
Power Analysis
An Introduction for the Life Sciences
Oxford University Press
Published on 17. November 2020
Book
Paperback/Softback
176 pages
978-0-19-884663-5 (ISBN)
Description
Written primarily for mid-to-upper level undergraduates, this compelling introduction to power analysis in a biological context offers a clear, conceptual understanding of the factors that influence statistical power, and emphasises the importance of high power in experiments. It also explains how to improve the power of an experiment and offers guidance on how to present the outcomes of power analyses to justify experimental design decisions.
Digital formats and resources
The book is available for students and institutions to purchase in a variety of formats, and is supported by online resources:
? The e-book offers a mobile experience and convenient access along with functionality tools, navigation features, and links that offer extra learning support: www.oxfordtextbooks.co.uk/ebooks
? Online resources include multiple choice questions for students to check their understanding, and, for registered adopters, figures and tables from the book
Digital formats and resources
The book is available for students and institutions to purchase in a variety of formats, and is supported by online resources:
? The e-book offers a mobile experience and convenient access along with functionality tools, navigation features, and links that offer extra learning support: www.oxfordtextbooks.co.uk/ebooks
? Online resources include multiple choice questions for students to check their understanding, and, for registered adopters, figures and tables from the book
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Target group
College/higher education
Dimensions
Height: 245 mm
Width: 188 mm
Thickness: 10 mm
Weight
360 gr
ISBN-13
978-0-19-884663-5 (9780198846635)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Nick Colegrave is Professor of Evolutionary Biology at the University of Edinburgh, Scotland. He has held faculty positions there for 20 years, and published over 70 peer-reviewed papers. His research sits at the interface between ecology and evolution, understanding how these processes interact and affect each other. He also has strong interests in infection and disease. He has always taught courses in experimental design and statistics, and gives seminars and conference keynote addresses on issues in these fields.
Graeme Ruxton is Professor of Evolutionary Ecology at the University of St Andrews, Scotland. He has held faculty positions for 25 years, and published over 400 peer-reviewed papers. His research focuses on diverse aspects of behavioural ecology, but he has published numerous papers on aspects of experimental design and statistics, and co-authored a statistical textbook. He has always taught courses in various aspects of experimental design and statistics and has delivered postgraduate workshops on this internationally.
Graeme Ruxton is Professor of Evolutionary Ecology at the University of St Andrews, Scotland. He has held faculty positions for 25 years, and published over 400 peer-reviewed papers. His research focuses on diverse aspects of behavioural ecology, but he has published numerous papers on aspects of experimental design and statistics, and co-authored a statistical textbook. He has always taught courses in various aspects of experimental design and statistics and has delivered postgraduate workshops on this internationally.
Content
1: What is statistical power?
2: Why low power is undesirable
3: Improving the power of an experiment
4: How to quantify power by simulation
5: Simple factorial designs
6: Extensions to other designs
7: Dealing with multiple hypotheses
8: Applying our simulation approach beyond null hypothesis testing
2: Why low power is undesirable
3: Improving the power of an experiment
4: How to quantify power by simulation
5: Simple factorial designs
6: Extensions to other designs
7: Dealing with multiple hypotheses
8: Applying our simulation approach beyond null hypothesis testing