Wise Use of Null Hypothesis Tests

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 a 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 a3.10 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 a 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.