Stop stressing. There's a simpler way. Is hypothesis testing overwhelming you? In Introductory Statistics, hypothesis testing is one of the most difficult topics and every teacher, textbook, YouTube video explains in a slightly different way. If it's making you frustrated, you're not alone. The solution? Learn the 5-Step Method to Crush Hypothesis Testing. This book will help you to: Make hypothesis testing easier and understandable Avoid the most common mistakes students make on hypothesis testing questions Recognize what type of hypothesis testing question you're dealing with and what variables you should use Formulate your null and alternative hypotheses Identify which distribution and test you should (and shouldn't) use Correctly use the Critical Value Method and the P-value Method (and how they differ) Understand what your result means and write a conclusion Go into your Statistics test confident that you can answer whatever hypothesis testing question your teacher throws at you About the Authors Allison Dillard is a math professor, author of Crush Math Now and the Love Math Journal, and host of the Allison Loves Math Podcast. You can find her at allisonlovesmath.com. Jennifer Flenner is a math professor, former Navy researcher, and Statistics enthusiast.

Written to appeal to students and instructors who appreciate statistics for its precision and logic, Introductory Statistics: A Problem-Solving Approach helps students learn statistical concepts by using a stepped problem-solving approach. After completing an introductory statistics course with this textbook, students should understand the process of basic statistical arguments. They should grasp the importance of assumptions and be able to follow valid arguments or identify inaccurate conclusions. Most importantly, they should understand the process of statistical inference. The philosophy of this text is simple: statistics is often hard for students, and in order to understand concepts, the material must be presented in an orderly, precise, friendly manner. It must be easy to read and follow, and there must be numerous examples and exercises. The text aims to be easy-to-read, down-to-earth, systematic, and methodical. Each new idea builds upon concepts presented earlier. A touch of humor is important, especially for many students who are afraid of, and even dislike, mathematics and statistics.

Carpal-tunnel and other entrapment syndromes are perhaps the most common work-related injuries currently reported. With this book, Vladimir Golovchinsky presents the first evidence of double-crush syndrome as a subgroup of these. To date the existence of double-crush syndrome has been a matter of debate. Dr. Golovchinsky presents a statistical analysis of substantial clinical material, which finds a cause-and-effect relationship between cervical or lumbar radiculopathy and peripheral entrapment in corresponding nerves, thus proving the existence of double-crush syndrome. This book will be invaluable to physicians performing EMG-NCV testing and to other healthcare professionals who encounter peripheral entrapment syndromes in their practices.

Monte Carlo simulation has become one of the most important tools in all fields of science. Simulation methodology relies on a good source of numbers that appear to be random. These "pseudorandom" numbers must pass statistical tests just as random samples would. Methods for producing pseudorandom numbers and transforming those numbers to simulate samples from various distributions are among the most important topics in statistical computing. This book surveys techniques of random number generation and the use of random numbers in Monte Carlo simulation. The book covers basic principles, as well as newer methods such as parallel random number generation, nonlinear congruential generators, quasi Monte Carlo methods, and Markov chain Monte Carlo. The best methods for generating random variates from the standard distributions are presented, but also general techniques useful in more complicated models and in novel settings are described. The emphasis throughout the book is on practical methods that work well in current computing environments. The book includes exercises and can be used as a test or supplementary text for various courses in modern statistics. It could serve as the primary test for a specialized course in statistical computing, or as a supplementary text for a course in computational statistics and other areas of modern statistics that rely on simulation. The book, which covers recent developments in the field, could also serve as a useful reference for practitioners. Although some familiarity with probability and statistics is assumed, the book is accessible to a broad audience. The second edition is approximately 50% longer than the first edition. It includes advances in methods for parallel random number generation, universal methods for generation of nonuniform variates, perfect sampling, and software for random number generation.

This book is about mathematical resilience: what it is, why it is important, how learners can develop it and how teachers can teach in ways that help learners become mathematically resilient. Teaching for mathematical resilience helps mitigate mathematics anxiety, which is often hidden from view as poor behaviour, avoidance and underachievement and can have long-term implications especially for people’s employment opportunities and career progression. Written by a team of expert contributors that specialise in teaching every age of learner from primary school through to adult, the book shows that everyone can progress in mathematics if they have Mathematical Resilience and sets out practical strategies to support learners in developing this important attribute. Chapters cover: How to measure mathematics anxiety and resilience Tools for building mathematical resilience Building a resilient mathematical learning environment The power of coaching Working with groups, individuals and support staff Supporting parents and carers The world faces huge challenges that involve mathematical thinking, including climate change and economic injustice. This valuable text shows teachers how they can help learners to engage positively with mathematical learning and reach their full potential.

How do people search evidence for a hypothesis? A well documented answer in cognitive psychology is that they search for confirming evidence. However, the rational strategy is to try to falsify the hypothesis. This book critically evaluates this contradiction. Experimental research is discussed against the background of philosophical and formal theories of hypothesis testing with striking results: Falsificationism and verificationism - the two main rival philosophies of testing - come down to one and the same principle for concrete testing behaviour, eluding the contrast between rational falsification and confirmation bias. In this book, the author proposes a new perspective for describing hypothesis testing behaviour - the probability-value model - which unifies the contrasting views. According to this model, hypothesis testers pragmatically consider what evidence and how much evidence will convince them to reject or accept the hypothesis. They might either require highly probative evidence for its acceptance, at the risk of its rejection, or protect it against rejection and go for minor confirming observations. Interestingly, the model refines the classical opposition between rationality and pragmaticity because pragmatic considerations are a legitimate aspect of 'rational' hypothesis testing. Possible future research and applications of the ideas advanced are discussed, such as the modelling of expert hypothesis testing.