Diggle P.J., Chetwynd A.G. Statistics and Scientific Method: An Introduction for Students and Researchers

Oxford: Oxford University Press, 2011. — 189 p. — ISBN: 978-0-19-954318-2.Most introductory statistics text-books are written either in a highly mathematical style for an intended readership of mathematics undergraduate students, or in a recipe-book style for an intended audience of non-mathematically inclined undergraduate or postgraduate students, typically in a single discipline; hence, "statistics for biologists", "statistics for psychologists", and so on.
An antidote to technique-oriented service courses, Statistics and Scientific Method is different. It studiously avoids the recipe-book style and keeps algebraic details of specific statistical methods to the minimum extent necessary to understand the underlying concepts. Instead, the text aims to give the reader a clear understanding of how core statistical ideas of experimental design, modeling and data analysis are integral to the scientific method.
Aimed primarily at beginning postgraduate students across a range of scientific disciplines (albeit with a bias towards the biological, environmental and health sciences), it therefore assumes some maturity of understanding of scientific method, but does not require any prior knowledge of statistics, or any mathematical knowledge beyond basic algebra and a willingness to come to terms with mathematical notation.
Any statistical analysis of a realistically sized data-set requires the use of specially written computer software. An Appendix introduces the reader to our open-source software of choice, R, whilst the book's web-page includes downloadable data and R code that enables the reader to reproduce all of the analyses in the book and, with easy modifications, to adapt the code to analyze their own data if they wish. However, the book is not intended to be a textbook on statistical computing, and all of the material in the book can be understood without using either R or any other computer software.Objectives
Statistics as part of the scientific method
What is in this book, and how should you use it?
Overview: investigating Newton's law
Newton's laws of motion
Defining the question
Designing the experiment
Exploring the data
Modeling the data
Notational conventions
Making inferences from data
What have we learnt so far?
Uncertainty: variation, probability and inference
Variation
Probability
Statistical inference
The likelihood function: a principled approach to statistical inference
Further reading
Exploratory data analysis: gene expression microarrays
Gene expression microarrays
Displaying single batches of data
Comparing multiple batches of data
Displaying relationships between variables
Customized plots for special data types
Graphical design
Numerical summaries
Experimental design: agricultural field experiments and clinical trials
Agricultural field experiments
Randomization
Blocking
Factorial experiments
Clinical trials
Statistical significance and statistical power
Observational studies
Simple comparative experiments: comparing drug treatments for chronic asthmatics
Drug treatments for asthma
Comparing two treatments: parallel group and paired designs
Analysing data from a simple comparative trial
Crossover designs
Comparing more than two treatments
Statistical modeling: the effect of trace pollutants on plant growth
Pollution and plant growth
Scientific laws
Turning a scientific theory into a statistical model: mechanistic and empirical models
The simple linear model
Fitting the simple linear model
Extending the simple linear model
Checking assumptions
An exponential growth model
Non-linear models
Generalized linear models
The statistical modeling cycle: formulate, fit, check, reformulate
Survival analysis: living with kidney failure
Kidney failure
Estimating a survival curve
How long do you expect to live?
Regression analysis for survival data: proportional hazards
Analysis of the kidney failure data
Discussion and further reading
Time series analysis: predicting fluctuations in daily maximum temperatures
Weather forecasting
Why do time series data need special treatment?
Trend and seasonal variation
Autocorrelation: what is it and why does it matter?
Prediction
Discussion and further reading
Spatial statistics: monitoring air pollution
Air pollution
Spatial variation
Exploring spatial variation: the spatial correlogram
Exploring spatial correlation: the variogram
A case-study in spatial prediction: mapping lead pollution in Galicia
Further reading
Appendix: The R computing environment
Background material
Installing R
An example of an R session