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Randomization Tests (Statistics
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Table of Contents

Statistical Tests That Do Not Require Random Sampling. Randomized Experiments. Calculating P-Values. Between-Subjects Designs. Factorial Designs. Repeated-Measures and Randomized Block Designs. Multivariate Designs. Correlation. Trend Tests. Matching and Proximity Experiments. N-of-1 Designs. Tests of Quantitative Laws. Tests of Direction and Magnitude of Effect. Fundamentals of Validity. General Guidelines and Software Availability.

About the Author

Eugene Edgington, Patrick Onghena

Reviews

"...Overall, this is an interesting and well-written book that provides a useful discussion of the theory, design, and application of randomization tests, illustrated with appropriate examples using experimental data. The end-of-chapter questions and exercises make it useful also as a textbook for college students. It should be of interest for every experimenter who is interested in randomization or permutation tests or is skeptical about the reliability of the assumptions of parametric tests."
-Andreas Karlsson (Uppsala University), Journal of the Royal Statistical Society

"...Overall, this is an interesting and well-written book that provides a useful discussion of the theory, design, and application of randomization tests, illustrated with appropriate examples using experimental data. The end-of-chapter questions and exercises make it useful also as a textbook for college students. It should be of interest for every experimenter who is interested in randomization or permutation tests or is skeptical about the reliability of the assumptions of parametric tests."
-Andreas Karlsson (Uppsala University), Journal of the Royal Statistical Society"I believe Randomization Tests will be useful for those researchers who often work with data that are not compatible with the usual parametric assumptions (e.g., normality). In these cases, the permutation and randomization tests discussed in the text are more appropriate and more reliable." - Michael Sherman, Texas A&M University, in Journal of the American Statistical Association, June 2009, Vol. 104, No. 486

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