Evidence-based ecology, evolution and conservation require quantitative analyses of field data typically collected under imperfectly controlled conditions and across heterogeneous habitats. This module will develop generic skills in (1) the design of data collection protocols, particularly for field experiments and observational studies, and (2) the testing of hypotheses with appropriate statistical models. Although basic statistical awareness will be assumed to undergraduate level, the first third of the module will review core principles of experimental design and analysis that underpin all quantitative methods using the freely distributed environment R (www.r-project.org). Thereafter, the module will develop and apply statistical models for data types commonly encountered in fieldwork using the generalised linear model framework. The importance of scripting for transparent data management will be of central importance. Life science questions commonly seek to explain response variables in terms of predictor variables that co-vary with each other or with nuisance variables or cannot be measured in balanced designs. Techniques to resolve these issues will be introduced in a practical approach designed to pre-empt common issues of later independent research projects. The final part of the module will treat these issues in multifactorial and multivariate analysis.