Learning from Data: An Introduction to Statistical Reasoning

Learning from Data: An Introduction to Statistical Reasoning

Learning from Data: An Introduction to Statistical Reasoning

Learning from Data: An Introduction to Statistical Reasoning

Synopsis

'Learning from Data: An Introduction to Statistical Reasoning' teaches you a new way of thinking about and learning about the world. After you have learned the basics of statistical reasoning, you will be in a good position to understand psychological data, and their limitations. More important, even if you are not planning a career in psychology, you will learn how to evaluate data that affect all aspects of your life: psychological, social, educational, political, economic. You will learn new ways to think, to question, to challenge.

Excerpt

Well, it's true; statistics is a difficult subject. There is a lot to learn, and much of it involves new ways of thinking. This does not mean that you should panic or quit; rather, it means that you must apply yourself to learning the material. Reading a statistics text is not like reading a mystery.

You might ask, "Why should I bother?" There are two good reasons. The first reason is simply that it is worth it. As the title implies, Learning from Data: An Introduction to Statistical Reasoning teaches you a new way of thinking about and learning about the world. After you have learned the basics of statistical reasoning, you will be in a good position to understand psychological data -- and their limitations. More important, even if you are not planning a career in psychology, you will learn how to evaluate data that affect all aspects of your life: psychological, social, educational, political, economic. You will learn new ways to think, to question, to challenge.

A second reason to read this text is that it is written in a way that will help you retain the material. Cognitive psychologists have developed (from data) techniques that facilitate memory and comprehension, and I have incorporated three of these techniques into the book. First, I have devoted extra attention to explaining difficult-to-understand concepts in enough detail to give you a fighting chance of understanding them. For example, some textbooks attempt to combine important concepts such as sampling distributions, hypothesis testing, power, and parameter estimation in one chapter. In this book, each concept has its own chapter. Yes, this means more reading. But it also means greater understanding.

Second, the book uses repetition extensively to help you learn and remember concepts. There are multiple fully explained examples of each major procedure. Many concepts (for example, power, Type I errors) are repeated from chapter to chapter. You will also repeatedly review what you have learned because the problem sets at the ends of most chapters require you to apply principles introduced in earlier chapters.

The third major learning aid is the use of a consistent schema (the six-step procedure) for describing all statistical tests from the simplest to the most complex. Learning the schema at an early stage (in Chapter 8) will ease your way through . . .

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