• Journal of Sasang Constitutional Medicine
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• v.22 no.4
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• pp.49-55
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• 2010

1. Objectives The purpose of this study is to assess the statistical methods used in the Journal of Sasang Constitutional Medicine and to identify the types of errors in the statistical analysis. 2. Methods We have reviewed quantitative articles published in the Journal of Sasang Constitutional Medicine from 2000 to 2009. Except for the case report, a total of 505 articles was reviewed by year. We focused on the methods of statistics in these articles. We have analysed the number of thesis used the statistical methods, the number of the statistical methods, the types of the statistical methods, and the errors of the statistical methods with the object of 202 thesis used the statistical method. 3. Results 1. Of the 370 articles, 202 used inferential statistics. 2. The most dominant was the thesis with one type of the statistical method forming 61.9% (125 thesis) of the total. 3. The ANOVA was the statistic of choice overall (40.1%), followed by the t-test (35.6%). 4. By examining the errors of the statistical methods, there were 49 thesis with errors among 81 thesis using ANOVA, 25 thesis among 72 thesis using t-test, and 18 over 62 thesis using chi-squared test. 4. Conclusions We have found various mistakes or misuses in the applications of statistical methodologies in the articles published in the Journal of Sasang Constitutional Medicine. It is necessary to present the cases of inappropriate statistical methods in order to improve the quality of academic researches. So long as Sasang constitutional medicine uses the statistical methods, researchers should fully examine and be aware of the statistical methods which they wish to use through other research articles, and statistic publications.

• Journal of Korean Medicine Rehabilitation
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• v.20 no.4
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• pp.105-130
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• 2010

Objectives : The purpose of this study was to assess the statistical methods errors used in the journal of Oriental Rehabilitation Medicine(JORM) and to identify the types of errors in statistical analysis. Methods : We reviewed quantitative articles that were published in the JORM from January 2005 through October 2009. Those were not used by statistical analysis such as literature studies, case study, review articles were not included in this analysis. A total of 296 articles was reviewed. We evaluated the adequacy and the validity of the statistical techniques with our checklist established be modified Lee's checklist, and three statistical evaluators assessed together to minimize bias. Results : Of the 222 articles, 213 were used in inferential and descriptive statistics. Of those 80% of articles adopting descriptive and inferential statistics were detected having statistical errors. One articles used 1.7 statistical method unit generally. Most frequently employed statistics were student t-test, one way ANOVA. pearson correlation analysis, Mann-whitney U test, paired t-test, and chi-square test in their order. However, most frequent statistics having errors were similar in order. The most common statistic errors were as follow: 1. absence of normality test, 2. misuse between paired test and unpaired test, 3. wrong choice of repeated measures analysis without consideration of time variables, 4, increase of Type I error by using inappropriate multiple test, 5. inappropriate application of discrete or categorical data instead of continuous data in correlation analysis, 6. poor consideration of basic consumption in chi-square test, 7. confusion between frequency comparison and average comparison, 8. mentioning the statistical technique without using it. Conclusions : We found various mistake or misuses in the applications of statistical methodologies in the articles published in the JORM. Careful consideration of statistical use and review from the specialist of statistics are warranted for improving the quality of JORM.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.35-42
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• 1998

Once a contingency table is constructed, the first interest will be the hypotheses of either homogeneity or independence depending on the sampling scheme. The most widely used test statistic in practice is the classical Pearson's $\chi^2$ statistic. When the null hypothesis is rejected, another natural interest becomes which cell contributed to the rejection of the null hypothesis more than others. For this purpose, so called cell $\chi^2$ components are investigated. In this paper, the influence function of a cell to the $\chi^2$ statistic is derived, which can be used for the same purpose. This function measures the effect of each cell to the $\chi$$^2$ statistic. A numerical example is given to demonstrate the role of the new function.

• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.225-230
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• 1996

We propose a statistical test for the nonlinear combiner logics which are usually combined with two maximal Linear Feedback Shift Registers and generate pseudorandom bit sequences. This test uses the mutual information between the output and set of inputs which will be a random variable and its distribution is obeyed to an approximate $\{chi}^2$ -distribution. We adopt this statistic to a $\{chi}^2$ -test of independence by using contingency table. We also apply a proposed test to some non-linear crptosystems and show that this useful to evaluate the strength of the cryptosystems.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.777-781
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• 2010

Yates' continuity correction of the chi-squared test for testing the homogeneity of two binomial proportions in $2{\times}2$ contingency tables is developed to lower the value of the test statistic slightly. The effect of continuity correction is expected to decrease as the sample size increases. However, in extremely unbalanced $2{\times}2$ contingency tables, we find some cases where the effect of continuity correction is eccentric and is larger than expected. In such cases, we conclude that the chi-squared test with continuity correction should not be employed as a test statistic in both asymptotic tests and exact tests.

• Restorative Dentistry and Endodontics
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• v.42 no.2
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• pp.152-155
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• 2017

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.323-327
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• 2004

In this paper, we investigate the limiting distribution of the Ljung-Box test statistic in the unit root AR models with ARCH errors. We show that the limiting distribution is approximately chi-square distribution with the degrees of freedom only depending on the number of autocorrelation lags appearing in the test. Some simulation results are provided for illustration.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.525-534
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• 2003

If a restriction is imposed only to a (proper) subset of parameters of interest, we call it a local restriction. Statistical inference under a local restriction in multinomial setting is studied. The maximum likelihood estimation under a local restriction and likelihood ratio tests for and against a local restriction are discussed. A real data is analyzed for illustrative purpose.

• Journal of the Korean Statistical Society
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• v.25 no.3
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• pp.381-391
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• 1996

In the analysis of contingency table with ordered categories, the relationship between odds for adjacent categories has received con-siderable interest. We consider likelihood ratio tests of independence against an order restriction on odds in 2 $\times$ k contingency tables.

• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.349-365
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• 1994

We develop in this paper the likelihood ratio test (LRT) for testing $H_1 : F_1 \preceq F_2$ against $H_2 - H_1$ where $H_2$ imposes no restriction on $F_1$ and $F_2$ and '$\preceq$' means failure rate ordering. Both one and two-sample problems will be considered. In the one-sample case, one of the two distributions is known, while we assume in the other case both are unknown. We derive the asymptotic null distribution of the LRT statistic which will be of chi-bar-square type. The main issue here is to determine the least favorable distribution which is stochastically largest within the class of null distributions.