Epilogue

This chapter serves as an epilogue, exploring the vast universe of statistics that lies beyond the foundational concepts covered in the book. Highlighting the limitations of introductory courses, it emphasises that mastering basic tools such as chi-square tests, t-tests, regression, and ANOVA is merely the starting point for real-world data analysis. The chapter provides a glimpse into advanced statistical methods and models that extend the scope of traditional tools. Topics such as logistic regression, survival analysis, and robust statistics are introduced, offering insight into their practical applications and advantages. Moreover, the chapter underscores the importance of alternative inferential frameworks, like Bayesian approaches and bootstrapping, to modernise the data analysis toolkit and bridge the gap between theoretical assumptions and real-world messiness.

Additionally, the chapter reflects on the pedagogy of teaching statistics, advocating for a fundamentals-first approach. This philosophy, while initially demanding, ensures extensibility, equipping learners with the skills to seamlessly transition from basic analyses to more advanced models used in professional practice. The epilogue also acknowledges the evolving landscape of statistics, encouraging readers to remain adaptable and inquisitive as they advance in their data analysis journey. By grounding readers in core principles and exposing them to the breadth of available methodologies, the chapter inspires a mindset of continual learning and critical thinking essential for tackling complex, real-world datasets.