Relevant bibliographies by topics / Statistical hypothesis testing

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

Academic literature on the topic 'Statistical hypothesis testing'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Statistical hypothesis testing"

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Eberly, Lynn E., and Susan E. Telke. "Statistical Hypothesis Testing." Journal of Wound, Ostomy and Continence Nursing 38, no. 1 (2011): 18–20. http://dx.doi.org/10.1097/won.0b013e3182032698.

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Eberly, Lynn E., and Susan E. Telke. "Statistical Hypothesis Testing." Journal of Wound, Ostomy and Continence Nursing 38, no. 2 (2011): 128–31. http://dx.doi.org/10.1097/won.0b013e31820acff7.

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Telke, Susan E., and Lynn E. Eberly. "Statistical Hypothesis Testing." Journal of Wound, Ostomy and Continence Nursing 38, no. 3 (2011): 225–30. http://dx.doi.org/10.1097/won.0b013e3182172627.

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Telke, Susan E., and Lynn E. Eberly. "Statistical Hypothesis Testing." Journal of Wound, Ostomy and Continence Nursing 38, no. 4 (2011): 351–54. http://dx.doi.org/10.1097/won.0b013e3182226e57.

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Telke, Susan E., and Lynn E. Eberly. "Statistical Hypothesis Testing." Journal of Wound, Ostomy and Continence Nursing 38, no. 5 (2011): 496–500. http://dx.doi.org/10.1097/won.0b013e31822b7fc8.

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Telke, Susan E., and Lynn E. Eberly. "Statistical Hypothesis Testing." Journal of Wound, Ostomy and Continence Nursing 38, no. 6 (2011): 621–26. http://dx.doi.org/10.1097/won.0b013e31823428a8.

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Marino, Ralph J. "Statistical hypothesis testing." Archives of Physical Medicine and Rehabilitation 76, no. 6 (June 1995): 587–88. http://dx.doi.org/10.1016/s0003-9993(95)80518-4.

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Sedgwick, P. "Statistical hypothesis testing." BMJ 340, apr21 1 (April 21, 2010): c2059. http://dx.doi.org/10.1136/bmj.c2059.

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Expósito-Ruiz, M., S. Pérez-Vicente, and F. Rivas-Ruiz. "Statistical inference: Hypothesis testing." Allergologia et Immunopathologia 38, no. 5 (September 2010): 266–77. http://dx.doi.org/10.1016/j.aller.2010.06.003.

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Sedgwick, P. "Understanding statistical hypothesis testing." BMJ 348, may30 1 (May 30, 2014): g3557. http://dx.doi.org/10.1136/bmj.g3557.

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Dissertations / Theses on the topic "Statistical hypothesis testing"

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Hardy, James C. (James Clifford). "A Monte Carlo Study of the Robustness and Power Associated with Selected Tests of Variance Equality when Distributions are Non-Normal and Dissimilar in Form." Thesis, University of North Texas, 1990. https://digital.library.unt.edu/ark:/67531/metadc332130/.

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When selecting a method for testing variance equality, a researcher should select a method which is robust to distribution non-normality and dissimilarity. The method should also possess sufficient power to ascertain departures from the equal variance hypothesis. This Monte Carlo study examined the robustness and power of five tests of variance equality under specific conditions. The tests examined included one procedure proposed by O'Brien (1978), two by O'Brien (1979), and two by Conover, Johnson, and Johnson (1981). Specific conditions included assorted combinations of the following factors: k=2 and k=3 groups, normal and non-normal distributional forms, similar and dissimilar distributional forms, and equal and unequal sample sizes. Under the k=2 group condition, a total of 180 combinations were examined. A total of 54 combinations were examined under the k=3 group condition. The Type I error rates and statistical power estimates were based upon 1000 replications in each combination examined. Results of this study suggest that when sample sizes are relatively large, all five procedures are robust to distribution non-normality and dissimilarity, as well as being sufficiently powerful.
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Jeng, Tian-Tzer. "Some contributions to asymptotic theory on hypothesis testing when the model is misspecified /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487332636473942.

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Bauer, Laura L. "Hypothesis testing procedures for non-nested regression models." Diss., Virginia Polytechnic Institute and State University, 1987. http://hdl.handle.net/10919/74755.

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Theory often indicates that a given response variable should be a function of certain explanatory variables yet fails to provide meaningful information as to the specific form of this function. To test the validity of a given functional form with sensitivity toward the feasible alternatives, a procedure is needed for comparing non-nested families of hypotheses. Two hypothesized models are said to be non-nested when one model is neither a restricted case nor a limiting approximation of the other. These non-nested hypotheses cannot be tested using conventional likelihood ratio procedures. In recent years, however, several new approaches have been developed for testing non-nested regression models. A comprehensive review of the procedures for the case of two linear regression models was presented. Comparisons between these procedures were made on the basis of asymptotic distributional properties, simulated finite sample performance and computational ease. A modification to the Fisher and McAleer JA-test was proposed and its properties investigated. As a compromise between the JA-test and the Orthodox F-test, it was shown to have an exact non-null distribution. Its properties, both analytically and empirically derived, exhibited the practical worth of such an adjustment. A Monte Carlo study of the testing procedures involving non-nested linear regression models in small sample situations (n ≤ 40) provided information necessary for the formulation of practical guidelines. It was evident that the modified Cox procedure, N̄ , was most powerful for providing correct inferences. In addition, there was strong evidence to support the use of the adjusted J-test (AJ) (Davidson and MacKinnon's test with small-sample modifications due to Godfrey and Pesaran), the modified JA-test (NJ) and the Orthodox F-test for supplemental information. Under non normal disturbances, similar results were yielded. An empirical study of spending patterns for household food consumption provided a practical application of the non-nested procedures in a large sample setting. The study provided not only an example of non-nested testing situations but also the opportunity to draw sound inferences from the test results.
Ph. D.
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Li, Longzhuang. "Statistical methods for performance evaluation and their applications /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3060118.

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Williams, Elliot. "Essays in multiple comparison testing /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3112194.

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Hahn, Georg. "Statistical methods for Monte-Carlo based multiple hypothesis testing." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/25279.

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Statistical hypothesis testing is a key technique to perform statistical inference. The main focus of this work is to investigate multiple testing under the assumption that the analytical p-values underlying the tests for all hypotheses are unknown. Instead, we assume that they can be approximated by drawing Monte Carlo samples under the null. The first part of this thesis focuses on the computation of test results with a guarantee on their correctness, that is decisions on multiple hypotheses which are identical to the ones obtained with the unknown p-values. We present MMCTest, an algorithm to implement a multiple testing procedure which yields correct decisions on all hypotheses (up to a pre-specified error probability) based solely on Monte Carlo simulation. MMCTest offers novel ways to evaluate multiple hypotheses as it allows to obtain the (previously unknown) correct decision on hypotheses (for instance, genes) in real data studies (again up to an error probability pre-specified by the user). The ideas behind MMCTest are generalised in a framework for Monte Carlo based multiple testing, demonstrating that existing methods giving no guarantees on their test results can be modified to yield certain theoretical guarantees on the correctness of their outputs. The second part deals with multiple testing from a practical perspective. We assume that in practice, it might also be desired to sacrifice the additional computational effort needed to obtain guaranteed decisions and to invest it instead in the computation of a more accurate ad-hoc test result. This is attempted by QuickMMCTest, an algorithm which adaptively allocates more samples to hypotheses whose decisions are more prone to random fluctuations, thereby achieving an improved accuracy. This work also derives the optimal allocation of a finite number of samples to finitely many hypotheses under a normal approximation, where the optimal allocation is understood as the one minimising the expected number of erroneously classified hypotheses (with respect to the classification based on the analytical p-values). An empirical comparison of the optimal allocation of samples to the one computed by QuickMMCTest indicates that the behaviour of QuickMMCTest might not be too far away from being optimal.
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Smith, Toni Michelle. "An investigation into student understanding of statistical hypothesis testing." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8565.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.
Thesis research directed by: Dept. of Curriculum and Instruction. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Sheng, Ru. "A Bayesian analysis of hypothesis testing problems with skewed alternatives." [Milwaukee, Wis.] : e-Publications@Marquette, 2009. http://epublications.marquette.edu/dissertations_mu/23.

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Wang, Yishi. "Some new tests for normality." Diss., Online access via UMI:, 2006.

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Miyanishi, Masako. "Essays on hypothesis testing in the presence of nearly integrated variables." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3222053.

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Thesis (Ph. D.)--University of California, San Diego, 2006.
Title from first page of PDF file (viewed September 20, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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Books on the topic "Statistical hypothesis testing"

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Lehmann, E. L. Testing statistical hypotheses. 2nd ed. New York: Springer, 1986.

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Lehmann, E. L. Testing statistical hypotheses. 2nd ed. New York: Springer, 1997.

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1960-, Romano Joseph P., ed. Testing statistical hypotheses. 3rd ed. New York: Springer, 2005.

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Shi, Ning-Zhong. Statistical hypothesis testing: Theory and methods. Singapore: World Scientific Publishing, 2008.

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Jian, Tao, ed. Statistical hypothesis testing: Theory and methods. Singapore: World Scientific Publishing, 2008.

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Mohr, Lawerence B. Understanding significance testing. Newbury Park, Calif: Sage Publications, 1990.

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Taeger, Dirk, and Sonja Kuhnt. Statistical Hypothesis Testing with SAS and R. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118762585.

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Wilcox, Rand R. Introduction to robust estimation and hypothesis testing. 3rd ed. Amsterdam: Academic Press, 2012.

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Wilcox, Rand R. Introduction to robust estimation and hypothesis testing. 3rd ed. Amsterdam: Academic Press, 2012.

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Landenna, Giampiero. La verifica di ipotesi statistiche. Bologna: Mulino, 1998.

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Book chapters on the topic "Statistical hypothesis testing"

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Abramovich, Felix, and Ya’acov Ritov. "Hypothesis Testing." In Statistical Theory, 49–78. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003175407-4.

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Casella, George, and Roger L. Berger. "Hypothesis Testing." In Statistical Inference, 302–36. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003456285-8.

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Prasad, Sahana. "Hypothesis Testing." In Elementary Statistical Methods, 147–240. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0596-4_4.

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Härdle, Wolfgang, and Léopold Simar. "Hypothesis Testing." In Applied Multivariate Statistical Analysis, 183–216. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05802-2_7.

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Härdle, Wolfgang Karl, and Léopold Simar. "Hypothesis Testing." In Applied Multivariate Statistical Analysis, 213–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45171-7_7.

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Härdle, Wolfgang Karl, and Léopold Simar. "Hypothesis Testing." In Applied Multivariate Statistical Analysis, 193–226. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-17229-8_7.

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Kiefer, Jack Carl. "Hypothesis Testing." In Introduction to Statistical Inference, 246–86. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4613-9578-2_8.

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Härdle, Wolfgang Karl, and Léopold Simar. "Hypothesis Testing." In Applied Multivariate Statistical Analysis, 195–229. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26006-4_7.

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Andersson, Jimmy. "Hypothesis Testing." In Statistical Analysis with Swift, 109–34. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7765-2_5.

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Almudevar, Anthony. "Hypothesis Testing." In Theory of Statistical Inference, 133–54. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003049340-5.

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Conference papers on the topic "Statistical hypothesis testing"

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Varshney, Kush R., and Lav R. Varshney. "Multilevel minimax hypothesis testing." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967633.

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Zitzmann, Cathel, Remi Cogranne, Florent Retraint, Igor Nikiforov, Lionel Fillatre, and Philippe Cornu. "Hypothesis testing by using quantized observations." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967743.

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Gul, Gokhan, and Abdelhak M. Zoubir. "Robust hypothesis testing with composite distances." In 2014 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2014. http://dx.doi.org/10.1109/ssp.2014.6884668.

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Sanghavi, Sujay, Vincent Tan, and Alan Willsky. "Learning Graphical Models for Hypothesis Testing." In 2007 IEEE/SP 14th Workshop on Statistical Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/ssp.2007.4301220.

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Sameni, Reza, and Christian Jutten. "A Hypothesis Testing Approach to Nonstationary Source Separation." In 2021 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2021. http://dx.doi.org/10.1109/ssp49050.2021.9513811.

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Penna, Federico, Henk Wymeersch, and Vladimir Savic. "Uniformly reweighted belief propagation for distributed Bayesian hypothesis testing." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967807.

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Haji, Mehdi, Kalyan Asis Sahoo, Tien D. Bui, Ching Y. Suen, and Dominique Ponson. "Statistical Hypothesis Testing for Handwritten Word Segmentation Algorithms." In 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2012. http://dx.doi.org/10.1109/icfhr.2012.272.

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PAVEL, Maria, and Dorin PAVEL. "Testarea ipotezelor de cercetare prin statistica Bayesiană." In Inter/transdisciplinary approaches in the teaching of the real sciences, (STEAM concept) = Abordări inter/transdisciplinare în predarea ştiinţelor reale, (concept STEAM). Ion Creangă Pedagogical State University, 2023. http://dx.doi.org/10.46727/c.steam-2023.p339-343.

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The paper highlights the restrictions of classical statistics for testing research hypotheses formulated by researchers in the field of social sciences, including the psychopedagogical field, and describes the alternative to the model of testing the significance of the null hypothesis, as a new statistical model - Bayesian statistics. This model is called "Null Hypothesis Bayesian Testing" (NHBT) and involves using Bayes factors instead of significance p values.
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Guderlei, Ralph, and Johannes Mayer. "Statistical Metamorphic Testing Testing Programs with Random Output by Means of Statistical Hypothesis Tests and Metamorphic Testing." In Seventh International Conference on Quality Software (QSIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/qsic.2007.4385527.

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Fazai, Radhia, Majdi Mansouri, Kamal Abodayeh, Mohamed Trabelsi, Hazem Nounou, and Mohamed Nounou. "Machine Learning-Based Statistical Hypothesis Testing for Fault Detection." In 2019 4th Conference on Control and Fault Tolerant Systems (SysTol). IEEE, 2019. http://dx.doi.org/10.1109/systol.2019.8864776.

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Reports on the topic "Statistical hypothesis testing"

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Martinez, Melissa. Hypothesis Testing. ConductScience, June 2022. http://dx.doi.org/10.55157/cs20220615.

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Hypothesis testing is a statistical method used to evaluate the validity of a potential outcome within a defined significance level, comparing it with an alternative hypothesis. It involves establishing null and alternative hypotheses, making assumptions, calculating a test statistic, and selecting a significance level. The decision to accept or reject the null hypothesis is based on the observed test statistic. Terminologies include null and alternative hypotheses, critical region, critical value, errors, p-value, power of a test, and more. Hypothesis testing is crucial in statistical inference and offers insights into relationships between variables. Practical applications range from courtroom trials to gender ratio analysis and behavioral effects studies. However, it's important to note the potential limitations and biases in the application of hypothesis testing.
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Kent, Jonathan, and Caroline Wallbank. The use of hypothesis testing in transport research. TRL, February 2021. http://dx.doi.org/10.58446/rrzh8247.

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Hypothesis testing is a well-used statistical method to evaluate whether a proposition is true or false. A fundamental part of the testing procedure is the calculation and interpretation of a p-value, which represents the probability of a set of data being observed, under the assumption that the proposition is true. This null hypothesis is then rejected if the p-value is less than a certain threshold, usually 0.05. In recent years, some members of the scientific community have called into question the validity of the hypothesis testing approach, because it places so much emphasis on whether or not a value is above or below an arbitrary threshold. We think that hypothesis testing is still a valid method, but it is important that, as well as the p-value additional information such as effect sizes is taken into account when interpreting results. In addition, there are alternative approaches, such as equivalence testing or Bayesian hypothesis testing, which should be considered in certain circumstances.
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Raj, Sunny, Sumit Kumar Jha, Laura L. Pullum, and Arvind Ramanathan. Statistical Hypothesis Testing using CNN Features for Synthesis of Adversarial Counterexamples to Human and Object Detection Vision Systems. Office of Scientific and Technical Information (OSTI), May 2017. http://dx.doi.org/10.2172/1361358.

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Panchenko, Liubov, and Andrii Khomiak. Education Statistics: Looking for Case-Study for Modeling. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4461.

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The article deals with the problem of using modeling in social statistics courses. It allows the student-researcher to build one-dimensional and multidimensional models of the phenomena and processes that are being studied. Social Statistics course programs from foreign universities (University of Arkansas; Athabasca University; HSE University, Russia; McMaster University, Canada) are analyzed. The article provides an example using the education data set – Guardian UK universities ranking in Social Statistics course. Examples of research questions are given, data analysis for these questions is performed (correlation, hypothesis testing, discriminant analysis). During the research the discriminant model with group variable – modified Guardian score – and 9 predictors: course satisfaction, teaching quality, feedback, staff-student ratio, money spent on each student and other) was built. Lower student’s satisfaction with feedback was found to be significantly different from the satisfaction with teaching. The article notes the modeling and statistical analysis should be accompanied by a meaningful interpretation of the results. In this example, we discussed the essence of university ratings, the purpose of Guardian rating, the operationalization and measurement of such concepts as satisfaction with teaching, feedback; ways to use statistics in education, data sources etc. with students. Ways of using this education data in group and individual work of students are suggested.
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Cattaneo, Matias D., Richard K. Crump, and Weining Wang. Beta-Sorted Portfolios. Federal Reserve Bank of New York, July 2023. http://dx.doi.org/10.59576/sr.1068.

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Beta-sorted portfolios—portfolios comprised of assets with similar covariation to selected risk factors—are a popular tool in empirical finance to analyze models of (conditional) expected returns. Despite their widespread use, little is known of their statistical properties in contrast to comparable procedures such as two-pass regressions. We formally investigate the properties of beta-sorted portfolio returns by casting the procedure as a two-step nonparametric estimator with a nonparametric first step and a beta-adaptive portfolios construction. Our framework rationalizes the well-known estimation algorithm with precise economic and statistical assumptions on the general data generating process. We provide conditions that ensure consistency and asymptotic normality along with new uniform inference procedures allowing for uncertainty quantification and general hypothesis testing for financial applications. We show that the rate of convergence of the estimator is non-uniform and depends on the beta value of interest. We also show that the widely used Fama-MacBeth variance estimator is asymptotically valid but is conservative in general and can be very conservative in empirically relevant settings. We propose a new variance estimator, which is always consistent and provide an empirical implementation which produces valid inference. In our empirical application we introduce a novel risk factor—a measure of the business credit cycle—and show that it is strongly predictive of both the cross-section and time-series behavior of U.S. stock returns.
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Dolor, Jason Mark. Investigating Statistics Teachers' Knowledge of Probability in the Context of Hypothesis Testing. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5914.

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Nuttall, Albert H., and Paul M. Baggenstoss. Joint Distributions for Two Useful Classes of Statistics, With Applications to Classification and Hypothesis Testing. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada477141.

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Fife, Dustin. Simplistics: An Intuitive Graphical Approach to Statistics. Instats Inc., 2023. http://dx.doi.org/10.61700/d47gzlztthikk469.

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This hands-on course explores statistics from a fresh, intuitive perspective and supports R, JASP, and Jamovi, with an easy to use and understand graphical approach to statistical analysis. Rather than focusing on decisions about the appropriate 'test' or using p-values and arbitrary 'significance' cutoffs, this seminar will teach you to build models and use them to interpret data directly. With these models, you will learn how to generate and interpret model visualizations and effect sizes in an intuitive manner, as well as building, evaluating, and testing competing models. The net result will be a much better understanding of your data and the relationships among variables through the process of modeling, and an improved ability to test your hypotheses and theory in a more inductive 'bottom-up' fashion that is grounded in your data. An official Instats certificate of completion is provided and the seminar offers 2 ECTS Equivalent points.
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Amengual, Dante, Xinyue Bei, Marine Carrasco, and Enrique Sentana. Score-type tests for normal mixtures. CIRANO, January 2023. http://dx.doi.org/10.54932/uxsg1990.

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Testing normality against discrete normal mixtures is complex because some parameters turn increasingly underidentified along alternative ways of approaching the null, others are inequality constrained, and several higher-order derivatives become identically 0. These problems make the maximum of the alternative model log-likelihood function numerically unreliable. We propose score-type tests asymptotically equivalent to the likelihood ratio as the largest of two simple intuitive statistics that only require estimation under the null. One novelty of our approach is that we treat symmetrically both ways of writing the null hypothesis without excluding any region of the parameter space. We derive the asymptotic distribution of our tests under the null and sequences of local alternatives. We also show that their asymptotic distribution is the same whether applied to observations or standardized residuals from heteroskedastic regression models. Finally, we study their power in simulations and apply them to the residuals of Mincer earnings functions.
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