Applied Statistics
Preface
This book is not written for statisticians. It is for everyone else that uses statistics in their work: ecologists, medical researchers, psychologists, and so on. You need some overview of what methods exist and when to use them. You probably don’t have time (or interest) to start with the mathematical foundations and build your knowledge from the ground up. That is why this book takes a different approach, rooted in application.
Motivation
Classical statistics textbooks typically start with some introductory math, then go from basic probability theory to the normal distribution and the central limit theorem, before moving on to univariate tests (\(t\)-test, \(\chi^2\)-test, \(F\)-test, non-parametric alternatives), and perhaps ending somewhere around ANOVA.1 This makes sense if you’re reading the book as an aspiring statistician—you need to understand each method at its core, and that requires some logical buildup of methodology. I did not make this book as a critique of this style of teaching. In fact, this is how I got into the field of statistics myself.
However, while teaching statistics to life scientists, it has always bothered me that we do not get to work with interesting, actually useful models till near the end of the course. That is what I want to do differently. You need to spend most of the time during your course working with flexible models. From the get go, we will start thinking in terms of probability distributions and try to decide which might be suitable for our problem. You may not understand what a generalized linear model really is in the beginning, but you will already be able to use it. As you gain more experience working with these models, you can always delve into the fundamentals at a later stage.
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