi.org/10.2308/acch.2006.20.1.1.

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Prior research suggests that the Big 4 audit firms are of higher quality than are non-Big 4 firms. However, existing tests for an association between audit firm size and reporting accuracy are indirect and provide mixed results. Our study extends this line of research by examining whether the Big 4 audit firms exhibit higher quality reporting by having fewer “audit-reporting errors” in the context of issuing going-concern modified reports. Our analyses examine both types of going-concern reporting errors (i.e., type I errors—modified opinions rendered to subsequently viable clients; and type I

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34

Williams, J. L., C. A. Hathaway, K. L. Kloster, and B. H. Layne. "Low power, type II errors, and other statistical problems in recent cardiovascular research." American Journal of Physiology-Heart and Circulatory Physiology 273, no. 1 (1997): H487—H493. http://dx.doi.org/10.1152/ajpheart.1997.273.1.h487.

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Frequently in biomedical literature, measurements are considered “not statistically different” if a statistical test fails to achieve a P value that is < or = 0.05. This conclusion may be misleading because the size of each group is too small or the variability is large, and a type II error (false negative) is committed. In this study, we examined the probabilities of detecting a real difference (power) and type II errors in unpaired t-tests in Volumes 246 and 266 of the American Journal of Physiology: Heart and Circulatory Physiology. In addition, we examined all articles for other statist

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35

Hauptman, Joe. "Presentation of Type I and Type II Error Rates to Non-statisticians." Teaching Statistics 26, no. 2 (2004): 46–48. http://dx.doi.org/10.1111/j.1467-9639.2004.00160.x.

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36

Ermakov, M. S. "Nonparametric hypothesis testing with small type I or type II error probabilities." Problems of Information Transmission 44, no. 2 (2008): 119–37. http://dx.doi.org/10.1134/s0032946008020051.

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37

Ermakov, Mikhail. "Nonparametric signal detection with small type I and type II error probabilities." Statistical Inference for Stochastic Processes 14, no. 1 (2011): 1–19. http://dx.doi.org/10.1007/s11203-010-9048-5.

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Fairweather, PG. "Statistical power and design requirements for environmental monitoring." Marine and Freshwater Research 42, no. 5 (1991): 555. http://dx.doi.org/10.1071/mf9910555.

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This paper discusses, from a philosophical perspective, the reasons for considering the power of any statistical test used in environmental biomonitoring. Power is inversely related to the probability of making a Type II error (i.e. low power indicates a high probability of Type II error). In the context of environmental monitoring, a Type II error is made when it is concluded that no environmental impact has occurred even though one has. Type II errors have been ignored relative to Type I errors (the mistake of concluding that there is an impact when one has not occurred), the rates of which

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39

Abbas, Kamran, Zamir Hussain, Noreen Rashid, et al. "Bayesian Estimation of Gumbel Type-II Distribution under Type-II Censoring with Medical Applications." Computational and Mathematical Methods in Medicine 2020 (March 26, 2020): 1–11. http://dx.doi.org/10.1155/2020/1876073.

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The time to event or survival time usually follows certain skewed probability distributions. These distributions encounter vital role using the Bayesian framework to analyze and project the maximum life expectancy in order to inform decision-making. The Bayesian method provides a flexible framework for monitoring the randomized clinical trials to update what is already known using prior information about specific phenomena under uncertainty. Additionally, medical practitioners can use the Bayesian estimators to measure the probability of time until tumor recurrence, time until cardiovascular d

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40

Reyad, Hesham, and Soha Othman Ahmed. "E-Bayesian analysis of the Gumbel type-ii distribution under type-ii censored scheme." International Journal of Advanced Mathematical Sciences 3, no. 2 (2015): 108. http://dx.doi.org/10.14419/ijams.v3i2.5093.

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<p>This paper seeks to focus on Bayesian and E-Bayesian estimation for the unknown shape parameter of the Gumbel type-II distribution based on type-II censored samples. These estimators are obtained under symmetric loss function [squared error loss (SELF))] and various asymmetric loss functions [LINEX loss function (LLF), Degroot loss function (DLF), Quadratic loss function (QLF) and minimum expected loss function (MELF)]. Comparisons between the E-Bayesian estimators with the associated Bayesian estimators are investigated through a simulation study.</p>

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41

Zelnitskaya, Elizaveta N., Alexey M. Litovchenko, and Alexandra A. Moskaleva. "I and II Type Errors Effects in Law Enforcement." Scientific Research of Faculty of Economics. Electronic Journal 10, no. 4 (2018): 77–89. http://dx.doi.org/10.38050/2078-3809-2018-10-4-77-98.

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Different approaches to the definition of I and II type errors, the diagnostics of their emergence and the evaluation of their emergence consequences are considered in this article. An overview of the probability evaluation methods for I and II type error commitment in the law enforcement is provided. A comparative analysis of the judiciary functioning quality based on the survey about respondents’ confidence in the judiciary functioning quality in Russian Federation and several OECD countries was performed. Judicial statistics of the 160 articles of the Criminal Code of Russian Federation was

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42

Izmirlian, Grant, Lev A. Sirota, Vance W. Berger, and Victor Kipnis. "The fundamentals of multiplicity adjustment in biostatistics." JNCI Monographs 2025, no. 68 (2025): 10–13. https://doi.org/10.1093/jncimonographs/lgae050.

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Abstract The statistical problem of multiplicity is concerned with making protected multiple inferences and their valid interpretation in a particular study. Most discussions of multiplicity focus on the increase of type I error rate if testing is done without any adjustment, with only a few papers discussing its ramifications for type II errors/power. We provide a survey of main approaches to protected inference in biomedical studies, touching on procedures to control the family-wise error rate, false discovery rate, as well as false discovery exceedance probability. We discuss several notion

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43

Chen, Tao, Tong Kang та Jun Li. "An A-ϕ Scheme for Type-II Superconductors". East Asian Journal on Applied Mathematics 7, № 4 (2017): 658–78. http://dx.doi.org/10.4208/eajam.141016.300517a.

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AbstractA fully discrete A-ϕ finite element scheme for a nonlinear model of type-II superconductors is proposed and analyzed. The nonlinearity is due to a field dependent conductivity with the regularized power-law form. The challenge of this model is the error estimate for the nonlinear term under the time derivative. Applying the backward Euler method in time discretisation, the well-posedness of the approximation problem is given based on the theory of monotone operators. The fully discrete system is derived by standard finite element method. The error estimate is suboptimal in time and spa

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44

Cunningham, William A., and Timothy R. Koscik. "Balancing Type I and Type II error concerns in fMRI through compartmentalized analysis." Cognitive Neuroscience 8, no. 3 (2017): 147–49. http://dx.doi.org/10.1080/17588928.2017.1299122.

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Aldona, Yusufira, Pinta Deniyanti Sampoerno, Lukman El Hakim, and Dwi Antari Wijayanti. "Giving an Individual Tutoring for 8th Grade Student in the Errors of Solving the Problem of Essay Test for Comparative Material in SMPN 232 Jakarta." Journal of Medives : Journal of Mathematics Education IKIP Veteran Semarang 6, no. 1 (2022): 135. http://dx.doi.org/10.31331/medivesveteran.v6i1.1946.

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This study aims to describe students’ errors in solving the problem of essay test for comparative material based on the type of errors, describe the causes of students making errors, and conduct individual tutoring to minimize and overcome students’ errors. This is action research that conducted in three cycles with Research Subject (RS) for four students of class VIII F SMPN 232 Jakarta. Each RS represents one type of error based on the results of error analysis on initial test that given during pre-cycle, successive factual error, conceptual error, operation error, and principle of error. Th

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46

Mahapoonyanont, Natcha, and Suwichaya Putuptim. "The Power of t-test with large sample size under the different condition of sample size and significance level between real data, transformed data and data from monte carlo simulation technique." 11th GLOBAL CONFERENCE ON BUSINESS AND SOCIAL SCIENCES 11, no. 1 (2020): 108. http://dx.doi.org/10.35609/gcbssproceeding.2020.11(108).

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The power of test is the probability that the test rejects the null hypothesis (H0) when a specific alternative hypothesis (H1) is true. The probability of occurrence of a type I error is modelled on medical research that tried to avoid the type I error, such as testing of new medicines, etc. The statistical significance level must be set to be as small as possible, and the probability of type II error would be considered later. In behavioural sciences and social sciences research, the researcher wants to avoid a type I error by determining the level of statistical significance. There are argu

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47

Jainer, A. K., A. N. Singh, and N. Soni. "Light therapy for seasonal affective disorder: A type II error." British Journal of Psychiatry 179, no. 3 (2001): 270. http://dx.doi.org/10.1192/bjp.179.3.270-a.

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何, 进. "Error Analysis of Cubic Spline Function Under Type II Interpolation." Pure Mathematics 14, no. 10 (2024): 19–29. http://dx.doi.org/10.12677/pm.2024.1410340.

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Milovanović, Gradimir V., Miodrag M. Spalević, and Miroslav S. Pranić. "Bounds of the error of Gauss–Turán-type quadratures, II." Applied Numerical Mathematics 60, no. 1-2 (2010): 1–9. http://dx.doi.org/10.1016/j.apnum.2009.08.002.

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Evans, C. "Melatonin secretion and anorexia nervosa – a serious type II error." British Journal of Psychiatry 153, no. 1 (1988): 121. http://dx.doi.org/10.1192/bjp.153.1.121.

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