Beyond Basic Statistics: An interview with author Kristin H. Jarman on what every data analyst should know

Features

  • Author: Statistics Views
  • Date: 11 May 2015

This month, Wiley is proud to publish Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know which features basic statistical concepts as a tool for thinking critically, wading through large quantities of information, and answering practical, everyday questions.

Author Kristin H. Jarman writes in an engaging and inviting manner and presents the more subjective side of statistics—the art of data analytics. Each chapter explores a different question using fun, common sense examples that illustrate the concepts, methods, and applications of statistical techniques.

The book features:

  • Plentiful examples throughout aimed to strengthen readers’ understanding of the statistical concepts and methods
  • A step-by-step approach to elementary statistical topics such as sampling, hypothesis tests, outlier detection, normality tests, robust statistics, and multiple regression
  • A case study in each chapter that illustrates the use of the presented techniques
  • Highlights of well-known shortcomings that can lead to false conclusions
  • An introduction to advanced techniques such as validation and bootstrapping

Featuring examples that are engaging and non-application specific, the book appeals to a broad audience of students and professionals alike, specifically students of undergraduate statistics, managers, medical professionals, and anyone who has to make decisions based on raw data or compiled results.

Author Kristin H. Jarman is a Senior Research Scientist at the Pacific Northwest National Laboratory in Richland, Washington. Statistics Views talks to her about the writing process and her career.

thumbnail image: Beyond Basic Statistics: An interview with author Kristin H. Jarman on what every data analyst should know

1. Congratulations on the publication of your book, Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know which features basic statistical concepts as a tool for thinking critically, wading through large quantities of information, and answering practical, everyday questions. You have also written another book for Wiley, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics. Is this book its follow-up? How did the writing process begin?

Beyond Basic Statistics is a follow-up to The Art of Data Analysis. This book discusses the limitations of the most basic statistical techniques, limitations such as the assumption that your data are perfectly bell-shaped without any extreme values. It teaches methods that can be used under these less-than-perfect circumstances.

Simply put, the writing process began as a result of years of working with colleagues who claim to hate statistics, but find themselves immersed in data on a daily basis. These people could read the traditional textbooks and memorize formulas, but this didn’t really help them digest, analyze, and make meaningful conclusions from their data. I’ve always loved to write, so I set out to create a series of books that show people how to do this.

2. What were your main objectives during the writing process?

There were four of them. First, I wanted to introduce the common statistical methods and show when and how to use them. Second, I wanted to illustrate the use of these methods on real world data. Third, I wanted to provide readers with examples on how to think critically through the entire data analysis process, from data collection to presentation of the results. And finally, I wanted to show readers that statistics can be fun.

3. The book presents the more subjective side of statistics—the art of data analytics. Each chapter explores a different question using fun, common sense examples that illustrate the concepts, methods, and applications of statistical techniques. Were these examples ones you had worked through yourself during research or examples from colleagues?

When I was learning statistics, one of my pet peeves was having to wade through example datasets that frankly, I cared nothing about. I work in the area of advanced scientific instrumentation, and while I find it interesting, I recognize most readers out there don’t. So rather than pulling examples from my work over the years, I came up with completely new ones. Each example is meant to be engaging and easy-to understand, and I constructed them so that they not only provide me data to work with, but they also reinforce the general idea of the statistical topics presented.

...rather than pulling examples from my work over the years, I came up with completely new ones. Each example is meant to be engaging and easy-to understand, and I constructed them so that they not only provide me data to work with, but they also reinforce the general idea of the statistical topics presented.

4. Throughout the book, you are careful not to go into the specifics of theorems, propositions, or formulas, so that the book effectively demonstrates statistics as a useful problem-solving tool. In addition, you demonstrate how statistics is a tool for thinking critically, wading through large volumes of information, and answering life’s important questions. If there was one piece of information or advice that you would want your reader to take away and remember after reading this, what would that be?

That’s easy. Know your data. Statistics, predictive models, machine learning, and all other data analysis techniques are useful tools, but that’s all they are: tools. Just like a hammer can’t tell you when a staple gun is a better choice, these tools can’t tell you when they do not answer your research question. They don’t understand the limitations of a particular dataset. And they definitely can’t put your conclusions into context for you. You have to do this and to do it well, you need to understand your data -- where it comes from, how it was collected, how it will (or won’t) address your question, what unusual characteristics it has, and how all these things impact your conclusions.

5. Who should read the book and why?

Anyone just learning how to digest and analyse data could benefit from this book. It teaches basic nonparametric methods that can be used on virtually any dataset, whether numerical or categorical, bell-shaped, bimodal, or truly oddball. More importantly, it highlights common problems that arise when analysing data and shows the reader how to deal with them. And like The Art of Data Analysis, this book does this by taking the reader through a series of fun, easy-to-understand, real-world case studies.

6. Why is this book of particular interest now?

Data science is a rapidly growing field and with improvements in technology, our dependence on statistical methods to digest and understand large volumes of information will only increase. Many people in many fields are inundated with data, and very few of them really know what to do with it. The Art of Data Analysis and Beyond Basic Statistics provide readers with the skills needed to navigate a typical data analysis using software almost everyone has already sitting on their computer desktop.

7. Were there areas of the book that you found more challenging to write, and if so, why?

The most challenging part of writing this book was probably trying to keep it light-hearted. I had a lot of statistical techniques to cover, and it was easy to fall into a more traditional, serious presentation of the material. I had to keep reminding myself to take a step back, remind the reader of the question I was trying to answer in each chapter, and have fun.

I had to keep reminding myself to take a step back, remind the reader of the question I was trying to answer in each chapter, and have fun.

8. What is it about this particular area that fascinates you?

Although my formal training is in mathematics, probability, and statistics, I’m a scientist at heart. I love the process of asking a question and trying to figure out how to answer that question with data. I also love being able to apply my speciality to many different scientific areas, and make contributions in those areas. It keeps me from getting bored.

9. What will be your next book-length undertaking?

I’m currently writing a book on big data analytics. Written in the same style as my first two books, this one will be a little more advanced, covering common statistical and machine learning methods used in the big data sciences. The book is currently scheduled to be published by Wiley in January 2017.

10. You are currently a Senior Research Scientist at the Pacific Northwest National Laboratory in Richland, Washington. Please could you tell us more about your educational background and what was it that brought you to recognise statistics as a discipline in the first place?

I started my college life at University of Colorado, Boulder as a chemistry major. After a couple years, I realized playing on a computer was a lot more fun than spending hours ruining experiments in a smelly laboratory. I switched to Applied Mathematics, graduated, and went on to graduate school at Northwestern University in Evanston, Illinois. It wasn’t until after I’d earned my Master’s degree in Applied Mathematics that I decided differential equations and fluid dynamics wasn’t for me. After a long talk with one of the fantastic statistics and probability professors there, I realized statistics could give me the perfect combination of mathematics, science, and diversity of problems to work on. I was hooked. I changed my area of focus and a few years later, earned my Ph.D. in the field.

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