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Parametric statistics is a branch of statistics which assumes that sample data comes from a population that can be adequately modeled by a probability distribution that has a fixed set of parameters. Most well-known statistical methods are parametric. The normal family of distributions all have the same general shape and are parameterized by mean and standard deviation.
This book demonstrates that nonparametric statistics can be taught from a parametric point of view. As a result, one can exploit various parametric tools such as the use of the likelihood function, penalized likelihood and score functions to not only derive well-known tests but to also go beyond and make use of Bayesian methods to analyze ranking data. The book bridges the gap between parametric and nonparametric statistics and presents the best practices of the former while enjoying the robustness properties of the latter. This book can be used in a graduate course in nonparametrics, with parts being accessible to senior undergraduates. In addition, the book will be of wide interest to statisticians and researchers in applied fields. Skip to main content Skip to table of contents. Advertisement Hide.
Quantitative Methods 2 Reading Hypothesis Testing Subject Parametric and Non-Parametric Tests. Why should I choose AnalystNotes? AnalystNotes specializes in helping candidates pass.
“nonparametric” Statistics for quantitative variables w/out making “assumptions about the form of the underlying data distribution”. • univariate stats -- median &.
Ben Derrick Ben. Derrick uwe. Paul White Paul.
Non-parametric methods are used to analyze data when the distributional assumptions of more common procedures are not satisfied. For example, many statistical procedures assume that the underlying error distribution is Gaussian, hence the widespread use of means and standard deviations. When the error distribution is not known, non-parametric statistical tests may be safer to apply. The non-parametric methods in Statgraphics are options within the same procedures that apply the classical tests. These non-parametric statistical methods are classified below according to their application.
Quantitative Methods 2 Reading Hypothesis Testing Subject Parametric and Non-Parametric Tests.
In terms of selecting a statistical test, the most important question is "what is the main study hypothesis?
Is my data normally distributed? Can I use a parametric test? 1. Know your subject matter – can you justify the assumption of normality? 2. Plot your.