Objectives: This course aims to introduce postgraduate students to the fundamental concepts of biostatistics necessary for planning, conducting, analysing, and writing a scientific study. The course is based on the R programming language and environment, enabling students to grasp biostatistical concepts in a practical context using a widely adopted software. Additionally, the course provides students with the opportunity to develop an analysis plan that can be applied directly to their own dissertation or thesis. Justification: A solid understanding of biostatistical principles is essential for every stage of research project development. However, many postgraduate students only encounter these concepts when analysing their study results, missing the opportunity to identify and address methodological weaknesses early on. Regardless of the study design, researchers should be familiar with sample size determination, data presentation methods, and the principles of qualitative and quantitative data analysis. These skills are crucial for ensuring the scientific rigour and reliability of research findings. Course Content: Week 1: Study design, statistical power, and sample size calculation. Week 2: Graphical interpretation and design. Week 3: Qualitative variables: hypothesis testing, types of error, Chi-square test, and statistical power for qualitative variables. Week 4: Quantitative variables: descriptive statistics (mean, median, variance, standard deviation, standard error vs. standard deviation, confidence intervals), normality testing, t-tests, ANOVA, correlation analysis, and statistical power for quantitative variables.