Explores scientific misconduct, p-value optimization, and spotting issues with conclusions using real-world examples.
Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
Explores portfolio rate of return, valuation, risk characterization, and historical performance, emphasizing diversification benefits and mean-variance analysis.
Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.
