• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.19 no.2
• /
• pp.81-87
• /
• 2009

Final purpose of this study was designed to develop the quality control(QC) sample for proficiency analytical testing of asbestos. This study consisted of two parts; first, development of manufacturing apparatus and sample preparing procedure for asbestos quality control(QC) sample: second, validation of the QC samples made by our developed method as asbestos proficiency analytical testing sample. The main results of the first part research are as followed We developed the apparatus for manufacturing the asbestos QC sample, consisted of filter hold, filter holder manifolder, vacuum system, and vacuum pump. The most proper filter of making the QC samples was a cellulose ester membrane filter with 25 mm diameter, pore size 0.8 um. And we presented the optimal procedure for preparing the asbestos QC sample by using the developed apparatus. We will verify the manufactured asbestos QC samples by this method, and present the validation results to confirm the reliability as a asbestos QC sample in next paper.

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

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.6
• /
• pp.1343-1351
• /
• 2010

Sample size determination is very important part in clinical trials because it influences the time and the cost of the experimental studies. In this article, we consider the Bayesian methods for sample size determination based on hypothesis testing. Specifically we compare the usual Bayesian method using Bayes factor with the decision theoretic method using Bayesian reference criterion in mean difference problem for the normal case with known variances. We illustrate two procedures numerically as well as graphically.

• Journal of the Korean Statistical Society
• /
• v.8 no.2
• /
• pp.99-105
• /
• 1979

In Thoman and Bain and Schafer and Sheffield, procedures for testing the equality of the scale parameters of two Weibull populations with a common shape parameter and procedures for selecting the Weibull population with the largest scale parameter are given. We give, in this paper, a modified procedure for the above testing and selection problems, which is more powerful than those previoulsy given.

• The Korean Journal of Applied Statistics
• /
• v.10 no.2
• /
• pp.339-352
• /
• 1997

Chi-square test based on large sample theory is inappropriate for testing the row homogeneity in two-way contingency table with several sparse cells. For that case, exact testing methods has been developed in the literature and implemented in StatXact(1991). However, considerable computing time is inevitable for moderate size tables. So, Monte Carlo approximation is recommended frequently. In this study, we propose a simple algorithm for generating two-way random tables with fixed row and column margins for small sample chi-square test. Also, we develop “Turkey-type” method for multiple between-row comparisons.

• Journal of the Korean Statistical Society
• /
• v.26 no.2
• /
• pp.223-229
• /
• 1997

It is demonstrated that many standard nonparametric test such as the Mann-Whitney-Wilcoxon test, the Fisher-Yates test, the Savage test and the median test are admissible for a two-sample nonparametric testing problem. The admissibility of the Kruskal-Wallis test is demonstrated for a nonparametric one-way layout testing problem.

• Smart Structures and Systems
• /
• v.23 no.1
• /
• pp.81-90
• /
• 2019

The shear behavior of soil-concrete interface is mainly affected by the surface roughness of the two contact surfaces. The present research emphasizes on investigating the effect of roughness of soil-concrete interface on the interface shear behavior in two-layered laboratory testing samples. In these specially prepared samples, clay silt layer with density of $2027kg/m^3$ was selected to be in contact a concrete layer for simplifying the laboratory testing. The particle size testing and direct shear tests are performed to determine the appropriate particles sizes and their shear strength properties such as cohesion and friction angle. Then, the surface undulations in form of teeth are provided on the surfaces of both concrete and soil layers in different testing carried out on these mixed specimens. The soil-concrete samples are prepared in form of cubes of 10*10*30 cm. in dimension. The undulations (inter-surface roughness) are provided in form of one tooth or two teeth having angles $15^{\circ}$ and $30^{\circ}$, respectively. Several direct shear tests were carried out under four different normal loads of 80, 150, 300 and 500 KPa with a constant displacement rate of 0.02 mm/min. These testing results show that the shear failure mechanism is affected by the tooth number, the roughness angle and the applied normal stress on the sample. The teeth are sheared from the base under low normal load while the oblique cracks may lead to a failure under a higher normal load. As the number of teeth increase the shear strength of the sample also increases. When the tooth roughness angle increases a wider portion of the tooth base will be failed which means the shear strength of the sample is increased.

• Journal of the Korean Statistical Society
• /
• v.25 no.1
• /
• pp.95-110
• /
• 1996

We consider the question of efficiency of the Bayes sequential procedure with respect to the optimal fixed sample size Bayes procedure in a simple vs. simple testing problem for data coming from a general nonregular density b(.theta.)h(x)l(x < .theta.). Efficiency is defined in two different ways in these caiculations. Also, the minimax sequential risk (and minimax sequential stratage) is studied as a function of the cost of sampling.

• Proceedings of the Safety Management and Science Conference
• /
• 2008.04a
• /
• pp.451-460
• /
• 2008

This paper reviews nonparametric statistics by Neyman-Pearson test and Fisher test. Nonparametric statistics deal with the small sample with distribution-free assumption in multi-product and small-volume production. Two tests for various nonparametric statistic methods such as sign test, Wilcoxon test, Mann-Whitney test, Kruskal-Wallis test, Mood test, Friedman test and run test are also presented with the steps for testing hypotheses and test of significance.

• Korean Journal of Veterinary Research
• /
• v.48 no.3
• /
• pp.357-361
• /
• 2008

Although power analysis is of important tool of research, investigators in veterinary medicine are unaware of the concepts of the statistical power. Two types of error occur in classical hypothesis testing and, those errors should be avoided, if possible. Since power is highly dependent on the sample size, whenever declaring non-statistically significant result they should consider the potential for committing a Type II error in their studies, which refers to the probability of falsely stating that two treatments are equivalent despite true difference between them. Also, sample size determination is one of the most important tasks facing the researcher when planning a diagnostic study, and provides valuable information on the characteristics of a test performance. This type of analysis forms the basis for proper interpretation of test results. The aim of this article was to re-evaluate some selected studies on diagnostic test reported in the domestic veterinary publications to determine the power and necessary sample size for inequality testing to ensure the desired power. Power calculations were illustrated using real-life examples of comparison of a new test and a reference test for detecting antibodies of various animal diseases. Factors affecting to the power were also discussed.