• Journal of the Korean Statistical Society
• /
• v.23 no.1
• /
• pp.199-206
• /
• 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
• /
• v.8 no.3
• /
• pp.787-795
• /
• 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.

• Proceedings of the Safety Management and Science Conference
• /
• 2012.11a
• /
• pp.461-471
• /
• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2006.05a
• /
• pp.199-202
• /
• 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 $H_{f,0}$.

• Journal of the korean academy of Pediatric Dentistry
• /
• v.40 no.3
• /
• pp.223-232
• /
• 2013

There are many problems in evaluating study results by p value in null hypothesis testing for dental research. It is a logical fallacy to conclude that the null hypothesis is true when the it is not rejected. There are much serious misunderstanding about p value, and researchers should be cautious about interpreting p value in writing papers. As alternatives to complement or replace the null hypothesis significance testing, effect size, confidence interval, and Bayesian statistics are introduced.

• Journal of Korean Academy of Nursing
• /
• v.45 no.5
• /
• pp.641-649
• /
• 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.

• The Korean Journal of Applied Statistics
• /
• v.21 no.5
• /
• pp.867-873
• /
• 2008

It is a convention to use 5% significance level when a statistical test is employed for clinical data. But when a confidence interval is used for testing equivalence, 90% confidence interval has often been used. When $1-{\alpha}$ confidence interval is used for hypothesis testing, its significance level is often ${\alpha}$. So it makes a confusion that the significance level is 10% if 90% confidence interval is employed for testing equivalence. In this paper I will clarify this issue by reviewing relevant papers and conducting simulation studies. I hope that it will be beneficial to statisticians in pharmaceutical companies, CROs, university hospitals.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2006.11a
• /
• pp.153-156
• /
• 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 ${\alpha}-level$.

• 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.

• Journal of the Korea Safety Management & Science
• /
• v.15 no.1
• /
• pp.283-292
• /
• 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.