• 통합자연과학논문집
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• 제12권3호
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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.

• 통합자연과학논문집
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• 제15권1호
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• pp.9-18
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• 2022

This research examines the power of bootstrap two-sample test, and compares it with the powers of two-sample t-test and Wilcoxon rank sum test, through simulation. For simulation work, a positively skewed and heavy tailed distribution was selected as a population distribution, the chi-square distributions with three degrees of freedom, χ²₃. For two independent samples, the fist sample was selected from χ²₃. The second sample was selected independently from the same χ²₃ as the first sample, and calculated d+ax for each sampled value x, a randomly selected value from χ²₃. The d in d+ax has from 0 to 5 by 0.5 interval, and the a has from 1.0 to 1.5 by 0.1 interval. The powers of three methods were evaluated for the sample sizes 10,20,30,40,50. The null hypothesis was the two population medians being equal for Bootstrap two-sample test and Wilcoxon rank sum test, and the two population means being equal for the two-sample t-test. The powers were obtained using r program language; wilcox.test() in r base package for Wilcoxon rank sum test, t.test() in r base package for the two-sample t-test, boot.two.bca() in r wBoot pacakge for the bootstrap two-sample test. Simulation results show that the power of Wilcoxon rank sum test is the best for all 330 (n,a,d) combinations and the power of two-sample t-test comes next, and the power of bootstrap two-sample comes last. As the results, it can be recommended to use the classic inference methods if there are widely accepted and used methods, in terms of time, costs, sometimes power.

• Journal of the Korean Statistical Society
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• 제22권2호
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• pp.159-169
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• 1993

In this article, we consider two sample problem with randomly right censored data. We propse two-sample confidence intervals for the difference in medians or any quantiles, based on bootstrap. The bootstrap version of two-sample confidence intervals proposed in this article is simple to apply and do not need the assumption of the shift model, so that for the non-shift model, the density estimation is not necessary, which is an attractive feature in small to moderate sized sample case.

• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.459-469
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• 1998

Balanced half sample method is a simple variance estimation method for complex sampling designs. Since it is simple and flexible, it has been widely used in large scale sample surveys. However, the usual BHS method overestimate the true variance in without replacement sampling and two-stage cluster sampling. Focusing on this point , we proposed an unbiased BHS variance estimator in a stratified two-stage cluster sampling and then described an implementation method of the proposed estimator. Finally, partially BHS design is explained as a tool of reducing the number of replications of the proposed estimator.

• 한국통신학회논문지
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• 제35권8C호
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• pp.709-712
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• 2010

이 논문에서는 시간상 확률 상관이 0이 아닌 약의존성 잡음 모형에서 두 표본을 쓰는 국소 최적 순위 검파기의 검정 통계량를 얻었다. 기준 관측과 보통 관측으로 이루어진 두 표본 관측 모형에서 약의존성 잡음 환경에 알맞은 국소 최적 순위 검파기의 검정 통계량을 네이먼 피어슨 정리로부터 유도하였다. 두 표본을 쓰는 국소 최적 순위 검파기는 한 표본을 쓰는 국소 최적 순위 검파기와 같은 점근 성능을 가지며 부호 통계량을 다룰 필요가 없어 얼개가 비교적 간단하다.

• Bulletin of the Korean Chemical Society
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• 제22권8호
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• pp.807-815
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• 2001

Two-dimensional (2D) correlation method, which generates the synchronous and the asynchronous 2D spectrum by complex cross correlation of the Fourier transformed spectra, is an analysis method for the changes of the sample spectrum induced by vari ous perturbations. In the present work, the 2D electronic correlation spectra have been simulated for the cases where the sample spectrum composed of two gaussian bands changes linearly. When only the band amplitudes of the sample spectrum change, the synchronous spectrum shows strong peaks at the band centers of the sample spectrum, but the asynchronous spectrum does not make peaks. When the sample spectrum shifts without changing intensity and width, the synchronous spectrum shows peaks around the initial and final positions of the band maximum and the asynchronous spectrum shows long peaks spanning the shifting range. The band width change produces the complex 2D correlation spectra. When the sample spectrum shifts with band broadening, the width change by 50% of full width at half maximum (FWHM) does not give so large an effect on the correlation spectrum as the spectral shift by one half of FWHM of the sample spectrum.

• 통합자연과학논문집
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• 제14권3호
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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 applied mathematics & informatics
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• 제9권2호
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• pp.825-835
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• 2002

In this paper, we extend the conditional unrelated question model which was suggested by Lee and Hong(2000) to two-sample case when there is no information about the true proportion of the unrelated character Y. Conditions are obtained under which the proposed model is more efficient than Carr et al.\`s conditional modal and Greenberg et al.'s two-sample unrelated question model.

• 한국수학교육학회지시리즈A:수학교육
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• 제45권2호
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• pp.145-154
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• 2006

The main purpose of this study is to investigate the effect of departures from normality and equal variance on the two-sample test when the variances are unknown. We have found that type I error brought about a little bit change which is ignorable in relation to kurtosis. But the change of type I error was mainly based on the skewness of the parent population. In introductory statistics classes where data analysis includes techniques for detecting skewness of two populations, we recommend the two-sample t-test when maximal skewness of two populations is smalter than the value 4 when the variances seem equal. Furthermore, our simulations reveal that the two-sample t-test appears somewhat more robust than that of z-test if the assumption of equal variance is satisfied. In the case of unequal variance, the two-sample t-test appears somewhat more robust provided the t-statistic using Satterthwaite's approximate degrees of freedom.

• 한국통신학회논문지
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• 제40권8호
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• pp.1515-1517
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• 2015

이 논문에서는 두 표본을 쓰는 순위 검파기의 성능과 기준 표본 크기의 상관 관계를 모의실험을 통해 밝힌다. 기준 표본의 크기가 클수록 검파기의 성능은 더 우수하다. 다만 기준 표본 크기가 커질수록 성능향상은 포화된다.