Jingyi Jessica Li | Statistical Hypothesis Testing versus Machine Learning Binary Classification
Jingyi Jessica Li (UCLA) discusses her paper "Statistical Hypothesis Testing versus Machine Learning Binary Classification". Jingyi noticed several high-impact cancer research papers using multiple hypothesis testing for binary classification problems. Concerned that these papers had no guarantee on their claimed false discovery rates, Jingyi wrote a perspective article about clarifying hypothesis testing and binary classification to scientists.
0:00 – Intro 1:50 – Motivation for Jingyi's article 3:22 – Jingyi's four concepts under hypothesis testing and binary classification 8:15 – Restatement of concepts 12:25 – Emulating methods from other publications 13:10 – Classification vs hypothesis test: features vs instances 21:55 - Single vs multiple instances 23:55 - Correlations vs causation 24:30 - Jingyi’s Second and Third Guidelines 30:35 - Jingyi’s Fourth Guideline 36:15 - Jingyi’s Fifth Guideline 39:15 – Logistic regression: An inference method & a classification method 42:15 – Utility for students 44:25 – Navigating the multiple comparisons problem (again!) 51:25 – Right side, show bio-arxiv paper