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AuthorWilcox, Rand R. author
TitleFundamentals of Modern Statistical Methods [electronic resource] : Substantially Improving Power and Accuracy / by Rand R. Wilcox
ImprintNew York, NY : Springer New York : Imprint: Springer, 2001
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Descript XIII, 258 p. online resource


Conventional statistical methods have a very serious flaw: They routinely miss differences among groups or associations among variables that are detected by more modern techniques - even under very small departures from normality. Hundreds of journal articles have described the reasons standard techniques can be unsatisfactory, but simple, intuitive explanations are generally unavailable. Improved methods have been derived, but they are far from obvious or intuitive based on the training most researchers receive. Situations arise where even highly nonsignificant results become significant when analyzed with more modern methods. Without assuming any prior training in statistics, Part I of this book describes basic statistical principles from a point of view that makes their shortcomings intuitive and easy to understand. The emphasis is on verbal and graphical descriptions of concepts. Part II describes modern methods that address the problems covered in Part I. Using data from actual studies, many examples are included to illustrate the practical problems with conventional procedures and how more modern methods can make a substantial difference in the conclusions reached in many areas of statistical research


I -- 1 Introduction -- 2 Getting Started -- 3 The Normal Curve and Outlier Detection -- 4 Accuracy and Inference -- 5 Hypothesis Testing and Small Sample Sizes -- 6 The Bootstrap -- 7 A Fundamental Problem -- II -- 8 Robust Measures of Location -- 9 Inferences About Robust Measures of Location -- 10 Measures of Association -- 11 Robust Regression -- 12 Alternate Strategies -- References

Mathematics Probabilities Statistics Mathematics Probability Theory and Stochastic Processes Statistical Theory and Methods Statistics for Social Science Behavorial Science Education Public Policy and Law Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Life Sciences Medicine Health Sciences


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