Biomeasurement by Hawkins, Dawn;

A student's guide to biological statistics

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Product details:

Edition number 2
Publisher OUP Oxford
Date of Publication 19 March 2009

ISBN 9780199219995
Binding Paperback
No. of pages368 pages
Size 246x189x21 mm
Weight 697 g
Language English
Illustrations 100 Black and White illustrations

Short description:

Biomeasurement offers a refreshing, student-focused introduction to the use of statistics in the study of the biosciences. Emphasising why statistical techniques are essential tools for bioscientists, the book removes the stigma attached to statistics by giving students the confidence to use key techniques for themselves.

Long description:

Statistical analysis allows us to attach meaning to data which we have collected; it helps us to understand what 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 the aspect of study which most students fear more than any other.

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 removes the stigma attached to statistics by giving students the confidence to use and further explore the key techniques for themselves.

The book starts by placing the role of data analysis in the context of wider scientific method, and introduces the student to the key terms and concepts which are common to all statistical tools. It 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. The book 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, whose teaching has won praise from the UK's Quality and Assurance Agency for Higher Education, Biomeasurement is sure to engage even the most wary of students, demonstrating the power and importance of statistics throughout the study of bioscience.

Online Resource Centre
The Online Resource Centre to accompany Biomeasurement features

For lecturers:
BL Figures from the book in electronic format, ready to download.

For students:
BL Data set, for use in a variety of statistical packages, so that students can practise carrying out statistical analysis.
BL Literature link articles: full-text versions of the Literature Link articles cited in the text.
BL Interactive calculation sheets to help students carry out key statistical tests quickly and easily, without needing other software.

This is a very user-friendly introduction to statistical methods for first-year undergraduate biology students.

Chapter 1: Why am I reading this book?
My lecturer is a sadist!
Doing science: the big picture
The process in practice
Essential skills for doing science
Types of data analysis
Chapter 2: Getting to grips with the basics
Populations and samples
Variation and variables
Understanding data
Demystifying formulae
Chapter 3: Describing a single sample
The single sample
Descriptive statistics
Frequency distributions
Pies, boxes, and errors
Example data: ranger patrol tusk records
Worked example: using SPSS
Chapter 4: Inferring and estimating
Overview of inferential statistics
Inferring through estimation
Exampledata: ground squirrels
Worked example: using SPSS
Chapter 5: Overview of hypothesis testing
Four steps of (statistical) hypothesis testing
Error and power
Parametric and nonparametric
One-and two-tailed tests
Chapter 6: Tests on frequencies
Introduction to chi-square tests
Example data
One-way classification chi-square test
Two-way classification chi-square test
Chapter 7: Tests of difference: two unrelated samples
Introduction to the t-and Mann-Whitney U tests
Example data: dem bones
t-Test
Mann-Whitney U test
Chapter 8: Tests of difference: two related samples
Introduction to paired t- and Wilcoxon signed- rank tests
Example data: big horn ewes
Paired t-test
Wilcoxon signed-rank test
Chapter 9: Tests of difference: more than two samples
Introduction to one-way and Kruskal-Wallis Anov atests
Example data: nitrogen levels in reeds
One-way Anova test
Two-way Anova test
Kruskal-Wallis test
Model I and model II Anova
Chapter 10: Tests of relationship: regression
Introduction
Example data: species richness
Regression test
Logistic regression
Multiple regression
Model I and model II regression
Chapter 11: Tests of relationship: correlation
Introduction to the Pearson and Spearman correlation tests
Example data: eyeballs
Pearson correlation test
Spearman correlation test
Comparison of correlation and regression
Chapter 12: Introducing th General Linear Model
Introduction to General Linear Model
Example data: watered willow
Testing using the General Linear Model
Interaction
Random factors and mixed models
Types of sums of squares
Getting the most out of GLM: Multiple models and model choice
The general and generalized linear models compared
Chapter 13: Choosing the right test and graph
Introduction to choosing
Which test?
Which graph?
Worked examples: graphs using SPSS
How to report your results