A Pen and Paper Introduction to Statistics
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Statistics is central in the biosciences, social sciences and other disciplines, yet many students often struggle to learn how to perform statistical tests, and to understand how and why statistical tests work. Although there are many approaches to teaching statistics, a common framework exists between them: starting with probability and distributions, then sampling from distribution and descriptive statistics and later introducing both simple and complex statistical tests, typically ending with regression analysis (linear models).
This book proposes to reverse the way statistics is taught, by starting with the introduction of linear models. Today, many statisticians know that the one unifying principle of statistical tests is that most of them are instances of linear models. This teaching method has two advantages: all statistical tests in a course can be presented under the same unifying framework, simplifying things; second, linear models can be expressed as lines over squared paper, replacing any equation with a drawing.
This book explains how and why statistics works without using a single equation, just lines and squares over grid paper. The reader will have the opportunity to work through the examples and compute sums of squares by just drawing and counting, and finally evaluating whether observed differences are statistically significant by using the tables provided. Intended for students, scientists and those with little prior knowledge of statistics, this book is for all with simple and clear examples, computations and drawings helping the reader to not only do statistical tests but also understand statistics.
Antonio Marco, Ph.D., is Senior Lecturer in the School of Life Sciences at The University of Essex. Dr Marco obtained a degree in Biological Sciences and a doctorate in Genetics from the University of Valencia and has a Graduate Diploma in Mathematics from the University of London (LSE). He has been a lecturer at the University of Essex since 2013, teaching statistics, mathematics, genomics and bioinformatics, and is the course director of the BSc Genetics and the MSc Health Genomics programmes.
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