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Journal articles on the topic 'Statistical hypothesis testing'

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

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Ren, Dianxu. "Understanding Statistical Hypothesis Testing." Journal of Emergency Nursing 35, no. 1 (January 2009): 57–59. http://dx.doi.org/10.1016/j.jen.2008.09.020.

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12

Sharma, Narendra K. "Hypothesis statement and statistical testing: a tutorial." BOHR International Journal of Operations Management Research and Practices 1, no. 1 (2022): 53–59. http://dx.doi.org/10.54646/bijomrp.2022.07.

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Many researchers and beginners in social research have several dilemmas and confusion in their minds aboutthe hypothesis statement and statistical testing of hypotheses. A distinction between the research hypothesis andstatistical hypotheses and understanding the limitations of the historically used null hypothesis statistical testingis useful in clarifying these doubts. This article presents some data from the published research articles to supportthe view that theisformat as well as thewillformat is appropriate to stating hypotheses. The article that presentsa social research framework to present the research hypothesis and statistical hypotheses in a proper perspective.
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13

K. Sharma, Narendra. "Hypothesis Statement and Statistical Testing: A Tutorial." BOHR International Journal of Operations Management Research and Practices 1, no. 1 (2022): 52–58. http://dx.doi.org/10.54646/bijomrp.007.

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Many researchers and beginners in social research have several dilemmas and confusion in their mind about hypothesis statement and statistical testing of hypotheses. A distinction between the research hypothesis and statistical hypotheses, and understanding the limitations of the historically used null hypothesis statistical testing, is useful in clarifying these doubts. This article presents some data from the published research articles to support the view that the is format as well as the will format is appropriate to stating hypotheses. The article presents a social research framework to present the research hypothesis and statistical hypotheses is proper perspective.
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14

Sedgwick, P. "Pitfalls of statistical hypothesis testing: multiple testing." BMJ 349, aug29 1 (August 29, 2014): g5310. http://dx.doi.org/10.1136/bmj.g5310.

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15

Sedgwick, P. "Errors when statistical hypothesis testing." BMJ 340, may05 1 (May 5, 2010): c2348. http://dx.doi.org/10.1136/bmj.c2348.

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16

Lieber, Richard L. "Statistical significance and statistical power in hypothesis testing." Journal of Orthopaedic Research 8, no. 2 (March 1990): 304–9. http://dx.doi.org/10.1002/jor.1100080221.

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17

Young, Linda J., and Jerry H. Young. "Alternative View of Statistical Hypothesis Testing." Environmental Entomology 20, no. 5 (October 1, 1991): 1241–45. http://dx.doi.org/10.1093/ee/20.5.1241.

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18

Matloff, Norman S. "Statistical Hypothesis Testing: Problems and Alternatives." Environmental Entomology 20, no. 5 (October 1, 1991): 1246–50. http://dx.doi.org/10.1093/ee/20.5.1246.

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19

Turner, Dana P., Hao Deng, and Timothy T. Houle. "Statistical Hypothesis Testing: Overview and Application." Headache: The Journal of Head and Face Pain 60, no. 2 (February 2020): 302–8. http://dx.doi.org/10.1111/head.13706.

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20

Petrie, A. "Statistical power in testing a hypothesis." Journal of Bone and Joint Surgery. British volume 92-B, no. 9 (September 2010): 1192–94. http://dx.doi.org/10.1302/0301-620x.92b9.25069.

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21

Emmert-Streib, Frank, and Matthias Dehmer. "Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference." Machine Learning and Knowledge Extraction 1, no. 3 (August 12, 2019): 945–61. http://dx.doi.org/10.3390/make1030054.

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Statistical hypothesis testing is among the most misunderstood quantitative analysis methods from data science. Despite its seeming simplicity, it has complex interdependencies between its procedural components. In this paper, we discuss the underlying logic behind statistical hypothesis testing, the formal meaning of its components and their connections. Our presentation is applicable to all statistical hypothesis tests as generic backbone and, hence, useful across all application domains in data science and artificial intelligence.
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22

TANI, Hiroaki. "Statistical Method. Hypothesis testing and Estimation.(Part2)." Journal of exercise physiology 4, no. 2 (1989): 93–97. http://dx.doi.org/10.1589/rika1986.4.93.

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23

Weed, Richard M., Robert L. Schmitt, Sam Owusu-Ababio, and Erik V. Nordheim. "Ranking Procedure Based on Statistical Hypothesis Testing." Transportation Research Record: Journal of the Transportation Research Board 1991, no. 1 (January 2007): 12–18. http://dx.doi.org/10.3141/1991-02.

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24

Chen, Kuen-Suan, Shui-Chuan Chen, Chang-Hsien Hsu, and Wei-Zong Chen. "Statistical Hypothesis Testing for Asymmetric Tolerance Index." Applied Sciences 11, no. 14 (July 6, 2021): 6249. http://dx.doi.org/10.3390/app11146249.

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Many of the nominal-the-best quality characteristics of important machine tool components, such as inner or outer diameters, have asymmetric tolerances. An asymmetric tolerance index is a function for the average of the process and the standard deviation. Unfortunately, it is difficult to obtain the 100(1−α)% confidence interval of the index. Therefore, this study adopts Boole’s inequality and DeMorgan’s theorem to find the combined confidence region for the average of the process as well as the standard deviation. Next, using the asymmetric tolerance index for the target function and the combined confidence region for the feasible region, this study applies mathematical programming to find the confidence interval as well as employs this confidence interval for statistical hypothesis testing. Lastly, this study demonstrates the applicability of the proposed approach with an illustrative example.
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25

Chao Liu, Long Fei, Xifeng Yan, Jiawei Han, and S. P. Midkiff. "Statistical Debugging: A Hypothesis Testing-Based Approach." IEEE Transactions on Software Engineering 32, no. 10 (October 2006): 831–48. http://dx.doi.org/10.1109/tse.2006.105.

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26

Anderson, H. Glenn, Michael G. Kendrach, and Shana Trice. "Understanding Statistical and Clinical Significance: Hypothesis Testing." Journal of Pharmacy Practice 11, no. 3 (June 1998): 181–95. http://dx.doi.org/10.1177/089719009801100309.

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This primer reviews a number of statistical concepts integral to the hypothesis testing process and its role in decision making. Concepts of variables, scales of measure, and measures of central tendency and dispersion are discussed, and a 5-step process of hypothesis testing is presented. Finally, a discussion of the statistical and clinical significance of research results is presented, along with the concept of confidence intervals as a method of conveying information about the effect size as well as the statistical significance of a difference between groups.
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27

Reijsbergen, Daniël, Pieter-Tjerk de Boer, Werner Scheinhardt, and Boudewijn Haverkort. "On hypothesis testing for statistical model checking." International Journal on Software Tools for Technology Transfer 17, no. 4 (October 1, 2014): 377–95. http://dx.doi.org/10.1007/s10009-014-0350-1.

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28

Xia, Yinglin, and Jun Sun. "Hypothesis testing and statistical analysis of microbiome." Genes & Diseases 4, no. 3 (September 2017): 138–48. http://dx.doi.org/10.1016/j.gendis.2017.06.001.

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29

Govindarajulu, Z. "The sequential statistical analysis of hypothesis testing." European Journal of Operational Research 44, no. 1 (January 1990): 126–27. http://dx.doi.org/10.1016/0377-2217(90)90326-7.

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30

Kelen, G. D., Charles G. Brown, and James Ashton. "Statistical reasoning in clinical trials: Hypothesis testing." American Journal of Emergency Medicine 6, no. 1 (January 1988): 52–61. http://dx.doi.org/10.1016/0735-6757(88)90207-0.

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31

Hung, Jung-Lin, Cheng-Che Chen, and Chun-Mei Lai. "Possibility Measure of Accepting Statistical Hypothesis." Mathematics 8, no. 4 (April 9, 2020): 551. http://dx.doi.org/10.3390/math8040551.

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Taking advantage of the possibility of fuzzy test statistic falling in the rejection region, a statistical hypothesis testing approach for fuzzy data is proposed in this study. In contrast to classical statistical testing, which yields a binary decision to reject or to accept a null hypothesis, the proposed approach is to determine the possibility of accepting a null hypothesis (or alternative hypothesis). When data are crisp, the proposed approach reduces to the classical hypothesis testing approach.
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32

Choi, Jeong-Seok. "Biostatistics for Multiple Testing." Korean Journal of Otorhinolaryngology-Head and Neck Surgery 63, no. 3 (March 21, 2020): 97–100. http://dx.doi.org/10.3342/kjorl-hns.2020.00164.

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Multiple testings are instances that contain simultaneous tests for more than one hypothesis. When multiple testings are conducted at the same time, it is more likely that the null hypothesis is rejected, even if the null hypothesis is correct. If individual hypothesis decisions are based on unadjusted <i>p</i>-values, it is usually more likely that some of the true null hypotheses will be rejected. In order to solve the multiple testing problems, various studies have attempted to increase the power by taking into account the family-wise error rate or false discovery rate and statistics required for testing hypotheses. This article discuss methods that account for the multiplicity issue and introduces various statistical techniques.
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33

Rigby, Alan S. "Statistical methods in epidemiology: I. Statistical errors in hypothesis testing." Disability and Rehabilitation 20, no. 4 (January 1998): 121–26. http://dx.doi.org/10.3109/09638289809166071.

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34

Yang, Da. "Interval Estimation and Hypothesis Testing." Applied Mechanics and Materials 543-547 (March 2014): 1717–20. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1717.

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Mathematical statistics is a branch of mathematics has extensive application of interval estimation and hypothesis testing, which are two important problems of statistical inference. As two important statistical inference methods, interval estimation and hypothesis testing problem is more and more widely used in the field of economic management, finance and insurance, scientific research, engineering technology, the science of decision functions are recognized by more and more people. Can go further to establish mutual influence and communication between the interval estimation and hypothesis testing, can use the theory to explain the problem of interval estimation of parameter hypothesis test, this is an important problem to improve the statistical inference theory. Therefore, the basis on the internal relations between the interval estimation and hypothesis test for deep research, explain the problem of hypothesis testing and interval estimation from the point of view, discusses the difference and connection between the two.
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35

SLEEP, D. J. H., M. C. DREVER, and T. D. NUDDS. "Statistical Versus Biological Hypothesis Testing: Response to Steidl." Journal of Wildlife Management 71, no. 7 (2007): 2120. http://dx.doi.org/10.2193/2007-140.

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36

Shekhar, Amit, and Sachin Patel. "Statistical Hypothesis Testing: Tool for Drawing Meaningful Conclusions." International Journal of Computer Applications 184, no. 10 (April 30, 2022): 1–4. http://dx.doi.org/10.5120/ijca2022921967.

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37

Sarmukaddam, SanjeevB. "Interpreting "statistical hypothesis testing" results in clinical research." Journal of Ayurveda and Integrative Medicine 3, no. 2 (2012): 65. http://dx.doi.org/10.4103/0975-9476.96518.

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38

Haktanır, Elif, and Cengiz Kahraman. "Z-fuzzy hypothesis testing in statistical decision making." Journal of Intelligent & Fuzzy Systems 37, no. 5 (November 22, 2019): 6545–55. http://dx.doi.org/10.3233/jifs-182700.

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39

Nakagawa, K., and F. Kanaya. "On the converse theorem in statistical hypothesis testing." IEEE Transactions on Information Theory 39, no. 2 (March 1993): 623–28. http://dx.doi.org/10.1109/18.212293.

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40

Bernstein, Joseph, Kevin McGuire, and Kevin B. Freedman. "Statistical Sampling and Hypothesis Testing in Orthopaedic Research." Clinical Orthopaedics and Related Research 413 (August 2003): 55–62. http://dx.doi.org/10.1097/01.blo.0000079769.06654.8c.

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41

JAKŠIĆ, V., Y. OGATA, C. A. PILLET, and R. SEIRINGER. "QUANTUM HYPOTHESIS TESTING AND NON-EQUILIBRIUM STATISTICAL MECHANICS." Reviews in Mathematical Physics 24, no. 06 (June 17, 2012): 1230002. http://dx.doi.org/10.1142/s0129055x12300026.

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We extend the mathematical theory of quantum hypothesis testing to the general W*-algebraic setting and explore its relation with recent developments in non-equilibrium quantum statistical mechanics. In particular, we relate the large deviation principle for the full counting statistics of entropy flow to quantum hypothesis testing of the arrow of time.
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42

Zhu, Hongjie, and Man Luo. "Chemical structure informing statistical hypothesis testing in metabolomics." Bioinformatics 30, no. 4 (December 5, 2013): 514–22. http://dx.doi.org/10.1093/bioinformatics/btt708.

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43

Kim, Munchurl, and Jinwoong Kim. "Moving video object segmentation using statistical hypothesis testing." Electronics Letters 36, no. 2 (2000): 128. http://dx.doi.org/10.1049/el:20000180.

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44

Park, Chanseok. "Statistical hypothesis testing for dot-matrix type products." International Journal of Quality Engineering and Technology 1, no. 1 (2009): 27. http://dx.doi.org/10.1504/ijqet.2009.030499.

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45

Kanatani, K. "Statistical Foundation for Hypothesis Testing of Image Data." CVGIP: Image Understanding 60, no. 3 (November 1994): 382–91. http://dx.doi.org/10.1006/ciun.1994.1064.

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46

Kanatani, K. "Statistical Foundation for Hypothesis Testing of Image Data." Computer Vision and Image Understanding 60, no. 3 (November 1994): 382–91. http://dx.doi.org/10.1006/cviu.1994.1070.

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47

Keeler, Courtney, and Alexa Colgrove Curtis. "Introduction to Statistical Hypothesis Testing in Nursing Research." AJN, American Journal of Nursing 123, no. 7 (July 2023): 53–55. http://dx.doi.org/10.1097/01.naj.0000944936.37768.29.

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48

Cooper, Robert A. "Making Decisions with Data: Understanding Hypothesis Testing & Statistical Significance." American Biology Teacher 81, no. 8 (October 1, 2019): 535–42. http://dx.doi.org/10.1525/abt.2019.81.8.535.

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Statistical methods are indispensable to the practice of science. But statistical hypothesis testing can seem daunting, with P-values, null hypotheses, and the concept of statistical significance. This article explains the concepts associated with statistical hypothesis testing using the story of “the lady tasting tea,” then walks the reader through an application of the independent-samples t-test using data from Peter and Rosemary Grant's investigations of Darwin's finches. Understanding how scientists use statistics is an important component of scientific literacy, and students should have opportunities to use statistical methods like this in their science classes.
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49

de Almeida, Vicente Nejar, Eduardo Ribeiro, Nassim Bouarour, João Luiz Dihl Comba, and Sihem Amer-Yahia. "SHEVA: A Visual Analytics System for Statistical Hypothesis Exploration." Proceedings of the VLDB Endowment 16, no. 12 (August 2023): 4102–5. http://dx.doi.org/10.14778/3611540.3611631.

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We demonstrate SHEVA, a System for Hypothesis Exploration with Visual Analytics. SHEVA adopts an Exploratory Data Analysis (EDA) approach to discovering statistically-sound insights from large datasets. The system addresses three longstanding challenges in Multiple Hypothesis Testing: (i) the likelihood of rejecting the null hypothesis by chance, (ii) the pitfall of not being representative of the input data, and (iii) the ability to navigate among many data regions while preserving the user's train of thought. To address (i) & (ii), SHEVA implements significance adjustment methods that account for data-informed properties such as coverage and novelty. To address (iii), SHEVA proposes to guide users by recommending one-sample and two-sample hypotheses in a stepwise fashion following a data hierarchy. Users may choose from a collection of pre-trained hypothesis exploration policies and let SHEVA guide them through the most significant hypotheses in the data, or intervene to override suggested hypotheses. Furthermore, SHEVA relies on data-to-visual element mappings to convey hypothesis testing results in an interpretable fashion, and allows hypothesis pipelines to be stored and retrieved later to be tested on new datasets.
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50

Casabianca, Jodi M., and Charles Lewis. "Statistical Equivalence Testing Approaches for Mantel–Haenszel DIF Analysis." Journal of Educational and Behavioral Statistics 43, no. 4 (December 1, 2017): 407–39. http://dx.doi.org/10.3102/1076998617742410.

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The null hypothesis test used in differential item functioning (DIF) detection tests for a subgroup difference in item-level performance—if the null hypothesis of “no DIF” is rejected, the item is flagged for DIF. Conversely, an item is kept in the test form if there is insufficient evidence of DIF. We present frequentist and empirical Bayes approaches for implementing statistical equivalence testing for DIF using the Mantel–Haenszel (MH) DIF statistic. With these approaches, rejection of the null hypothesis of “DIF” allows the conclusion of statistical equivalence, a more stringent criterion for keeping items. In other words, the roles of the null and alternative hypotheses are interchanged in order to have positive evidence that the DIF of an item is small. A simulation study compares the equivalence testing approaches to the traditional MH DIF detection method with the Educational Testing Service classification system. We illustrate the methods with item response data from the 2012 Programme for International Student Assessment.
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