• The Korean Journal of Applied Statistics
• /
• v.30 no.2
• /
• pp.301-309
• /
• 2017

It is often possible to test for differences in population means when two or more samples are extracted from each N population. However, it is not possible to test for the mean difference if one sample is extracted from each population since a sample mean does not exist. But, by dividing a group of samples extracted one by one into two groups and generating a sample mean, we can identify a heterogeneity that may exist within the group by comparing the differences of the groups' mean. Therefore, we propose a minimum combination t-test method that can test the mean difference by the number of combinations that can be divided into two groups. In this paper, we proposed a method to test differences between means to check heterogeneity in a group of extracted samples. We verified the performance of the method by simulation study and obtained the results through real data analysis.

• Journal of the Korean Statistical Society
• /
• v.27 no.4
• /
• pp.397-405
• /
• 1998

We develop parametric bootstrap model selection criteria in an example to fit a random sample to either a general normal distribution or a normal distribution with prespecified mean. We apply the bootstrap methods in two ways; one considers the direct substitution of estimated parameter for the unknown parameter, and the other focuses on the bias correction. These bootstrap model selection criteria are compared with AIC. We illustrate that all the selection rules reduce to the one sample t-test, where the cutoff points converge to some certain points as the sample size increases.

• Communications for Statistical Applications and Methods
• /
• v.6 no.3
• /
• pp.659-666
• /
• 1999

In comparing two population means, the test statistic depends on the sample means and the variances, which are very sensitive to the extremely large or small values. This paper aims at examining the behavior of such observations using proper criterion which can measure the influence of them. We derive a computationally feasible statistic which can detect influential observations on the two-sample t-statistic.

• Journal of the Korean Statistical Society
• /
• v.33 no.1
• /
• pp.79-97
• /
• 2004

We propose a permutation approach to the classic Behrens-Fisher problem of comparing two means in the presence of unequal variances. It is motivated by the observation that a paired test is valid whether or not the variances are equal. Rather than using a single arbitrary pairing of the data, we average over all possible pairings. We do this in both a parametric and nonparametric setting. When the sample sizes are equal, the parametric version is equivalent to referral of the unpaired t-statistic to a t-table with half the usual degrees of freedom. The derivation provides an interesting representation of the unpaired t-statistic in terms of all possible pairwise t-statistics. The nonparametric version uses the same idea of considering all different pairings of data from the two groups, but applies it to a permutation test setting. Each pairing gives rise to a permutation distribution obtained by relabeling treatment and control within pairs. The totality of different mean differences across all possible pairings and relabelings forms the null distribution upon which the p-value is based. The conservatism of this procedure diminishes as the disparity in variances increases, disappearing completely when the ratio of the smaller to larger variance approaches 0. The nonparametric procedure behaves increasingly like a paired t-test as the sample sizes increase.

• Communications for Statistical Applications and Methods
• /
• v.5 no.1
• /
• pp.133-144
• /
• 1998

In this paper, we propose the two-sample test statistic using Wilcoxon signed rank test on ranked-set sampling(RSS) and obtain the asymptotic relative efficiencies(ARE) of the proposed test statistic with respect to Mann-Whitney-Wilcoxon statistic on simple random sampling(SRS), the Mann-Whitney-Wilcoxon statistic on RSS, sign statistic on RSS and Wilcoxon signed rank test on SRS. From the simulation works, we compare the powers of the proposed test statistic, Mann-Whitney-Wilcoxon statistic on RSS, the usual two-sample t statistic, sign statistic on RSS, where the underlying distributions are uniform, normal, double exponential, logistic and Cauchy distributions.

• The Korean Journal of Applied Statistics
• /
• v.19 no.2
• /
• pp.231-240
• /
• 2006

It is well understood that a paired comparison (paired t test) provides better precision than a group comparison (two-sample t test), when the pairing is effective (the variation within a pair is small). However, when the variation among the pairs is sufficiently small, the group comparison is likely to yield a better result. To get a statistical explanation of this, we examine the two methods through an analogy to one-way and two-way analysis of variance. We introduce a new measure, R statistic, which is the ratio of their confidence interval lengths, as a quantitative criterion for comparing the two methods. The distribution of the Rf statistic is described by t and F distribution functions. Through this characterization, we show that the paired comparison can be better than group comparison when the variation among the pairs is statistically significantly large.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.15 no.1
• /
• pp.120-127
• /
• 2009

Purpose: In the literature power analysis in experimental studies is often executed and reported falsely. This descriptive study was done to promote the correct application of the Cohen(1988)'s power analysis method. Method: Articles of experimental studies from a nursing journal were selected and reviewed to examine the uses of and the reports on the power analysis process. Also, the appropriate power analysis process was discussed with an example of the most common experimental design, an independent two-group design with t test analysis plan. Result: Around half the experimental studies examined reported that they carried out power analysis. Cohen's method was the most frequently utilized but with accuracy in question. Conclusion: Power analysis to estimate sample size is the interplay between alpha, power, and effect size, and other factors in the case of t test analysis. Researchers should have a clear understanding of how to apply the Cohen's power analysis method so they do not produce poorly estimated sample sizes.

• Communications for Statistical Applications and Methods
• /
• v.27 no.4
• /
• pp.397-412
• /
• 2020

In this paper, a notion of data depth is used to propose nonparametric multivariate two sample tests for difference between scale parameters. Data depth can be used to measure the centrality or outlying-ness of the multivariate data point relative to data cloud. A difference in the scale parameters indicates the difference in the depth values of a multivariate data point. By observing this fact on a depth vs depth plot (DD-plot), we propose nonparametric multivariate two sample tests for scale parameters of multivariate distributions. The p-values of these proposed tests are obtained by using Fisher's permutation approach. The power performance of these proposed tests has been reported for few symmetric and skewed multivariate distributions with the existing tests. Illustration with real-life data is also provided.

• The Korean Journal of Applied Statistics
• /
• v.23 no.6
• /
• pp.1067-1080
• /
• 2010

A number of methods have been developed to determine sample sizes in clinical trial, and most clinical trial organizations determine sample sizes based on the methods. In contrast, determining sufficient sample sizes needed for experiments using microarray chips is unsatisfactory and not widely in use. In this paper, our objective is to provide a guideline in determining sample sizes, utilizing reproducibility of real microarray data. In the reproducibility comparison, five methods for discovering differential expression are used: Fold change, Two-sample t-test, Wilcoxon rank-sum test, SAM, and LPE. In order to standardize gene expression values, both MAS5 and RMA methods are considered. According to the number of repetitions, the upper 20 and 100 gene accordances are also compared. In determining sample sizes, more realistic information can be added to the existing method because of our proposed approach.

• Journal of radiological science and technology
• /
• v.35 no.1
• /
• pp.35-44
• /
• 2012

The goal of this study is to awaken about risk occurred by CT examination. For radio-technologists working at 'S medical center' located in Seoul, we investigated a recognition about dose and risk CT and normal X-ray examination according by working experience in hospital, experience about CT examination and radiation source. For subjects of investigation, radio-technologists working at 'S medical center' located in Seoul helped us. We collected 131 questionnaires for a test of hypothesis. Cronbach @ coefficients of questionnaires were 0.825988 and 0.767161 and a rejection rate of p-value was below 0.05. SAS 9.1(SAS Institute Inc., Cary, NC, USA.) statistic package was used for hypothesis test. We used Mann-Whitney test, Kruskai-Wallis test, Two sample T-test, Two sample T-test with Bonferroni's Correction and One-way ANOVA methods. P-values of hypothesis about dose of CT and normal X-ray examination were 0.2291 ~ 0.9663. p-values of hypothesis about risk were 0.1924 ~ 1.0000. All of hypothesis is over rejection rate(<0.05). This study shows that radio-technologists of S medical center recognized that CT has higher dose and risk than general X-ray examination.