I was once a data hesitant educator. I hated working with data. I loathed the time spent in mandated data analysis meetings. I cringed under my own lack of understanding, skill, and confidence in data analysis. One day, I decided to overcome my frustration with data and set myself to the study of data analysis. Most books on the market are heavy, theoretical tomes that seem to lack any practical advice. In this post, I want to offer my recommendations of books that I found to be insightful and helpful as I sought to exit the data hesitant stage of professional development. The best news – this is probably the only list of book recommendations online that aren’t tied to affiliate sales. They are my own recommendations, offered without reward.
This short, tongue and cheek book uncovers the ways that marketers, media specialists, and occasionally researchers manipulate data to tell the story they wish to tell. Originally published in 1993, How to Lie with Statistics was, for a long time, the most popular book on the market written for mass market consumption. It will help you learn how to take a critical eye to the data points that you come across in your work and give you some insight into spotting statistical trickery wherever it lies.
In Spurious Correlations, Vigen depicts correlational graphs on a wide variety of topics. This book, which is really more of a nerdy gift than an instructional tool, helps to illustrate the common cliché “correlation does not equal causation”. This is my go-to book when I need a little help to find the levity in data analysis or to illustrate a ridiculous point.
Do you ever feel like the world of research and data use is a big… ridiculous? I frequently do. In those times, I turn to Wright’s Academia Obscura. This hilarious book seeks to document the absurdity of the academy. My favorite chapter discusses the manner in which researchers title their articles. I must admit, I am guilty of some of these title clichés as well. This book is a great read for anyone who feels like they simply don’t belong in the world of research and data analysis. Allow it to help you channel your indignation into giving the gatekeepers what for.
It is not often that a book about statistics makes its way on to the New York Times Bestseller list, but once you read this book you will easily understand why. Unlike some of the other books on this list, Naked Statistics dips its toe into the theory and seeks to re-examine it through a slightly less pretentious lens than you may have originally been taught. This book also does a good job of exploring how data can be manipulated – either intentionally or through careless oversight – to prove a point.
In The Data Detective, Tim Harford provides ten clear-cut rules to help even the most data hesitant among us to think about data more critically and accurately. As a reporter, Harford takes a practical, mass-market approach to the discussion of data analysis. The book tells vivid stories and gives great examples of data analysis mistakes that you have probably read or seen in the media. Written during the COVID-19 pandemic, this book feels incredibly timely and relevant to those of us seeking to lead our institutions through the recovery.
So, this one isn’t exactly about data analysis or research – but it is extremely relevant to this cause as it provides an in-depth look into how we think. It can often feel as though data analysis is black and white. The average is what the average is. The results of a statistical test are either significant or not significant. But in Thinking Fast and Slow, Kahneman helps provide a window into the way that we think about our work, the types of questions that we ask, and how biases influence the way we interpret the results of experiments grand and small.
Let’s round out the list with one theoretical volume. After you have read the other books on the list and determined that you are ready to become a true data analysis, Hatcher’s volume will help you take your learning to the next level. This is my go-to manual when I need a refresher on statistical methods and their application or interpretation. I also like that this book has appendices that discuss the proper way to write up and report statistical results and format charts.
I hope that this list of recommendations has given you a good starting point for overcoming your data hesitancy and learning to love data. If the books aren’t quite your speed, check out the resources available in The Repository. There you will find auto-analysis tools, eBooks, and tutorial videos to jumpstart your learning. Good luck on your journey friends, and let me know how I can help.