Topic: ANOVA

Conventional approaches to hypothesis testing often hinge on linear models like ANOVA, subjective decision-making regarding factors and their interactions, and reliance on in-sample inference without thorough evaluation on hold-out data. These oversimplifications, subjective choices, and limited generalizability may cast doubt on the relevance and validity of scientific theories and conclusions. A transformative shift towards more resilient methodologies entails the incorporation of interpretability techniques, sophisticated machine learning models, and advanced out-of-sample model evaluation