Nonparametric Hypothesis Testing Report
Nguyen, Loc PhD, MD, Mba
No 3vxmy, OSF Preprints from Center for Open Science
This report is the brief survey of nonparametric hypothesis testing. It includes four main sections about hypothesis testing, one additional section discussing goodness-of-fit and conclusion section. Sign test section gives an overview of nonparametric testing, which begins with the test on sample median without assumption of normal distribution. Signed-rank test section and rank-sum test section concern improvements of sign test. The prominence of signed-rank test is to be able to test sample mean based on the assumption about symmetric distribution. Rank-sum test discards the task of assigning and counting plus signs and so it is the most effective method among ranking test methods. Nonparametric ANOVA section discusses application of analysis of variance (ANOVA) in nonparametric model. ANOVA is useful to compare and evaluate various data samples at the same time. Nonparametric goodness-fit-test section, an additional section, focuses on different hypothesis, which measure the distribution similarity between two samples. It determines whether two samples have the same distribution without concerning how the form of distribution is. The last section is the conclusion. Note that in this report terms sample and data sample have the same meaning. A sample contains many data points. Each data point is also called an observation.
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