• 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.

• 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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.199-202
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• 2006

We propose some properties of fuzzy p-value and fuzzy significance level to the test statistics for the fuzzy hypotheses testing. Appling the principle of agreement index, we suggest two method for fuzzy hypothesis testing by fuzzy rejection region and fuzzy p-value with fuzzy hypothesis $H_{f,0}$.

• 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.

• 대한간호학회지
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• 제45권5호
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• pp.641-649
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• 2015

Purpose: The purpose of this study was to introduce the main concepts of statistical testing and effect size and to provide researchers in nursing science with guidance on how to calculate the effect size for the statistical analysis methods mainly used in nursing. Methods: For t-test, analysis of variance, correlation analysis, regression analysis which are used frequently in nursing research, the generally accepted definitions of the effect size were explained. Results: Some formulae for calculating the effect size are described with several examples in nursing research. Furthermore, the authors present the required minimum sample size for each example utilizing G*Power 3 software that is the most widely used program for calculating sample size. Conclusion: It is noted that statistical significance testing and effect size measurement serve different purposes, and the reliance on only one side may be misleading. Some practical guidelines are recommended for combining statistical significance testing and effect size measure in order to make more balanced decisions in quantitative analyses.

• 응용통계연구
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• 제21권5호
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• pp.867-873
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• 2008

임상시험의 자료에 대하여 통계적 검정을 실시하는 경우 유의수준 5%를 사용하는 것이 관례이다. 하지만 동등성 시험을 신뢰구간을 사용하여 검정하는 경우, 신뢰도 90%인 신뢰구간이 사용되고 있다. 흔히 신뢰도 $1-{\alpha}$인 신뢰구간을 검정에 사용하는 경우, 그 검정법의 유의수준은 ${\alpha}$이다. 이 때문에 동등성 검정에서 신뢰도 90%인 신뢰구간을 사용하게 되면, 유의수준은 10%가 되는 것이 아닌가 하는 혼란을 일으켰다. 본 논문에서는 이와 관련된 이슈들을 관련 문헌의 검토와 시뮬레이션을 통하여 명확하게 하여, 제약회사, CRO, 대학병원 등에 종사하는 통계전문가들에게 도움을 주고자 한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
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• pp.153-156
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• 2006

We propose some properties of fuzzy p-value and fuzzy significance level to the test statistics for the fuzzy hypotheses testing. Appling the principle of agreement index, we suggest two method for fuzzy hypothesis testing by fuzzy rejection region and fuzzy p-value with fuzzy hypothesis to separately ${\alpha}-level$.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2008년도 춘계학술대회
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• pp.451-460
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• 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.

• 대한안전경영과학회지
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• 제15권1호
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• pp.283-292
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• 2013

This paper aims to propose a new steps of hypothesis testing using analysis process and improvement process in the six sigma DMAIC. The six sigma implementation models proposed in this paper consist of six steps. The first step is to establish a research hypothesis by specification directionality and FBP(Falsibility By Popper). The second step is to translate the research hypothesis such as RHAT(Research Hypothesis Absent Type) and RHPT(Research Hypothesis Present Type) into statistical hypothesis such as $H_0$(Null Hypothesis) and $H_1$(Alternative Hypothesis). The third step is to implement statistical hypothesis testing by PBC(Proof By Contradiction) and proper sample size. The fourth step is to interpret the result of statistical hypothesis test. The fifth step is to establish the best conditions of product and process conditions by experimental optimization and interval estimation. The sixth step is to draw a conclusion by considering practical significance and statistical significance. Important for both quality practitioners and academicians, case analysis on six sigma projects with implementation guidelines are provided.