Statistics at the bench: A step-by-step handbook for biologists

✍ Scribed by Craig Paul

• Publisher: The American Society for Biochemistry and Molecular Biology
• Year: 2011
• Tongue: English
• Weight: 34 KB
• Volume: 39
• Category: Article
• ISSN: 1470-8175
• DOI: 10.1002/bmb.20497

✦ Synopsis

The authors present a very useful and a readable guide to the biological applications of statistics, and they state their purpose very clearly in their introduction: ''this is a bench side manual that is designed and intended to be used by people who are in need of a quick refresher or a big-picture overview of a statistical procedure.'' Briefly, they achieved their purpose.

The book consistently presents and interprets the language of the statistician so that a bench scientist with limited or no formal training in statistics can grasp the principles being discussed. Each chapter presents abstract ideas and just enough theory to enable users to move on to the specific examples that are provided. Although they mention a number of dedicated statistical software packages, the authors chose to present their examples with specific instructions on data manipulation and evaluation in Microsoft Excel, ''because it is one of the most commonly available software applications and is widely used by biologists today.''

CHAPTER 1 INTRODUCTION

The authors describe the increasing need for statistical evaluation as biology becomes more data intensive. Also, they explain their approach in their writing and the reasons they presented their data analysis with command scripts for Microsoft Excel.

CHAPTER 2 COMMON PITFALLS This chapter focuses primarily on vocabulary, explaining common mistakes or misconceptions that happen in conversations with statisticians. Some of the terminology was confusing me. My training in analytical chemistry identified two sources of error: random and systematic. In this text, the authors list biological variation and technical variation. It is unclear to me if these are different from random error (biological variation) and systematic error (technical variation).

CHAPTER 3 DESCRIPTIVE STATISTICS