Statistical analysis allows us to attach meaning to data that we have collected; it helps us to understand what experimental results really mean, and to assess whether we can trust what experiments seem to be telling us. Yet, despite being a collection of the most valuable and important tools available to bioscientists, statistics is often the aspect of study most feared by students. Biomeasurement offers a refreshing, student-focused introduction to the use of statistics in the study of the biosciences. With an emphasis on why statistical techniques are essential tools for bioscientists, the book develops confidence in students to use and further explore the key techniques for themselves. Beginning by placing the role of data analysis in the context of the wider scientific method and introducing the student to the key terms and concepts common to all statistical tools, the book then guides the student through descriptive statistics, and on to inferential statistics, explaining how and why each type of technique is used, and what each can tell us in order to better understand our data. It goes on to present the key statistical tests, walking the student step-wise through the use of each, with carefully-integrated examples and plentiful opportunities for hands-on practice. The book closes with an overview of choosing the right test to suit your data, and tools for presenting data and their statistical analyses. Written by a talented educator, Biomeasurement is sure to engage even the most wary of students, demonstrating the power and importance of statistics throughout the study of bioscience. New to this edition The book now supports R through 'help sheets' and screencasts on the Online Resource Centre. The chapter introducing the Generalized Linear Model has been enhanced with further guidance on model choice. A new chapter on binary data expands on the existing coverage of the Generalized Linear Model by introducing the reader to logistic GLMs. More emphasis has been placed on interpretation of results from a biological perspective through greater consideration of effect size. Author screencasts outline key statistical techniques and walk students through the use of statistical analysis packages, SPSS and R. New activities have been added to the Online Resource Centre.