This is the second book in a two-part series. The first - Introduction to Quantitative and Qualitative Research Methods in the Age of AI - gave you a broad foundation across the whole of the research process: forming a question, choosing a design, collecting data (quantitative and qualitative), analysing it, writing it up, and doing all of this responsibly and ethically in an era where AI tools are part of ordinary research practice. This book does something narrower and deeper. It picks up exactly where Book 1's statistics chapter (Chapter 10) had to stop, and spends twenty chapters doing what a single introductory chapter never can: working through the full logic of probability and sampling distributions that inferential statistics rests on; developing real fluency with measures of location, dispersion, and distribution shape; building confidence intervals, hypothesis tests, and power analyses from first principles; and then applying all of it to the full range of techniques a genuinely advanced undergraduate or beginning postgraduate project is likely to need - the t-family of tests, one-way and factorial ANOVA, non-parametric alternatives, correlation, simple and multiple regression
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