Phillip I. Good

Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses

This book provides a step-by-step manual on the application of permutation tests in biology, medicine, science, and engineering. Its intuitive and informal style will ideally suit it as a text for students and researchers coming to these methods for the first time. In particular, it shows how the problems of missing and censored data, nonresponders, after-the-fact covariates, and outliers may be handled.

Common Errors in Statistics (and How to Avoid Them): Good/Common Errors in Statistics (and How to Avoid Them), Second Edition

Common errors in statistics (and how to avoid them) , Common errors in statistics (and how to avoid them) , کتابخانه دیجیتال جندی شاپور اهواز

Common Errors in Statistics (And How to Avoid Them): Good/Common Errors 4E

Reading is a hobby to open the knowledge windows. Besides, it can provide the inspiration and spirit to face this life. By this way, concomitant with the technology development, many companies serve the e-book or book in soft file. The system of this book of course will be much easier. No worry to forget bringing the common errors in statistics and how to avoid them book. You can open the device and get the book by on-line.

Power of a Test

In Chapter 3, you were introduced to some practical, easily computed tests of hypotheses. But are they the best tests one can use? And are they always appropriate? In this chapter, we consider the assumptions that underlie a statistical test and look at some of a test’s formal properties: its significance level, power, and robustness.

Testing a Hypothesis

In Chapter 1, we learned how to describe a sample and how to use the sample to describe and estimate the parameters of the population from which it was drawn. In this chapter, we learn how to frame hypotheses and alternatives about the population and to test them using the relabeling method. We learn the fundamentals of probability and apply them in practical situations. And we learn how to draw random, representative samples that can be used for testing and estimation.

Which Statistic Should I Use

This chapter provides you with an expert system for use in choosing an appropriate estimation or testing technique. Your expert system comes to you in two versions—a professional’s handbook with detailed explanations of the choices and a quick-reference version at the end of the chapter.

Multiple Variables and Multiple Hypotheses

The value of an analysis based on simultaneous observations of several variables such as height, weight, blood pressure, and cholesterol level, for example, is that it can be used to detect subtle changes that might not be detectable, except with very large, prohibitively expensive samples, were we to consider only one variable at a time.

When the Distribution Is Known

One of the strengths of the hypothesis-testing procedures described in the preceding chapter is that you don’t need to know anything about the underlying population(s) to apply them. But suppose you do know something about these populations, that you have full knowledge of the underlying processes that led to the observations in your sample(s), should you still use the same statistical tests? The answer is no,not always, particularly with very small or very large amounts of data. In this chapter, we’ll consider several parametric approximations, including the binomial (which you were introduced to in Chapter 2), the Poisson, and the normal or Gaussian, along with several distributions derived from the latter that are of value in testing location and dispersion.

Experimental Design and Analysis

Failing to account for or balance extraneous factors can lead to major errors in interpretation. In this chapter, you’ll learn to block or measure all factors that are under your control and to use random assignment to balance the effects of those you cannot. You’ll learn to design experiments to investigate multiple factors simultaneously, thus obtaining the maximum amount of information while using the minimum number of samples.