This lecture provides a focused and practical introduction to statistical methods tailored for postgraduate students in computer science. Emphasizing real-world applications, it walks participants through the statistical tools required to analyze, validate, and communicate research findings in a thesis setting. Using R and R Markdown, the session is anchored in a simulated case study comparing the performance of MergeSort and QuickSort. Students will explore descriptive statistics, visualization techniques (histograms, boxplots, scatterplots), normality testing, confidence intervals, hypothesis testing (t-tests, ANOVA, chi-squared), correlation, and non-parametric alternatives. By the end of the session, students will understand how to apply statistics to their own research projects, justify their conclusions with evidence, and produce reproducible analytical reports in R.

Contains 2 clustering techniques, hierarchical clustering and non-hierarchical clustering with data on the percentage of employment in the European continent in 1979.