Continuous data is everywhere in healthcare. From physiological measures in patients such as systolic blood pressure or pulmonary function tests, through to population measures like life expectancy or disease incidence, the analysis of continuous outcome measures is common and important. The chapter shows the reader how to conduct one/two sample t-tests, ANOVA, introduces the concepts of pairwise testing vs multiple comparisons, as well as non-parametric tests (Mann-Whitney U/Wilcoxon rank-sum test). Throughout this chapter, data and effect sizes are always visualised alongside the statistical tests, to emphasize the importance of understanding your data and its distribution over just conducting a test and running with the p-value.