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Thesis
SIMULATIONS TO ANALYZE TYPE I ERROR AND POWER IN THE ANOVA F TEST AND NONPARAMETRIC ALTERNATIVES
University of West Florida
Master of Science (MS), University of West Florida
2009
Abstract
The Analysis of Variance (ANOVA) F test is used to test the equality of population means for three or more samples. However, the F test has some assumptions that are frequently ignored and often violated when used in real world applications. These assumptions include that the data is normally distributed and the population variances are equal. Violating these assumptions can lead to an inflated Type I error and a decrease in power. Simulations are conducted to analyze the Type I error and power of the F test when the assumptions are violated. Two nonparametric alternatives to the F test, the Kruskal-Wallis test and the normal scores test, are also analyzed. The results show that the nonparametric tests have a lower Type I error and higher power under certain cases of nonnormality and unequal variances. These simulations serve as a guide to which test should be used based on the type of data being analyzed.
Details
- Title
- SIMULATIONS TO ANALYZE TYPE I ERROR AND POWER IN THE ANOVA F TEST AND NONPARAMETRIC ALTERNATIVES
- Resource Type
- Thesis
- Publisher
- University of West Florida
- Format
- text
- Identifiers
- 99380090758106600
- Academic Unit
- Mathematics and Statistics
- Language
- English
- Awarding Institution
- University of West Florida; Master of Science (MS)
- Theses and Dissertations
- Master of Science (MS), University of West Florida