First half of book presents fundamental mathematical definitions, concepts, and facts while remaining half deals with statistics primarily as an interpretive tool. Well-written text, numerous worked examples with step-by-step presentation. Includes 116 tables.

This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments often do not replicate. Finally, the textbook shows how complex statistics can be avoided by using clever experimental design. Both non-scientists and students in Biology, Biomedicine and Engineering will benefit from the book by learning the statistical basis of scientific claims and by discovering ways to evaluate the quality of scientific reports in academic journals and news outlets.

Unique in commencing with relatively simple statistical concepts and ideas found in most introductory statistical textbooks, this book goes on to cover more material useful for undergraduates and graduate in statistics and biostatistics.

This book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are takenfrom particle physics, the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques,statistical tests, and methods of parameter estimation. The last three chapters are somewhat more specialized than those preceding, covering interval estimation, characteristic functions, and the problem of correcting distributions for the effects of measurement errors (unfolding).

Even with a limited mathematics background, readers can understand what statistical methods are & how they may be used to obtain the best possible results from experimental measurements & data.

Professionals in all areas – business; government; the physical, life, and social sciences; engineering; medicine, etc. – benefit from using statistical experimental design to better understand their worlds and then use that understanding to improve the products, processes, and programs they are responsible for. This book aims to provide the practitioners of tomorrow with a memorable, easy to read, engaging guide to statistics and experimental design. This book uses examples, drawn from a variety of established texts, and embeds them in a business or scientific context, seasoned with a dash of humor, to emphasize the issues and ideas that led to the experiment and the what-do-we-do-next? steps after the experiment. Graphical data displays are emphasized as means of discovery and communication and formulas are minimized, with a focus on interpreting the results that software produce. The role of subject-matter knowledge, and passion, is also illustrated. The examples do not require specialized knowledge, and the lessons they contain are transferrable to other contexts. Fundamentals of Statistical Experimental Design and Analysis introduces the basic elements of an experimental design, and the basic concepts underlying statistical analyses. Subsequent chapters address the following families of experimental designs: Completely Randomized designs, with single or multiple treatment factors, quantitative or qualitative Randomized Block designs Latin Square designs Split-Unit designs Repeated Measures designs Robust designs Optimal designs Written in an accessible, student-friendly style, this book is suitable for a general audience and particularly for those professionals seeking to improve and apply their understanding of experimental design.

A handbook for those seeking engineering information and quantitative data for designing, developing, constructing, and testing equipment. Covers the planning of experiments, the analyzing of extreme-value data; and more. 1966 edition. Index. Includes 52 figures and 76 tables.

This book focuses on experimental research in two disciplines that have a lot of common ground in terms of theory, experimental designs used, and methods for the analysis of experimental research data: education and psychology. Although the methods covered in this book are also frequently used in many other disciplines, including sociology and medicine, the examples in this book come from contemporary research topics in education and psychology. Various statistical packages, commercial and zero-cost Open Source ones, are used. The goal of this book is neither to cover all possible statistical methods out there nor to focus on a particular statistical software package. There are many excellent statistics textbooks on the market that present both basic and advanced concepts at an introductory level and/or provide a very detailed overview of options in a particular statistical software programme. This is not yet another book in that genre. Core theme of this book is a heuristic called the question-design-analysis bridge: there is a bridge connecting research questions and hypotheses, experimental design and sampling procedures, and common statistical methods in that context. Each statistical method is discussed in a concrete context of a set of research question with directed (one-sided) or undirected (two-sided) hypotheses and an experimental setup in line with these questions and hypotheses. Therefore, the titles of the chapters in this book do not include any names of statistical methods such as ‘analysis of variance’ or ‘analysis of covariance’. In a total of seventeen chapters, this book covers a wide range of topics of research questions that call for experimental designs and statistical methods, fairly basic or more advanced.