• 아동학회지
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• 제22권4호
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• pp.331-341
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• 2001

This study is to investigate whether young Korean children have understanding for testing hypothesis. Questions explored are; First, do children have notions of testing hypothesis? Or, do they just produce an effect? Second, choosing between conflicting hypotheses, can children distinguish between experiments that would produce conclusive and inconclusive evidence? For this study, 15 first grade and 15 second grade children in elementary school located in Kyunggi area near Seoul participated. Data collection and analysis were based on interviews with children for two weeks. Children were presented two conflicted hypotheses to decide which one is correct through conclusive evidence and inconclusive evidence in the interview. The results showed that children(1st: 93.3%, 2nd: 81.3%) of each grade can distinguish between hypothesis and evidence to do testing hypothesis, and distinguish between conclusive and inconclusive evidence. In conclusion, most young children have understanding of testing hypothesis based on their familiar experiences, so it was possible for them to differentiate hypothesis from evidence in certain situations.

• Journal of the Korean Statistical Society
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• 제23권1호
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• pp.199-206
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• 1994

Sometimes a researcher with a view of conventional significance testing rejects his/her hypothesis, even through it could have not been rejected with a smaller sample. This can be a logical dilemma for a researcher who wants to "prove" a hypothesis rather than to show discrepancy from a null hypothesis. In this study, a new testing paradigm called equivalence testing via confidence interval will be developed so that it is suitable for the purpose of statistical proof.cal proof.

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.787-795
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• 2001

In this paper, Bayesian hypothesis testing procedures are proposed under the non-informative prior distributions, which can be thought as the Bayesian counterparts of the classical ones in the sense of using the concept of significance level. The performances of proposed procedures are compared with those of classical procedures through several examples.

• Journal of the Korean Data and Information Science Society
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• 제19권3호
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• pp.995-1006
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• 2008

This article deals with the one-sided hypothesis testing problem in inverse Gaussian distribution. We propose Bayesian hypothesis testing procedures for the one-sided hypotheses of the shape parameter under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian hypothesis testing procedures based on the fractional Bayes factor, the median intrinsic Bayes factor and the encompassing intrinsic Bayes factor under the reference prior. Simulation study and a real data example are provided.

• 품질경영학회지
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• 제36권4호
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• pp.1-6
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• 2008

As more and more data are collected one may ask whether the data collected within a short period of time are same. In this case traditional hypothesis testing of $H_o:{\mu}_1={\mu}_2$ vs $H_1:{\mu}_1{\neq}{\mu}_2$ is used to determine whether the data are same when there is no knowledge about equivalence testing. However, this type of hypothesis testing has the undesirable property of penalizing higher precision. So TOST is to be performed in the event of equivalence testing. In this study equivalence testing is introduced where one can find the applications in industry. Traditional two sample t testing is to be compared with the equivalent testing and the procedure to perform the equivalence testing is to be presented along with an example. Finally equivalence testing in terms of the other parameters such as variance, proportion or failure rate is to be sought.

• Journal of the Korean Data and Information Science Society
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• 제21권1호
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• pp.163-172
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• 2010

In this paper, we deal with the problem for testing homogeneity of coecients of variation in several normal distributions. We propose Bayesian hypothesis testing procedures based on the Bayes factor under noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be dened up to a multiplicative constant. So we propose the objective Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real data example are provided.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1379-1390
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• 2008

This article deals with the problem of testing the difference of quantiles in exponential distributions. We propose Bayesian hypothesis testing procedures for the difference of two quantiles under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factor under the matching prior. Simulation study and a real data example are provided.

• 대한소아치과학회지
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• 제40권3호
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• pp.223-232
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• 2013

치의학 연구에서 사용되는 귀무가설 유의성 검정에서 p값을 기준으로 연구의 결과를 평가하는 것은 많은 문제점을 내포하고 있다. 귀무가설이 기각되지 않은 경우에 귀무가설이 옳다는 결론을 내리는 것은 논리적 오류이다. p값에 대한 중대한 오해가 많이 있으며 연구자는 논문을 작성할 때 p값의 해석에 신중해야 한다. 귀무가설검정을 보완하거나 대체할 수 있는 대안으로서, 효과 크기, 신뢰구간, 베이지안 통계 등이 있다.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2012년 추계학술대회
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• pp.461-471
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• 2012

Most useful statistical techniques in six sigma DMAIC are hypothesis testing and interval estimation. So this paper reviews and derives sample size formula by considering significance level, power of detectability and effect difference. The quality practioners can effectively interpret the practical and statistical significance with the rational sample sizing.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1119-1128
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• 2005

The Bayes factors with improper noninformative priors are defined only up to arbitrary constants. So it is known that Bayes factors are not well defined due to this arbitrariness in Bayesian hypothesis testing and model selections. The intrinsic Bayes factor and the fractional Bayes factor have been used to overcome this problem. In this paper, we suggest a Bayesian hypothesis testing based on the intrinsic Bayes factor and the fractional Bayes factor for the comparison of two lognormal variances. Using the proposed two Bayes factors, we demonstrate our results with some examples.