Computational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy! gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use.

The chief executive officer of a corporation is not much different from a public school administrator. While CEOs base many of their decisions on data, for school administrators, this type of research may conjure up miserable memories of searching for information to meet a graduate school requirement. However, the value of data-based decision making will continue to escalate and the school community—students, teachers, parents and the general public—expect this information to come from their administrators. Administrators are called on to be accountable, but few are capable of presenting the mountain of data that they collect in a cohesive and strategic manner. Most statistical books are focused on statistical theory versus application, but Statistics Made Simple for School Leaders presents statistics in a simple, practical, conceptual, and immediately applicable manner. It enables administrators to take their data and manage it into strategic information so the results can be used for action plans that benefit the school system. The approach is 'user friendly' and leaves the reader with a confident can-do attitude to communicate results and plans to staff and the community.

Build a solid foundation for understanding how hypothesis tests work and become confident that you know when to use each type of test, how to use them properly to obtain reliable results, and interpret the results correctly. Chances are high that you'll need a working knowledge of hypothesis testing to produce new findings yourself and to understand the work of others. I present a wide variety of tests that assess characteristics of different data types. I focus on helping you grasp key concepts, methodologies, and procedures while deemphasizing equations. Learn how to use these tests painlessly in this ebook!In today's data-driven world, we hear about making decisions based on the data all the time. Hypothesis testing plays a crucial role in that process, whether you're in academia, making business decisions, or in quality improvement. Without hypothesis tests, you risk drawing the wrong conclusions and making bad decisions. The world today produces more data and more analyses designed to influence you than ever before. Are you ready for it? In this 367-page ebook, build the skills and knowledge you'll need for effective hypothesis testing, including the following: Why you need hypothesis tests and how they work. Using significance levels, p-values, confidence intervals. Select the correct type of hypothesis test to answer your question. Learn how to test means, medians, variances, proportions, distributions, counts, correlations for continuous and categorical data, and outliers. Use One-Way ANOVA, Two-Way ANOVA and interaction effects. Interpreting the results. Checking assumptions and obtaining reliable results. Manage the error rates for false positives and false negatives. Understand sampling distributions, central limit theorem, and statistical power. Know how t-tests, F-tests, chi-squared, and post hoc tests work. Learn about the differences between parametric, nonparametric, and bootstrapping methods. Examples of different types of hypothesis tests. Downloadable datasets so you can try it yourself. For each hypothesis test I cover, you will learn what it tells you, understand its assumptions, know how to interpret the results, and work through examples with downloadable datasets.

It is not necessary to know how to do a statistical analysis to critically appraise a paper. However, it is necessary to have a grasp of the basics, of whether the right test has been used and how to interpret the resulting figures. Short, readable, and useful, this book provides the essential, basic information without becoming bogged down in the

Researchers across the natural and social sciences find themselves navigating tremendous amounts of new data. Making sense of this flood of information requires more than the rote application of formulaic statistical methods. The premise of Statistical Thinking from Scratch is that students who want to become confident data analysts are better served by a deep introduction to a single statistical method than by a cursory overview of many methods. In particular, this book focuses on simple linear regression-a method with close connections to the most important tools in applied statistics-using it as a detailed case study for teaching resampling-based, likelihood-based, and Bayesian approaches to statistical inference. Considering simple linear regression in depth imparts an idea of how statistical procedures are designed, a flavour for the philosophical positions one assumes when applying statistics, and tools to probe the strengths of one's statistical approach. Key to the book's novel approach is its mathematical level, which is gentler than most texts for statisticians but more rigorous than most introductory texts for non-statisticians. Statistical Thinking from Scratch is suitable for senior undergraduate and beginning graduate students, professional researchers, and practitioners seeking to improve their understanding of statistical methods across the natural and social sciences, medicine, psychology, public health, business, and other fields.

This new edition of Medical Statistics Made Easy 2nd edition enables readers to understand the key statistical techniques used throughout the medical literature. Featuring a comprehensive updating of the 'Statistics at work' section, this new edition retains a consistent, concise, and user-friendly format. Each technique is graded for ease of use and frequency of appearance in the mainstream medical journals. Medical Statistics Made Easy 2nd edition is essential reading for anyone looking to understand: * confidence intervals and probability values * numbers needed to treat * t tests and other parametric tests * survival analysis If you need to understand the medical literature, then you need to read this book. Reviews: "This book helps medical students understand the basic concepts of medical statistics starting in a 'step-by-step approach'. The authors have designed the book assuming that the reader has no prior knowledge. It focuses on the most common statistical concepts that are likely to be faced in medical literature. All chapters are concise and simple to understand. Each chapter starts with an introduction which consists of “how important” that particular statistical concept is, using a 'star' system. A 'thumbs-up' system shows how easy the statistical concept is to understand. Both these systems indicate time-efficient learning allowing yourself to focus on areas you find most difficult. Following this, there are worked out examples with exam-tips at the end of some chapters. The last chapter, 'Statistics at Work', shows how medical statistics is put into practice using worked out examples from renowned journals. This helps in assessing the reader’s own knowledge and gives them confidence in analysis of statistics of a journal. In conclusion, we would recommend this book as an introduction into medical statistics before plunging into the deep 'statistical' waters! It gives confidence to the reader in taking up the challenge of understanding statistics and [being] able to apply knowledge in analysing medical literature." Stefanie Zhao Lin Lip & Louise Murchison, Scottish Medical Journal, June 2010 "If ever there was a book that completely lived up to its title, this is it...Perhaps above everything, it is the chapter layout and design that makes this book stand out head and shoulders above the crowd. At the beginning of each chapter two questions are posed – how important is the subject in question and how difficult is it to understand? The first is answered on the basis of how often the subject is mentioned / used in papers published in mainstream medical journals. A star rating is then given from one to five with five stars implying use in the majority of papers published. The second question is answered by means of a ‘thumbs up’ grading system. The more thumbs, the easier the concept is to understand (maximum of five). This, of course, provides a route into statistics for even the most idle of uneducated individuals! Five stars and five thumbs must surely indicate time-efficient learning! At the end of each chapter exam tips (light bulb icon!) are given – I doubt anyone could ask for more! The whole way in which the authors have written this book is commendable; the chapters are succinct, easy to follow and a pleasure to read...Is it value for money? – a definite yes even at twice the price. Of course I never exaggerate but if you breathe, you should own this book!" Ian Pearce, Urology News, June 2010

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.