Psychological Statistics: The Basics

Psychological Statistics: The Basics

by Thomas J. Faulkenberry
Psychological Statistics: The Basics

Psychological Statistics: The Basics

by Thomas J. Faulkenberry

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Overview

Psychological Statistics: The Basics walks the reader through the core logic of statistical inference and provides a solid grounding in the techniques necessary to understand modern statistical methods in the psychological and behavioral sciences.

This book is designed to be a readable account of the role of statistics in the psychological sciences. Rather than providing a comprehensive reference for statistical methods, Psychological Statistics: The Basics gives the reader an introduction to the core procedures of estimation and model comparison, both of which form the cornerstone of statistical inference in psychology and related fields. Instead of relying on statistical recipes, the book gives the reader the big picture and provides a seamless transition to more advanced methods, including Bayesian model comparison.

Psychological Statistics: The Basics not only serves as an excellent primer for beginners but it is also the perfect refresher for graduate students, early career psychologists, or anyone else interested in seeing the big picture of statistical inference. Concise and conversational, its highly readable tone will engage any reader who wants to learn the basics of psychological statistics.


Product Details

ISBN-13: 9781032020952
Publisher: Taylor & Francis
Publication date: 04/04/2022
Series: The Basics
Pages: 122
Product dimensions: 6.00(w) x 9.00(h) x (d)

About the Author

Thomas J. (Tom) Faulkenberry, Ph.D., is Associate Professor and Head of the Department of Psychological Sciences at Tarleton State University in Stephenville, TX (USA). A mathematician by training, he teaches courses on statistics and mathematical modeling in the behavioral sciences, and his primary research areas are mathematical cognition and Bayesian statistics.

Table of Contents

1. A (Very) Brief Introduction to Statistical Inference 2. Describing the Observed Data 3. Modeling the Observed Data 4. How Likely is the Observed Data? 5. Comparing Statistical Models 6. Introduction to the t-test 7. Bayesian Model Comparison 8. Recap and Next Steps

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