• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.317-323
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• 2010

Measures are very useful tools for comparing the shape variability in statistical shape analysis. For examples, the Procrustes statistic(PS) is isolated measure, and the mean Procrustes statistic(MPS) and the root mean square measure(RMS) are overall measures. But these measures are very subjective, complicated and moreover these measures are not statistical for comparing the shape variability. Therefore we need to study some tests. It is well known that the Hotelling's $T^2$ test is used for testing shape variability of two independent samples. And for testing shape variabilities of several independent samples, instead of the Hotelling's $T^2$ test, one way analysis of variance(ANOVA) can be applied. In fact, this one way ANOVA is based on the balanced samples of equal size which is called as BANOVA. However, If we have unbalanced samples with unequal size, we can not use BANOVA. Therefore we propose the unbalanced analysis of variance(UNBANOVA) for testing shape variabilities of several independent samples of unequal size.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.357-365
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• 2019

Purposes: This study aimed to investigate the differences in the health-related quality of life (EQ-5D) in Korean adults according to gender, age period, and the metabolic syndrome or not. Methods: The study subjects were 5,021 adults, who completed all of the KNHANES VI-2. The subjects were categorized, using life periods, into young, middle-aged, and older adults. Methods were selected based on the KNHANES VI-2. IBM SPSS 25.0 was used for a complex samples cross-tabulation, general linear model and a complex samples t-test. Results: The HRQoL (EQ-5D) was higher in male(p<.001), in the young group by age(p<.001), and in the non-metabolic syndrome group(p>.05). In condition of the metabolic syndrome by gender, the female group's EQ-5D was higher in the non-metabolic syndrome group; there was the result of the complex samples independent t-test revealed significant differences in EQ-5D(p<.01). Conclusion: The HRQoL (EQ-5D) was higher in male, young, and in the non-metabolic syndrome group. Also, in condition of metabolic syndrome in female, there was the result of the complex samples independent t-test revealed significant differences in EQ-5D.

• Journal of Veterinary Clinics
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• v.26 no.5
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• pp.401-407
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• 2009

In the previous article in this series we introduced the basic concepts for statistical analysis. The present review introduces hypothesis testing for continuous and categorical data for readers of the veterinary science literature. For the analysis of continuous data, we explained t-test to compare a single mean with a hypothesized value and the difference between two means from two independent samples or between two means arising from paired samples. When the data are categorical variables, the $x^2$ test for association and homogeneity, Fisher's exact test and Yates' continuity correction for small samples, and test for trend, in which at least one of the variables is ordinal is described, together with the worked examples. McNemar test for correlated proportions is also discussed. The topics covered may provide a basic understanding of different approaches for analyzing clinical data.

• Journal of Integrative Natural Science
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• v.12 no.3
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• pp.91-99
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• 2019

Many researchers in various study fields use the two sample t-test to confirm their treatment effects. The two sample t-test is generally used for small samples, and assumes that two independent random samples are selected from normal populations, and the population variances are unknown. Researchers often conduct F-test, the test of equality of variances, before testing the treatment effects, and the test statistic or confidence interval for the two sample t-test has two formats according to whether the variances are equal or not. Researchers using the two sample t-test often want to know how large sample sizes they need to get reliable test results. This research gives some guidelines for sample sizes to them through simulation works. The simulation had run for normal populations with the different ratios of two variances for different sample sizes (${\leq}30$). The simulation results are as follows. First, if one has no idea equality of variances but he/she can assume the difference is moderate, it is safe to use sample size at least 20 in terms of the nominal level of significance. Second, the power of F-test for the equality of variances is very low when the sample sizes are small (<30) even though the ratio of two variances is equal to 2. Third, the sample sizes at least 10 for the two sample t-test are recommendable in terms of the nominal level of significance and the error limit.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.653-660
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• 2020

The paper examines the impact of proctoring environments on student performance in two different exam proctoring environments: online versus offline proctored exams. This study employs a set of aggregated data from 1,762 students over the eight-year period from 2009 to 2016 in a university. Although there were nine courses offered, they could have been counted more than once as students may appear several times to take exams for different courses. This study employs independent samples t-test and regression analysis to compare the means of two independent groups and to test the hypothesis. The results of the independent samples t-test and the regression analysis indicate that there is no difference in the mean scores of exams and, therefore, the findings suggest that the exam proctoring environment is unlikely related to student performance even when students take their exams either in online proctoring or offline proctoring environments. This study concludes that the proctoring environment unlikely results in a statistically significant difference of exam scores and, thus, the exam proctoring environment does not appear to cause any change in student performance. The findings suggest that the exam proctoring environments does not appear to impact on student academic achievements and assessments.

• Restorative Dentistry and Endodontics
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• v.44 no.3
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• pp.26.1-26.6
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• 2019

• Journal of Integrative Natural Science
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• v.14 no.3
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• pp.123-129
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• 2021

This research examines the effect of positively skewed population distribution on the two sample t-test through simulation. For simulation work, two independent samples were selected from the same chi-square distributions with 3, 5, 10, 15, 20, 30 degrees of freedom and sample sizes 3, 5, 10, 15, 20, 30, respectively. Chi-square distribution is largely skewed to the right at small degrees of freedom and getting symmetric as the degrees of freedom increase. Simulation results show that the sampled populations are distributed positively skewed like chi-square distribution with small degrees of freedom, the F-test for the equality of variances shows poor performances even at the relatively large degrees of freedom and sample sizes like 30 for both, and so it is recommended to avoid using F-test. When two population variances are equal, the skewness of population distribution does not affect on the t-test in terms of the confidence level. However even though for the highly positively skewed distribution and small sample sizes like three or five the t-test achieved the nominal confidence level, the error limits are very large at small sample size. Therefore, if the sampled population is expected to be highly skewed to the right, it will be recommended to use relatively large sample size, at least 20.

• Journal of Genetic Medicine
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• v.12 no.2
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• pp.96-99
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• 2015

Purpose: To evaluate the performance of the Momguard noninvasive prenatal test by tracing the 'screen positive' results based on preliminary samples from Korean cohorts. Materials and Methods: This preliminary study is based on data collected by the LabGenomics Clinical Laboratory (Seongnam, Korea) with informed consent. Only pregnant women who underwent both the Momguard test and karyotyping were included in this study. Momguard test results were compared with those of the karyotyping analysis. Results: Among the 38 cases with 'screen positive' results by Momguard, 30 cases also had karyotyping results available. In three trisomy (T) 18 and three T13 cases, the Momguard results were concordant with the karyotyping results. For the T21 cases, except for one case belonging to the mid-risk zone, Momguard results from 23 out of 24 cases matched the karyotyping results. Conclusion: Momguard is a highly reliable screening tool for detecting T13, T18, and T21 cases in independent Korean cohort samples.

• The Journal of Korean Academic Society of Nursing Education
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• v.21 no.1
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• pp.28-35
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• 2015

Purpose: The purpose of this study was to examine the effects of prior information about ICU environment on the anxiety and environmental stress of cardiac surgery ICU patients. Methods: A non-equivalent control group non-synchronized quasi-experimental research design was used. Participants were 60 (control 30, experimental 30) patients who had been admitted to ICU. Prior information about the ICU environment was provided to the experimental group. The anxiety level of subjects was measured by State-Trait Anxiety Inventory (STAI) and the stress level of subjects was measured by the Intensive Care Unit Environmental Stressor Scale (ICUESS). Data were analyzed using a Chi-square test or a Fisher's exact test, independent samples t-test, and paired samples t-test. Results: There was no difference in Anxiety (t=-0.58, p=.563), but there was a significant difference in environmental stress (t=10.46, p<.001). Conclusion: Providing prior information would be an effective nursing intervention to reduce environmental stress.

• Journal of Environmental Impact Assessment
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• v.6 no.1
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• pp.111-119
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• 1997

This study was carried out to suggest the best statistical test which may be used to quantify the change of water quality between two groups. Traditional t-test may not be used in cases where the normality of underlying population distribution is not assured. Three non-parametric tests which are based on the relative order of the measurements, were studied to find out the applicability in water quality data analysis. The sign test is based on the sign of the deviation of the measurement from the median value, and the binomial distribution table is used. The signed rank test utilizes not only the sign but also the magnitude of the deviation. The Wilcoxon rank-sum test which is basically same as Mann-Whitney test, tests the mean difference between two independent samples which may have missing data. Among the three non-parametric tests studied, the singed rank test was found out to be applicable in the quantification of the change of water quality between two samples.