In this unit, you will develop your capacity to design, conduct, and critically evaluate statistical analyses in research and public contexts, using examples drawn from social media, news reporting, government publications, and research in biology, health, ecology, and environmental science. You will design studies and statistical modelling frameworks to test hypotheses about environmental, biological, and human systems, extending your foundational knowledge of ANOVA and linear regression to general linear models (LMs), generalised linear models (GLMs), and multivariate techniques. You will examine the theoretical basis of likelihood-based inference and the frequentist and Bayesian paradigms and justify your choice of model with reference to its assumptions and limitations. Using R and RStudio, you will develop documented, reproducible analytical workflows to independently analyse and interpret complex datasets. You will critique published and public data visualisations and design them yourself to communicate complex findings to specialist and non-specialist audiences, and you will evaluate the validity and robustness of study designs and statistical conclusions in light of current debates about research practice and reproducibility.
Before starting this unit, you should have prior knowledge of foundational statistical concepts (e.g. mean, standard error, ANOVA, simple linear regression), study design principles (e.g. randomisation, replication, independence), and experience with R, RStudio, or other statistical software.
