375: Type I Error Rates When Multiple Comparison Procedures Follow a Significant F Test of ANOVA

Biometrics ◽  
1975 ◽  
Vol 31 (1) ◽  
pp. 229 ◽  
Author(s):  
Clemens S. Bernhardson
Keyword(s):  
Type I Error ◽  
Error Rates ◽  
Multiple Comparison ◽  
Type I ◽  
F Test ◽  
Type I Error Rates ◽  
Multiple Comparison Procedures

scholarly journals Sisvar: a Guide for its Bootstrap procedures in multiple comparisons

2014 ◽  
Vol 38 (2) ◽  
pp. 109-112 ◽  
Author(s):  
Daniel Furtado Ferreira
Keyword(s):  
Scientific Community ◽  
Type I Error ◽  
Multiple Comparisons ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rates ◽  
Statistical Analysis System ◽  
Scientific Results ◽  
Analysis System ◽  
Multiple Comparison Procedures

Sisvar is a statistical analysis system with a large usage by the scientific community to produce statistical analyses and to produce scientific results and conclusions. The large use of the statistical procedures of Sisvar by the scientific community is due to it being accurate, precise, simple and robust. With many options of analysis, Sisvar has a not so largely used analysis that is the multiple comparison procedures using bootstrap approaches. This paper aims to review this subject and to show some advantages of using Sisvar to perform such analysis to compare treatments means. Tests like Dunnett, Tukey, Student-Newman-Keuls and Scott-Knott are performed alternatively by bootstrap methods and show greater power and better controls of experimentwise type I error rates under non-normal, asymmetric, platykurtic or leptokurtic distributions.

The Quest for α: Developments in Multiple Comparison Procedures in the Quarter Century Since

1996 ◽  
Vol 66 (3) ◽  
pp. 269-306 ◽  
Author(s):  
Gregory R. Hancock ◽  
Alan J. Klockars
Keyword(s):  
Type I Error ◽  
Reference Group ◽  
Error Rates ◽  
Multiple Comparison ◽  
Type I ◽  
Quarter Century ◽  
Homogeneity Of Variance ◽  
Current Article ◽  
Post Hoc ◽  
Multiple Comparison Procedures

In a highly regarded work, Games (1971) presented state-of-the-art multiple comparison procedures (MCPs) for a variety of research scenarios and sought to bring order to the seemingly chaotic array of MCPs being used at that time. The current article is a sequel of sorts, placing Games’s insights in the context of many of the major developments in simultaneous and sequential inference since his article’s publication. Specifically, we address the common MCP scenarios of orthogonal contrasts, nonorthogonal contrasts, comparisons against a reference group, all possible pairwise comparisons, and exploratory post hoc contrasts, all under the assumed conditions of independent scores, normality, and homogeneity of variance. In addition, discussions of the philosophical issues surrounding the control of Type I error rates are presented.

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