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# Statistics for Biomedical Engineers and Scientists Author Q&A

• Can you explain why a knowledge of statistics is important for the biomedical engineer?

The aim of biomedical engineering is to develop new technological solutions to healthcare problems. For example, these solutions can be treatments, diagnostic tests or predictive models. Like all new techniques it is important that their use is supported by evidence and therefore much effort is put into gathering data for such purposes. Statistics is the tool that allows us to make sense of these large amounts of data. This process typically involves the use of statistics to visualize and numerically summarize the gathered data, and subsequently to test hypotheses such as whether or not the new solution is better than an existing one. Without statistics we would be in the dark about the true effectiveness of the solutions developed by biomedical engineers, so really it is (or should be) an essential part of the process of developing new technological tools in healthcare

• You have been teaching statistics to biomedical engineers for a number of years. How has that experience influenced your approach to writing this textbook?

Students in any subject can be motivated to learn by realising the practical utility of the concepts being taught. When teaching statistics to biomedical engineering students it is essential to demonstrate how the fundamental concepts of statistics are not purely abstract and mathematical, but can have a big impact in the real world. Therefore, we believe that illustrating concepts using realistic biomedical examples is very important, and we have put in a lot of work over the years on developing practical examples with this in mind.

Another important factor that needs to be considered when teaching statistics is how much of the underlying theory of statistical techniques should be included. It is often possible to apply statistical techniques in a “black box” sense, in other words without understanding how they work. We believe that this approach does not really equip students with the background knowledge needed to make informed decisions about which approach to use, or to be able to extend their knowledge in the future. At the other extreme, it is possible to present rigorous mathematical proofs of all techniques and insist that students are familiar with these. We think that this type of knowledge is probably only of real use to specialists in statistics. We choose to take a middle road, in which we try to get across the fundamental intuition behind statistical techniques without getting bogged down with too much mathematical detail. This approach has grown out of our experience of teaching biomedical engineering students and we believe that it provides them with an appropriate level of knowledge for their needs.

• You have used biomedical engineering examples, with some using MATLAB. Can you say more about these examples?

We are lucky that our Biomedical Engineering department at King’s College London is based in a hospital (St. Thomas’ Hospital in London). Many clinicians with different specialisms have academic positions within the department and this gives us easy access to their expertise. We have used this expertise to devise a wide range of realistic examples that we hope illustrate the practical uses of statistics in healthcare and technology. For example, we include examples based on the visualization and analysis of medical images such as MR, CT, PET and ultrasound, and the evaluation of new robotic surgery technologies.

Of course, although it is valuable for students to learn how to apply statistical visualizations and analyses “by hand”, almost nobody these days does so routinely. It is much more common to use a computational package. MATLAB is one such package and we chose it because of the additional flexibility it provides. MATLAB is not just a statistics package – it also enables users to write programs to read, process and manipulate data. In our experience, in biomedical engineering this type of data processing is often necessary before applying statistical techniques, and this is why we chose MATLAB for this book. But we have written the book in such a way that it is possible to use it without engaging with the MATLAB content. All MATLAB content appears at the end of each chapter and is not essential for understanding the key statistical concepts.

• For a student or researcher learning to use statistics for their research, what tips do you have on using your book for this purpose?

Although the book is an ideal text to accompany a taught course on statistics, we have designed it so that it can also be easily used for self-learning. Each chapter starts with a clear set of learning objectives, and each objective indicates whether it applies to the use of MATLAB or not. During the text of the chapters, activities are provided to reinforce the concepts being introduced, and these activities are all linked to one of more of the learning objectives. If readers wish to engage with the MATLAB content, they should also read the section devoted to this at the end of each chapter. Exercises are also provided for each chapter, both using MATLAB and not, which are also linked to the learning objectives. Readers should use these for self-assessment and review the text and activities of any objectives they feel that they are less confident on. Although such an approach may be slower than simply reading the book cover-to-cover, in our opinion it is a much better way of building knowledge on firm foundations in order to come to a thorough and deep knowledge of the subject.

We hope that readers will find this book useful and illuminating, as well as of practical use to workers looking to develop exciting new technologies for use in healthcare.

About the book

• Presents a practical guide on how to visualize and analyze statistical data
• Provides numerous practical examples and exercises to illustrate the power of statistics in biomedical engineering applications
• Gives an intuitive understanding of statistical tests
• Covers practical skills by showing how to perform operations ‘by hand’ and by using MATLAB as a computational tool
• Includes an online resource with downloadable materials for students and teachers

The book is available now on ScienceDirect. For a limited time, you can access Chapter 1: Descriptive Statistics I: Univariate Statistics.

Need your own print copy? Enter code STC319 at the checkout when order via the Elsevier Store to save up to 30%

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Biomedicine & Biochemistry

The disciplines of biomedicine and biochemistry impact the lives of millions of people every day. Research in these areas has led to practical applications in cardiology, cancer treatment, respiratory medicine, drug development, and more. Interdisciplinary fields of study, including neuroscience, chemical engineering, nanotechnology, and psychology come together in this research to yield significant new discoveries. Elsevier’s biomedicine and biochemistry content spans a wide range of subject matter in various forms, including journals, books, eBooks, and online information services, enabling students, researchers, and clinicians to advance these fields. Learn more about our Biomedical and Biochemistry books here.