帰無仮説の賢い利用法：神経科学のための実践的ハンドブック Wise Use of Null Hypothesis Tests : A Practitioner's Handbook

著者名：Corotto, Frank S

Academic Press（2022/10/14発売）

• 言語：ENG
• ISBN：9780323952842
• eISBN：9780323952859

Description

Few students sitting in their introductory statistics class learn that they are being taught the product of a misguided effort to combine two methods into one. Few students learn that some think the method they are being taught should be banned. Wise Use of Null Hypothesis Tests: A Practitioner's Handbook follows one of the two methods that were combined: the approach championed by Ronald Fisher. Fisher's method is simple, intuitive, and immune to criticism.Wise Use of Null Hypothesis Tests is also a user-friendly handbook meant for practitioners. Rather than overwhelming the reader with endless mathematical operations that are rarely performed by hand, the author of Wise Use of Null Hypothesis Tests emphasizes concepts and reasoning. In Wise Use of Null Hypothesis Tests, the author explains what is accomplished by testing null hypotheses—and what is not. The author explains the misconceptions that concern null hypothesis testing. He explains why confidence intervals show the results of null hypothesis tests, performed backwards. Most importantly, the author explains the Big Secret. Many—some say all—null hypotheses must be false. But authorities tell us we should test false null hypotheses anyway to determine the direction of a difference that we know must be there (a topic unrelated to so-called one-tailed tests). In Wise Use of Null Hypothesis Tests, the author explains how to control how often we get the direction wrong (it is not half of alpha) and commit a Type III (or Type S) error.- Offers a user-friendly book, meant for the practitioner, not a comprehensive statistics book- Based on the primary literature, not other books- Emphasizes the importance of testing null hypotheses to decide upon direction, a topic unrelated to so-called one-tailed tests- Covers all the concepts behind null hypothesis testing as it is conventionally understood, while emphasizing a superior method- Covers everything the author spent 32 years explaining to others: the debate over correcting for multiple comparisons, the need for factorial analysis, the advantages and dangers of repeated measures, and more- Explains that, if we test for direction, we are practicing an unappreciated and unnamed method of inference

Table of Contents

Chapter 1. The conventional method is a flawed fusion1.1 Three statisticians, two methods, and the mess that should be banned1.2 Wise use and testing nulls that must be false1.3 Null hypothesis testing in perspectiveChapter 2. The point is to generalize beyond our results2.1 Samples and populations2.2 Real and hypothetical populations2.3 Randomization2.4 Know your population, and do not generalize beyond itChapter 3. Null hypothesis testing explained3.1 The effect of sampling error3.2 The logic of testing a null hypothesis3.3 We should know from the start that many null hypotheses cannot be correct3.4 The traditional explanation of how to use p3.5 What use of α accomplishes3.6 The flawed hybrid in action3.7 Criticisms of the flawed hybrid3.8 We should test nulls in a way that answers the criticisms3.9 How to use p and α3.10 Mouse preference, done right this time3.11 More p-values in action3.12 What were the nulls and predictions?3.13 What if p50.05000?3.14 A radical but wise way to use p3.15 0.05 or .05? p or P?Chapter 4. How often do we get it wrong?4.1 Distributions around means4.2 Distributions of test statistics4.3 Null hypothesis testing explained with distributions4.4 Type I errors explained4.5 Probabilities before and after collecting data4.6 The null's precision explained4.7 The awkward definition of p explained4.8 Errors in direction4.9 Power and errors in direction4.10 Manipulating power to lower p-values4.11 Increasing power with one-tailed tests4.12 Power and why we should we set α to 0.10 or higher4.13 Power, estimated effect size, and type M errors4.14 How can we know a population's distribution?Chapter 5. Important things to know about null hypothesis testing5.1 Examples of null hypotheses in proper statistics books and what they really mean5.2 Categories of null hypotheses?5.3 What if is important to accept the null?5.4 Never do this5.5 Null hypothesis testing as never explained before5.6 Effect size: what is it and when is it important?5.7 We should provide all results, even those not statistically "significantChapter 6. Common misconceptions6.1 Null hypothesis testing is misunderstood by many6.2 Statistical "significance means a difference is large enough to be important—wrong!6.3 p is the probability of a type I error—wrong!6.4 If results are statistically "significant, we should accept the alternative hypothesis that something other than the null is correct—wrong!6.5 If results are not statistically "significant, we should accept the null hypothesis—wrong!6.6 Based on p we should either reject or fail to reject the null hypothesis—often wrong!6.7 Null hypothesis testing is so flawed that we should use confidence intervals instead—wrong!6.8 Power can be used to justify accepting the null hypothesis—wrong!6.9 The null hypothesis is a statement of no difference—not always6.10 The null hypothesis is that there will be no significant difference between the expected and observed values—very, very wrong!6.11 A null hypothesis should not be a negative statement—wrong!Chapter 7. The debate over null hypothesis testing and wise use as the solution7.1 The debate over null hypothesis testing7.2 Communicate to educate7.3 Plan ahead7.4 Test nulls when appropriate, not promiscuously7.5 Strike the right balance between what is conventional and what is best7.6 Think outside of the null hypothesis test7.7 Encourage our audience to draw their own conclusions7.8 Allow ourselves to draw our own conclusions7.9 Strike the right balance when providing our results7.10 Know the misconceptions and do not fall for them7.11 Do not say that two groups "differ or "do not differ7.12 Provide all results somehow7.13 Other reformed methods of null hypothesis testingChapter 8. Simple principles behind the mathematics and some essential concepts8.1 Why different types of data require different types of tests8.1.1 Simple principles behind the mathematics8.1.2 Numerical data exhibit variation8.