Essentials of Statistics for Research by Gerow, Ken; Navarro Alberto, Jorge;

Short description:

Offers a working introduction to essential statistical methods that uses an accessible conceptual understanding without excessive mathematical details, emphasizes role of good judgment when choosing analysis approaches - analysis should serve the science, shed light on the story contained in the data.

Long description:

Essentials of Statistics for Research offers a working introduction to essential statistical methods through an accessible conceptual approach without excessive mathematical details. The book emphasizes the importance of good judgment when choosing analysis approaches and illustrates the statistical analysis process through numerous examples. At its core, this text demonstrates how analysis should serve science and illuminate the stories contained within data.

Key Features

• Provides conceptual foundations of a practitioner's statistical toolkit, focusing on the role of normality, hypothesis tests, and confidence intervals


• Presents regression methods as core analytical tools while also covering t-based methods for comparing means among groups


• Demonstrates how logarithmic transformations capture relativity in relationships (such as exponential increase) rather than simply meeting statistical assumptions


• Includes over 100 graphs and visual representations to enhance understanding of statistical concepts


• Written in an engaging first-person voice that positions the authors as fellow learners alongside the reader


• Emphasizes stories, examples, and practical applications over abstract theory

This book is designed for researchers across a broad range of disciplines, from graduate students beginning their research journey to experienced professionals seeking a refresher on statistical methods. The accessible approach makes it particularly valuable for those who need to understand and apply statistical concepts without getting lost in mathematical complexity. Readers will gain practical knowledge they can immediately apply to their own research questions and data analysis challenges.

"Ken Gerow pours his life out into everything he does, so it's no surprise that he and Jorge have made a book that not only encapsulates thirty-plus years of teaching, but also a lifetime of experience. Essentials of Statistics for Researchers is not a textbook in the traditional sense. Rather, this book is a field guide on statistical thinking crafted with a tone that dismantles the fear surrounding mathematics and replaces it with curiosity and confidence. Through real-world examples, this book walks the reader through the use of essential statistical tools in a way that is accessible, informative, and engaging. So whether you're a practicing researcher, a student, or someone who swore statistics off all together, this book invites you back to the table with no judgement, just good teaching. This book is a must-have for any researcher."
~Carson Keeter, Lead Biostatistician, Department of Orthopaedics, University of Colorado Anschutz Medical Campus, USA

“Are you a scientific researcher concerned about how all your data are to be analysed and whether you will do well enough to satisfy your referees or journal editors? If yes, this is the book for you. It is informal, friendly, and full of nuggets of wisdom borne out of long experience of collaborating with field scientists. You will have fun learning the tricks of the trade. Look for entertaining and informative anecdotes. There are some fun facts as well. The authors do a great job of motivating each technique. They go to the roots and talk about what the pioneers wanted to do and how they did it. If you are a field scientist, you may be surprised by references to issues you faced when you did the research. Pay attention to exercises. Doing them may give you new insights into the subject. Happy reading!”
~Anil Gore, Professor of Statistics, Pune University (Retired), India

Table of Contents:

1. Introduction. 2. Data Concepts. 3. The Statistical Law of Gravity (a.k.a the Central Limit Theorem). 4. Using the data: Introducing Hypothesis Tests and Confidence Intervals. 5. How Confidence Intervals and tests play well together (or not). 6. Introduction to Simple Linear Regression. 7. Regression by the Numbers: Making Sense of and Using the Output. 8. Background Reading and a Few New Ideas. 9. The Use of Logarithms in Regression Models. 10. Introduction to Multiple Regression. 11. Multiple Regression Examples. 12. Two Essays on Multiple Regression. 13.  Introduction to Logistic Regression. 14.  One and Two Sample Methods for Means and Proportions. 15.  Relative Inference for Means From Two Samples: Introducing the Bootstrap. 16.  A Brief Introduction to ANOVA. 17.  Response Feature Analyses for Repeated Measures Data. 18.  Epilogue.