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.

#datascience #science #statistics

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