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Journal articles on the topic 'Chi-square test'

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

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1

Kilic, Selim. "Chi-square Test." Journal of Mood Disorders 6, no. 3 (2016): 180. http://dx.doi.org/10.5455/jmood.20160803110534.

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2

Daya, Salim. "Chi-square test." Evidence-based Obstetrics & Gynecology 3, no. 1 (March 2001): 3–4. http://dx.doi.org/10.1054/ebog.2001.0223.

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3

Franke, Todd Michael, Timothy Ho, and Christina A. Christie. "The Chi-Square Test." American Journal of Evaluation 33, no. 3 (November 8, 2011): 448–58. http://dx.doi.org/10.1177/1098214011426594.

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4

Pandis, Nikolaos. "The chi-square test." American Journal of Orthodontics and Dentofacial Orthopedics 150, no. 5 (November 2016): 898–99. http://dx.doi.org/10.1016/j.ajodo.2016.08.009.

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5

Germogenov, A. P., and A. F. Ronzhin. "A Sequential Chi-Square Test." Theory of Probability & Its Applications 29, no. 2 (January 1985): 397–403. http://dx.doi.org/10.1137/1129052.

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6

Wu, Jyh-Shyang, and Wen-Shuenn Deng. "Averaged shifted chi-square test." Journal of Nonparametric Statistics 24, no. 1 (March 2012): 39–57. http://dx.doi.org/10.1080/10485252.2011.608849.

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7

Koletsi, Despina, and Nikolaos Pandis. "The chi-square test for trend." American Journal of Orthodontics and Dentofacial Orthopedics 150, no. 6 (December 2016): 1066–67. http://dx.doi.org/10.1016/j.ajodo.2016.10.001.

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8

Leorato, S. "A chi-square-type test for covariances." Journal of Nonparametric Statistics 18, no. 2 (February 2006): 159–80. http://dx.doi.org/10.1080/10485250600687051.

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9

บวรกิติวงศ์, สุชาดา. "ความแกร่งของสถิติทดสอบไคสแควร์ Robustness of the Chi-square Test." Journal of Education Studies 44, no. 3 (July 2016): 212–20. http://dx.doi.org/10.58837/chula.educu.44.3.14.

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10

Rofiqoh, Siti Nur Indah, Ririn Triratnasari, Nelly Rahmatillah, Fatin Fadhilah Hasib, and Alimin Alimin. "Uji chi-square pada Penelitian Ekonomi Syariah." International Conference on Islamic Economic (ICIE) 2, no. 1 (July 13, 2023): 152–61. http://dx.doi.org/10.58223/icie.v2i1.200.

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In Islamic economic research, we may collect categorical variable data. Using statistical analysis, chi-square test can be an alternative to evaluate those data. Furthermore, the analysis of chi-square test can be aimed at evaluating the differences between study groups according to the assign degree of error. This article also explains the purpose of chi-square test, the Karl Person chi-square hypothesis, what type of data can use in chi-square test, the assumptions of chi-square analysis and also the rule to analyze categorical variables data in sharia economics research.
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11

Kneidel, Ken. "How the Chi-Square Test Works: A Lesson." American Biology Teacher 58, no. 7 (October 1, 1996): 420–23. http://dx.doi.org/10.2307/4450195.

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12

Alalouf, I. S. "The Chi Square Test With Both Margins Fixed." Communications in Statistics - Theory and Methods 16, no. 1 (January 1987): 29–43. http://dx.doi.org/10.1080/03610928708829350.

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13

Norman, Geofrey R., and David L. Streiner. "Body counts and biostatistics: the chi-square test." Community Oncology 7, no. 6 (June 2010): 286–87. http://dx.doi.org/10.1016/s1548-5315(11)70571-x.

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14

Da, Ren, and Ching-Fang Lin. "Sensitivity Analysis of the State Chi-Square Test." IFAC Proceedings Volumes 29, no. 1 (June 1996): 6596–601. http://dx.doi.org/10.1016/s1474-6670(17)58741-8.

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15

Curtis, Keith, and Scott T. Youngquist. "Part 21: Categoric Analysis: Pearson Chi-Square Test." Air Medical Journal 32, no. 4 (July 2013): 179–80. http://dx.doi.org/10.1016/j.amj.2013.04.007.

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16

Pavlov, A. V. "Large deviation theorems for the chi-square test." Russian Mathematical Surveys 51, no. 1 (February 28, 1996): 158–59. http://dx.doi.org/10.1070/rm1996v051n01abeh002760.

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17

Onoghojobi, B., and N. P. Olewuezi. "CHI-SQUARE STATISTIC AS A SCORE TEST STATISTIC." Far East Journal of Theoretical Statistics 51, no. 1-2 (December 1, 2015): 1–15. http://dx.doi.org/10.17654/ts051120001.

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18

Viollaz, A. J. "On the reliability of the chi-square test." Metrika 33, no. 1 (December 1986): 135–42. http://dx.doi.org/10.1007/bf01894740.

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19

Kahn, James A., and Masao Ogaki. "A chi-square test for a unit root." Economics Letters 34, no. 1 (September 1990): 37–42. http://dx.doi.org/10.1016/0165-1765(90)90178-4.

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20

Nurhasanah, Nurhasanah, Rito Goejantoro, and Suyitno Suyitno. "Penerapan Metode Klasifikasi Chi-Square Automatic Interaction Detection dan Exhaustive Chi-Square Automatic Interaction Detection." EKSPONENSIAL 13, no. 1 (June 9, 2022): 29. http://dx.doi.org/10.30872/eksponensial.v13i1.877.

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The Chi-Square Automatic Interaction Detection (CHAID) and Exhaustive CHAID methods are nonparametric statistical methods that can be used to classify. CHAID and Exhaustive CHAID were used to determine the significant relationship between the dependent variable and the independent variables based on the chi-square independence test. This study was applied to data on the study period of students of FMIPA UNMUL batch 2014. Based on the CHAID and Exhaustive CHIAD methods, it can be seen that the dependent variable of the study period has a significant relationship with the independent variable, namely the study program and GPA predicate. Where students who graduated on time for the Statistics, Biology and Chemistry study program with a satisfactory GPA predicate of 82 students and with a very satisfactory GPA predicate and cum laude with 46 students. Meanwhile, students who did not graduate on time for the Statistics, Biology and Chemistry study program with an adequate GPA predicate of 5 students, a satisfactory GPA predicate of 41 students, very satisfactory and cum laude with 3 students. Students who graduated on time for the Physics study program were 13 students and those who did not graduate on time were 34 students. The chi-square independence test performed on the CHAID method uses fewer possible categorical pairs than the Exhaustive CHAID method which uses all possible categorical pairs so that it requires a long computational and calculation time.
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21

Singhal, Richa, and Rakesh Rana. "Chi-square test and its application in hypothesis testing." Journal of the Practice of Cardiovascular Sciences 1, no. 1 (2015): 69. http://dx.doi.org/10.4103/2395-5414.157577.

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22

Adekpedjou, Akim, WITHANAGE A. De Mel, and Gideon KD Zamba. "Data Dependent Cells Chi-Square Test With Recurrent Events." Scandinavian Journal of Statistics 42, no. 4 (April 24, 2015): 1045–64. http://dx.doi.org/10.1111/sjos.12150.

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23

Da, Ren, and Ching-Fang Lin. "Sensitivity analysis algorithm for the state chi-square test." Journal of Guidance, Control, and Dynamics 19, no. 1 (January 1996): 219–22. http://dx.doi.org/10.2514/3.21601.

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24

Daya, Salim. "Chi-square test for larger (r×c) contingency tables." Evidence-based Obstetrics & Gynecology 3, no. 2 (June 2001): 59–60. http://dx.doi.org/10.1054/ebog.2001.0248.

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25

Daya, Salim. "Chi-square test for trend (2×c contingency table)." Evidence-based Obstetrics & Gynecology 3, no. 3 (September 2001): 116–17. http://dx.doi.org/10.1054/ebog.2001.0275.

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26

GLINER, JEFFREY A., GEORGE A. MORGAN, and ROBERT J. HARMON. "The Chi-Square Test and Accompanying Effect Size Indices." Journal of the American Academy of Child & Adolescent Psychiatry 41, no. 12 (December 2002): 1510–12. http://dx.doi.org/10.1097/00004583-200212000-00024.

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27

Ottenbacher, Kenneth J. "The chi-square test: Its use in rehabilitation research." Archives of Physical Medicine and Rehabilitation 76, no. 7 (July 1995): 678–81. http://dx.doi.org/10.1016/s0003-9993(95)80639-3.

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28

Fong, Joseph, Shi Ming Huang, and Hsiang Yuan Hsueh. "Online analytical mining association rules using Chi-square test." International Journal of Business Intelligence and Data Mining 2, no. 3 (2007): 311. http://dx.doi.org/10.1504/ijbidm.2007.015487.

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29

Dijkers, Marcel P. "Misuse of the Pearson chi-square test of association." Archives of Physical Medicine and Rehabilitation 86, no. 3 (March 2005): 602. http://dx.doi.org/10.1016/j.apmr.2004.12.019.

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30

Ahad, Nor Aishah, Friday Zinzendoff Okwonu, Joshua Sarduana Apanapudor, Festus Irimisose Arunaye, and Sunday Amoju Ojobor. "Chi-square and Adjusted Standardised Residual Analysis." ASM Science Journal 18 (November 6, 2023): 1–11. http://dx.doi.org/10.32802/asmscj.2023.985.

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In hypothesis testing, inference is made by comparing the computed test statistic and the critical value which rely on a specified level of significance and degrees of freedom. This paper examines various study variables to assess whether there exists an interdependency between relationship intimacy and these variables. The chi-square test, likelihood ratio test, the adjusted standardised residual, and the proposed benchmark methods are applied to determine the acceptance or rejection of the null hypothesis and the individual category contribution that enhanced the inference. The comparative analysis involves data collected from a survey conducted before the onset of the Covid-19 pandemic, during the Covid-19 lockdown period, and following the relaxation of post-Covid-19 lockdown measures spanning a duration of six weeks. The findings showed that the null hypothesis cannot be rejected implying that the study variables are independent of relationship intimacy for the periods under study. The adjusted standardised residual and the benchmark methods revealed that sexual intimacy and quarrel are the highest variable contributors to the acceptance of the null hypothesis.
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31

Wang, Hung-Hsiang, and Chih-Ping Chen. "Comparison of Chi-Square Test and Representative Decision Tree in Features that Influence Vehicle Style." International Journal of Machine Learning and Computing 11, no. 5 (September 2021): 333–38. http://dx.doi.org/10.18178/ijmlc.2021.11.5.1057.

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32

Lugo-Armenta, Jesus Guadalupe, Luis Roberto Pino-Fan, and Blanca Rosa Ruiz Hernandez. "Chi-square Reference Meanings: a Historical-epistemological Overview." Revemop 3 (June 21, 2021): e202108. http://dx.doi.org/10.33532/revemop.e202108.

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The present article shows a historical-epistemological study on the Chi-square statistic. In which theoretical-methodological notions from the Onto-Semiotic Approach (OSA) of mathematical cognition and instruction were used to identify four problems that have been key to the evolution of the Chi-square statistic: the Goodness-of-fit-test, the test of independence, the test of homogeneity and distribution. Furthermore, various meanings of the Chi-square statistic were recognized in the mathematical-statistical practices that are used to solve each of those problems. These meanings could help to establish epistemic criteria that allow, on the one hand, to propose progressive levels of inferential reasoning for the statistic (from informal to formal); and on the other hand, to design tasks oriented to promote the understanding of the diverse meanings of the Chi-square.
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33

Kang, Hongzhaoning, Gang Liu, Zhengping Wu, Yumin Tian, and Lizhi Zhang. "A Modified FlowDroid Based on Chi-Square Test of Permissions." Entropy 23, no. 2 (January 30, 2021): 174. http://dx.doi.org/10.3390/e23020174.

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Android devices are currently widely used in many fields, such as automatic control, embedded systems, the Internet of Things and so on. At the same time, Android applications (apps) always use multiple permissions, and permissions can be abused by malicious apps that disclose users’ privacy or breach the secure storage of information. FlowDroid has been extensively studied as a novel and highly precise static taint analysis for Android applications. Aiming at the problem of complex detection and false alarms in FlowDroid, an improved static detection method based on feature permission and risk rating is proposed. Firstly, the Chi-square test is used to extract correlated permissions related to malicious apps, and mutual information is used to cluster the permissions to generate feature permission clusters. Secondly, risk calculation method based on permissions and combinations of permissions are proposed to identify dangerous data flows. Experiments show that this method can significantly improve detection efficiency while maintaining the accuracy of dangerous data flow detection.
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34

Hinoue, Tomohiko, Atsuko Miyaji, and Takatomi Wada. "The Security of RC6 against Asymmetric Chi-square Test Attack." IPSJ Digital Courier 3 (2007): 600–609. http://dx.doi.org/10.2197/ipsjdc.3.600.

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35

Inglot, Tadeusz, and Alicja Janic-Wróblewska. "Data driven chi-square test for uniformity with unequal cells." Journal of Statistical Computation and Simulation 73, no. 8 (August 2003): 545–61. http://dx.doi.org/10.1080/0094965021000060918.

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36

Brumback, B., and M. Srinath. "A Chi-square test for fault-detection in Kalman filters." IEEE Transactions on Automatic Control 32, no. 6 (June 1987): 552–54. http://dx.doi.org/10.1109/tac.1987.1104658.

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37

Daya, Salim. "Paired comparisons in contingency tables – the McNemar chi-square test." Evidence-based Obstetrics & Gynecology 4, no. 2 (June 2002): 56–57. http://dx.doi.org/10.1054/ebog.2002.0050.

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38

Bai, Z. D., and Xuming He. "A chi-square test for dimensionality with non-Gaussian data." Journal of Multivariate Analysis 88, no. 1 (January 2004): 109–17. http://dx.doi.org/10.1016/s0047-259x(03)00056-3.

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39

Jung, Sin-Ho, Seung-Ho Kang, and Chul Ahn. "Chi-Square Test for R×CContingency Tables with Clustered Data." Journal of Biopharmaceutical Statistics 13, no. 2 (January 4, 2003): 241–51. http://dx.doi.org/10.1081/bip-120019269.

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40

Gilbert, Gregory E., and Susan Prion. "Making Sense of Methods and Measurement: The Chi-Square Test." Clinical Simulation in Nursing 12, no. 5 (May 2016): 145–46. http://dx.doi.org/10.1016/j.ecns.2015.12.013.

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41

Kumar, Ashok. "The Chi-Square test: a frequency data based statistical device." Journal of Universal College of Medical Sciences 3, no. 3 (December 31, 2015): 53–55. http://dx.doi.org/10.3126/jucms.v3i3.24250.

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“Cause Effect” relationships are very important and play a most vital and significant role in medical researchers to reveal efficacy of medication as preventive and curative measures towards control and eradication of various diseases and morbidity factors. The present paper, in this direction is an attempt to highlight the main applicability of - test ( chi- square test ) ; a most appropriate statistical device in testing statistical significance of the above mentioned relationships in studying impact of one on the other ; supplemented by a few citations thereof.
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42

Satorra, Albert, and Peter M. Bentler. "Ensuring Positiveness of the Scaled Difference Chi-square Test Statistic." Psychometrika 75, no. 2 (June 20, 2009): 243–48. http://dx.doi.org/10.1007/s11336-009-9135-y.

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43

White, Wes. "Connecting Independence and the Chi-Square Statistic." Mathematics Teacher 94, no. 2 (February 2001): 134–36. http://dx.doi.org/10.5951/mt.94.2.0134.

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The chi-square statistic is used to test goodness of fit, homogeneity of proportions, and independence. Although the first two uses were at least somewhat new to my students, the concept of independence was not. This article begins by reviewing the use of the chi-square statistic for goodness of fit and then looks at how I have helped my students relate their earlier work with independence to the use of the chi-square statistic.
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44

Berry, Kenneth J., and Paul W. Mielke. "An Asymmetric Test of Homogeneity of Proportions." Psychological Reports 87, no. 1 (August 2000): 259–65. http://dx.doi.org/10.2466/pr0.2000.87.1.259.

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An asymmetrical test of homogeneity of proportions possesses distinct advantages over a symmetrical test. The symmetric chi square test of homogeneity is widely employed in psychological research. An asymmetrical alternative to the chi square test of homogeneity is proposed and described.
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45

Syoffnelli, Syoffnelli, Zulfan Saam, and Thamrin Thamrin. "Pengaruh Program Adiwiyata Terhadap Pengetahuan Perilaku dan Keterampilan Siswa dan Guru dalam Mengelola Lingkungan pada SMK di Kabupaten Pelalawan." Dinamika Lingkungan Indonesia 3, no. 1 (January 16, 2016): 16. http://dx.doi.org/10.31258/dli.3.1.p.16-23.

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Abstrak: The study aimed to determine adiwiyata program effects on knowledge, behaviour and skills of students and teachers also compared schools that have implemented the adiwiyata program with schools that have not implemented adiwiyata program. This study is a quantitative descriptive research. The subjects were students and teachers of SMK Negeri 1 Pangkalan Kerinci and SMK Negeri 1 Bunut with total 194 people. The data collection methode used were questionnaires, interviews and observations, data analysis technique using Chi-square test with SPSS version 20 for windows. Results of the study Chi Square test showed between SMK 1 Pangkalan Kerinci with SMKN 1 Bunut about students' knowledge Chi Square count 140.437 and Chi Square table 101.879, student behavior Chi Square count 102.920 and Chi Square tables 101.879 and attitudes Chi Square count 101 994 and Chi Square tables 101.879. Knowledge, attitudes and skills of teachers Chi Square test results Chi Square count of 10,000 and Chi Square Table 18.307. It can be concluded that the existence of a significant difference of knowledge, attitudes and skills of students between SMK 1 Pangkalan Kerinci with SMKN 1 Bunut, because the value of Chi Square count is greater than the value of Chi Square table and there was no significant difference about knowledge, attitudes and skills teacher between SMKN 1 Pangkalan Kerinci with SMKN 1 Bunut, because there is a calculated value of Chi Square is smaller than the value of Chi Square table.
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46

OLANUSI, Dr J. A., and S. A. SAMUEL. "Application of Chi-Square Test to Determine Architectural Impact on Church Patronage." International Journal of Research and Innovation in Social Science VII, no. VI (2023): 605–16. http://dx.doi.org/10.47772/ijriss.2023.7646.

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This paper provides a crucial analysis of chi square test. The concept of chi square test as well as the various and varying scales of disparity in chi square test availability are discussed. The Chi-square is the most commonly used significance test for categorical variables in the social sciences as well as architecture The tool has been employed as a means of assessing the relationship between two categorical variables as tabulated against each other in a contingency table. The test compares the actual values in the cells of the table with those that would be expected under conditions of independence (i.e., if there was no relationship between the variables being considered). Expected values are calculated for each cell by cross-multiplying the row and column proportions for that cell and taking as a share of the total number of cases considered. The chi square tool has been used to ascertain whether or not relationship exist between architecture and church patronage. The result is in the affirmative indicating that the reason for observed growth could be a combination of the spiritual as well as the physical aesthetic evocation of the church buildings and its environment. Therefore recommends professional architectural design for church patronage and growth.
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47

Kossovsky, Alex Ely. "On the Mistaken Use of the Chi-Square Test in Benford’s Law." Stats 4, no. 2 (May 28, 2021): 419–53. http://dx.doi.org/10.3390/stats4020027.

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Benford’s Law predicts that the first significant digit on the leftmost side of numbers in real-life data is distributed between all possible 1 to 9 digits approximately as in LOG(1 + 1/digit), so that low digits occur much more frequently than high digits in the first place. Typically researchers, data analysts, and statisticians, rush to apply the chi-square test in order to verify compliance or deviation from this statistical law. In almost all cases of real-life data this approach is mistaken and without mathematical-statistics basis, yet it had become a dogma or rather an impulsive ritual in the field of Benford’s Law to apply the chi-square test for whatever data set the researcher is considering, regardless of its true applicability. The mistaken use of the chi-square test has led to much confusion and many errors, and has done a lot in general to undermine trust and confidence in the whole discipline of Benford’s Law. This article is an attempt to correct course and bring rationality and order to a field which had demonstrated harmony and consistency in all of its results, manifestations, and explanations. The first research question of this article demonstrates that real-life data sets typically do not arise from random and independent selections of data points from some larger universe of parental data as the chi-square approach supposes, and this conclusion is arrived at by examining how several real-life data sets are formed and obtained. The second research question demonstrates that the chi-square approach is actually all about the reasonableness of the random selection process and the Benford status of that parental universe of data and not solely about the Benford status of the data set under consideration, since the focus of the chi-square test is exclusively on whether the entire process of data selection was probable or too rare. In addition, a comparison of the chi-square statistic with the Sum of Squared Deviations (SSD) measure of distance from Benford is explored in this article, pitting one measure against the other, and concluding with a strong preference for the SSD measure.
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48

Onesime, Mbulayi, Zhenyu Yang, and Qi Dai. "Genomic Island Prediction via Chi-Square Test and Random Forest Algorithm." Computational and Mathematical Methods in Medicine 2021 (May 24, 2021): 1–9. http://dx.doi.org/10.1155/2021/9969751.

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Genomic islands are related to microbial adaptation and carry different genomic characteristics from the host. Therefore, many methods have been proposed to detect genomic islands from the rest of the genome by evaluating its sequence composition. Many sequence features have been proposed, but many of them have not been applied to the identification of genomic islands. In this paper, we present a scheme to predict genomic islands using the chi-square test and random forest algorithm. We extract seven kinds of sequence features and select the important features with the chi-square test. All the selected features are then input into the random forest to predict the genome islands. Three experiments and comparison show that the proposed method achieves the best performance. This understanding can be useful to design more powerful method for the genomic island prediction.
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49

Chen, Zhongxue, Tong Lin, and Kai Wang. "A Powerful Variant-Set Association Test Based on Chi-Square Distribution." Genetics 207, no. 3 (September 14, 2017): 903–10. http://dx.doi.org/10.1534/genetics.117.300287.

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50

Berry, Kenneth J., and Paul Mielke. "An APL function for Radlow and Alf’s exact chi-square test." Behavior Research Methods, Instruments, & Computers 17, no. 1 (January 1985): 131–32. http://dx.doi.org/10.3758/bf03200912.

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