• 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.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.6
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• pp.982-989
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• 2010

This paper shows accuracy comparison results of reliability estimation methods for one-shot systems such as ammunitions. Quantal-response data, following a binomial distribution at each sampling time, characterizes lifetimes of one-shot systems. Various quantal-response data of different sample sizes are simulated using lifetime data randomly sampled from assumed weibull distributions with different shape parameters but the identical scale parameter in this paper. Then, reliability estimation methods in open literature are applied to the simulated quantal-response data to estimate true reliability over time. Rankings in estimation accuracy for different sample sizes are determined using t-test of SSE. Furthermore, MSE at each time, including both bias and variance of estimated reliability metrics for each method are analyzed to investigate how much both bias and variance contribute the SSE. From the MSE analysis, MSE provides reliability estimation trend for each method. Parametric estimation method provides more accurate reliability estimation results than the other methods for most of sample sizes.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.477-485
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• 2013

In this paper, we consider the sample sizes required for each group in independent two sample test of normal populations when both the type I error and type II error probabilities are specified with sample sizes and variances being possibly different. We derived the sample sizes and the power of the tests, and implement them by web programing. The result is available over the world wide web. Further, we also provide the power calculations and have them available on the web.

• Journal of Sericultural and Entomological Science
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• v.15 no.1
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• pp.27-30
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• 1973

The present research has been carried out in an attempt to obtain fundamental data which contribute to enhansing the accuracy of cocoon reeling test and improving the method of it more effectively. The results obtained are as follows: 1. The standard deviation and coefficient of variation of percentage of reelability and of percentage of raw silk yield to the sample size tended to show a gradual decrease according to the increasing of the sample size. However, the significant differences in the standard deviation and coefficient of variation of percentage of reelability and of percentage of raw silk yield between the different sizes of the samples having more than 300 cocoons have not been observed according to their sizes. 2. The significant difference in the standard errors from percentage of reelability between the samples of 300 cocoons and 400 cocoons have not been observed. 3. The significant difference in the standad errors from percentage of raw silk yield between the samples of 300 cocoons, 500 cocoons and 600 cocoons have not been observed. 4. The significant difference in the standard deviation, the standard errors and coefficient of variation to the length of a bave between the different sizes of the samples have not been shown. 5. From the above result, it is estimated that the optimum sire of the sample for the cocoon reeling test is the 300 cocoons.

• Journal of Veterinary Clinics
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• v.32 no.1
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• pp.73-77
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• 2015

There has been increasing attention on sample size requirements in peer reviewed medical literatures. Accordingly, a statistically-valid sample size determination has been described for a variety of medical situations including diagnostic test accuracy studies. If the sample is too small, the estimate is too inaccurate to be useful. On the other hand, a very large sample size would yield the estimate with more accurate than required but may be costly and inefficient. Choosing the optimal sample size depends on statistical considerations, such as the desired precision, statistical power, confidence level and prevalence of disease, and non-statistical considerations, such as resources, cost and sample availability. In a previous paper (J Vet Clin 2012; 29: 68-77) we briefly described the statistical theory behind sample size calculations and provided practical methods of calculating sample size in different situations for different research purposes. This review describes how to calculate sample sizes when assessing diagnostic test performance such as sensitivity and specificity alone. Also included in this paper are tables and formulae to help researchers for designing diagnostic test studies and calculating sample size in studies evaluating test performance. For complex studies clinicians are encouraged to consult a statistician to help in the design and analysis for an accurate determination of the sample size.

• Geomechanics and Engineering
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• v.14 no.2
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• pp.151-159
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• 2018

Annually, the global production of construction aggregates reaches over 40 billion tons, making aggregates the largest mining sector by volume and value. Currently, the aggregate industry is shifting from sand to hard rock as a result of legislation limiting the extraction of natural sands and gravels. A major implication of this change in the aggregate industry is the need for understanding rock fragmentation and energy absorption to produce more cost-effective aggregates. In this paper, we focused on incorporating dynamic rock and soil mechanics to understand the effects of loading rate and water saturation on the rock fragmentation and energy absorption of three different sandstones (Red, Berea and Buff) with different pore sizes. Rock core samples were prepared in accordance to the ASTM standards for compressive strength testing. Saturated and dry samples were subsequently prepared and fragmented via fast and dynamic compressive strength tests. The particle size distributions of the resulting fragments were subsequently analyzed using mechanical gradation tests. Our results indicate that the rock fragment size generally decreased with increasing loading rate and water content. In addition, the fragment sizes in the larger pore size sample (Buff sandstone) were relatively smaller those in the smaller pore size sample (Red sandstone). Notably, energy absorption decreased with increased loading rate, water content and rock pore size. These results support the conclusion that rock fragment size is positively correlated with the energy absorption of rocks. In addition, the rock fragment size increases as the energy absorption increases. Thus, our data provide insightful information for improving cost-effective aggregate production methods.

• International Journal of Reliability and Applications
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• v.2 no.3
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• pp.209-240
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• 2001

Computer-based simulation and analysis is used extensively in engineering for a variety of tasks. Despite the steady and continuing growth of computing power and speed, the computational cost of complex high-fidelity engineering analyses and simulations limit their use in important areas like design optimization and reliability analysis. Statistical approximation techniques such as design of experiments and response surface methodology are becoming widely used in engineering to minimize the computational expense of running such computer analyses and circumvent many of these limitations. In this paper, we compare and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity. The first example involves the analysis of a two-member frame that has three input variables and three responses of interest. The second example simulates the roll-over potential of a semi-tractor-trailer for different combinations of input variables and braking and steering levels. Detailed error analysis reveals that uniform designs provide good sampling for generating accurate approximations using different sample sizes while kriging models provide accurate approximations that are robust for use with a variety of experimental designs and sample sizes.

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

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1301-1313
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• 2009

Efficiency and sensitivity of BLS adjustment method have been studied and the method is known to provide more accurate estimate of total by using properly adjusted weights of samples. However, BLS methods provide different efficiencies according to the magnitudes of correlation coefficients and the sizes of samples in strata. In this paper we study the efficiency of the BLS adjustment according to the sample sizes and correlations in strata. For this study, 2007 monthly labor survey data is used.

• Fashion & Textile Research Journal
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• v.13 no.4
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• pp.590-599
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• 2011

The purpose of this study is to classify body types into different groups based on 3D scanned data, and identify the representative body shapes that most frequently appear in the groups. A high frequency range was defined based on body measurement data, and representative shapes were identified based on body scan data of women aged 18-24. The major findings of this study are as follows. First, the three typical types of height, bust and drop mixes among Korean women aged 18-24 are as follows:) 155-88-N,) 160-82-A, and 165-85-A. Regarding the body type, the A and N types frequently appeared, while the H type was rare. Second, among the subjects of the high-frequency groups, upright bodies were selected as sample models and were used for morphing. An adjustment was made to the morphing results to reflect the body characteristics of the sample models evenly. Third, to verify the morphing results, the body sizes of the sample models and the created models were compared. The result showed that, the sizes were close to the average size of the sample models. Fourth, to verify whether the morphing processes adequately reflected the features of the body shapes, cross-section models of key body parts were made and the degree of representativeness was determined.