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.

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.

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.

This is the first textbook for psychologists which combines the model comparison method in statistics with a hands-on guide to computer-based analysis and clear explanations of the links between models, hypotheses and experimental designs. Statistics is often seen as a set of cookbook recipes which must be learned by heart. Model comparison, by contrast, provides a mental roadmap that not only gives a deeper level of understanding, but can be used as a general procedure to tackle those problems which can be solved using orthodox statistical methods.Statistics and Experimental Design for Psychologists focusses on the role of Occam's principle, and explains significance testing as a means by which the null and experimental hypotheses are compared using the twin criteria of parsimony and accuracy. This approach is backed up with a strong visual element, including for the first time a clear illustration of what the F-ratio actually does, and why it is so ubiquitous in statistical testing.The book covers the main statistical methods up to multifactorial and repeated measures, ANOVA and the basic experimental designs associated with them. The associated online supplementary material extends this coverage to multiple regression, exploratory factor analysis, power calculations and other more advanced topics, and provides screencasts demonstrating the use of programs on a standard statistical package, SPSS.Of particular value to third year undergraduate as well as graduate students, this book will also have a broad appeal to anyone wanting a deeper understanding of the scientific method.

This book provides the first time user of statistics with an understanding of how and why statistical experimental design and analysis can be an effective problem solving tool. It presents experimental designs which are useful for small screening and response surface experiments.

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.