Here we present R scripts and Electronic Supplementary Materials associated with the Oxford University Press book, in the Oxford Biology Primers series, Power Analysis: An Introduction For the Life Sciences by Nick Colegrave and Graeme D Ruxton.
Throughout the book we refer to various numbered R scripts. Follow the link below to reach our GitHub page, where you can download all of the R scripts as a single ZIP folder by clicking on the green download ‘Code’ button.
Bulk download of all R scripts available at: GitHub – colegrave/Powerbook: This contains the R scripts used on the book “Power Analysis: an introduction for life Sciences”
There are also materials that we thought would be useful to some readers that we could not squeeze into the book. These are referred to throughout the book as Electronic Supplementary Materials (ESM). These take the form of Word files . You can select a particular one from the list below, where they are arranged by the book chapter that they link most strongly to.
ESM by chapter
Introduction
Chapter 1: What is statistical power?
Chapter 2: Why low power is undesirable
No associated material
Chapter 3: Improving the power of an experiment
Chapter 4: How to quantify power by simulation
Chapter 5: Simple factorial designs
Chapter 6: Extensions to other designs
No associated material
Chapter 7: Dealing with multiple hypotheses
No associated material
Chapter 8: Applying our simulation approach beyond null hypothesis testing: parameter estimation, Bayesian, and model-selection contexts
No associated material
Written primarily for mid-to-upper level undergraduates, Power Analysis: An Introduction for the Life Sciences offers a clear, conceptual understanding of the factors that influence statistical power, as well as guidance on improving and presenting the outcomes of power analyses to justify experimental design decisions.
Power Analysis 