Practical Nonparametric Statistics Conover: A Quick Reference Book for Nonparametric Methods
If you are looking for a clear, concise and comprehensive guide on how and when to use nonparametric statistics, you may want to check out Practical Nonparametric Statistics by W. J. Conover. This book, now in its third edition, is a classic text and reference book for advanced undergraduate and graduate students, as well as applied research workers who need to perform nonparametric tests.
Nonparametric statistics, also known as distribution-free statistics, are methods that do not rely on assumptions about the underlying distribution of the data. They are useful when the data are skewed, discrete, ordinal or have outliers. Nonparametric methods are also more robust and flexible than parametric methods, which can be sensitive to violations of assumptions.
In Practical Nonparametric Statistics, Conover covers a wide range of nonparametric methods, such as tests based on the binomial distribution, contingency tables, rank tests, Kolmogorov-Smirnov type tests and bootstrap methods. He also provides examples, exercises, tables and references for each topic. The book is organized into seven chapters:
Introduction: This chapter gives an overview of nonparametric statistics, including their advantages and disadvantages, types and applications.
Statistical Inference: This chapter reviews some basic concepts of statistical inference, such as hypothesis testing, confidence intervals and p-values.
Some Tests Based on the Binomial Distribution: This chapter introduces some nonparametric tests that are based on the binomial distribution, such as the sign test, the runs test and the median test.
Contingency Tables: This chapter discusses how to analyze categorical data using contingency tables, such as the chi-square test, the Fisher exact test and the Mantel-Haenszel test.
Some Methods Based on Ranks: This chapter presents some nonparametric methods that use ranks instead of raw data values, such as the Wilcoxon signed-rank test, the Mann-Whitney U test, the Kruskal-Wallis test and the Friedman test.
Statistics of the Kolmogorov-Smirnov Type: This chapter explains how to compare two or more distributions using statistics of the Kolmogorov-Smirnov type, such as the Kolmogorov-Smirnov test, the Lilliefors test and the Anderson-Darling test.
The Bootstrap Method: This chapter introduces the bootstrap method, which is a resampling technique that can be used to estimate standard errors, confidence intervals and p-values for any statistic.
Practical Nonparametric Statistics by W. J. Conover is a valuable resource for anyone who wants to learn more about nonparametric statistics and how to apply them in real-world situations. You can find this book online or at your local library.
How to Use Practical Nonparametric Statistics Conover
If you want to use Practical Nonparametric Statistics by W. J. Conover as a reference book for your nonparametric analysis, here are some tips on how to make the most of it:
Read the introduction chapter carefully. It will give you a general idea of what nonparametric statistics are and why they are useful. It will also help you decide which nonparametric method is appropriate for your data and research question.
Use the table of contents and the index to find the relevant chapter and section for your problem. The book is well-organized and easy to navigate. You can also use the references at the end of each chapter to find more sources on the topic.
Follow the examples and exercises in the book. They will help you understand the concepts and procedures better. You can also check your answers with the solutions provided at the end of the book.
Use the tables in the appendix to find the critical values and p-values for your tests. The book provides tables for various nonparametric tests, such as the Wilcoxon signed-rank test, the Mann-Whitney U test, the Kruskal-Wallis test and the Kolmogorov-Smirnov test. You can also use online calculators or software to perform the calculations.
Interpret your results carefully. The book explains how to report and interpret the results of nonparametric tests, such as stating the null and alternative hypotheses, reporting the test statistic and p-value, and drawing conclusions based on the significance level. You should also consider other factors that may affect your results, such as sample size, outliers and assumptions.
Practical Nonparametric Statistics by W. J. Conover is a comprehensive and user-friendly guide for nonparametric statistics. It will help you learn and apply nonparametric methods in a variety of situations. Whether you are a student, a researcher or a practitioner, you will find this book useful and informative. a474f39169